The Consciousness Prior (1709.08568v2)

Abstract: A new prior is proposed for learning representations of high-level concepts of the kind we manipulate with language. This prior can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by cognitive neuroscience theories of consciousness, seen as a bottleneck through which just a few elements, after having been selected by attention from a broader pool, are then broadcast and condition further processing, both in perception and decision-making. The set of recently selected elements one becomes aware of is seen as forming a low-dimensional conscious state. This conscious state is combining the few concepts constituting a conscious thought, i.e., what one is immediately conscious of at a particular moment. We claim that this architectural and information-processing constraint corresponds to assumptions about the joint distribution between high-level concepts. To the extent that these assumptions are generally true (and the form of natural language seems consistent with them), they can form a useful prior for representation learning. A low-dimensional thought or conscious state is analogous to a sentence: it involves only a few variables and yet can make a statement with very high probability of being true. This is consistent with a joint distribution (over high-level concepts) which has the form of a sparse factor graph, i.e., where the dependencies captured by each factor of the factor graph involve only very few variables while creating a strong dip in the overall energy function. The consciousness prior also makes it natural to map conscious states to natural language utterances or to express classical AI knowledge in a form similar to facts and rules, albeit capturing uncertainty as well as efficient search mechanisms implemented by attention mechanisms.

• The paper introduces the consciousness prior, a framework that uses a sparse cognitive bottleneck to enhance high-level representation learning.
• It applies insights from Global Workspace Theory and System 2 thinking to model abstract, declarative knowledge in neural networks.
• The approach suggests that focusing on limited, predictive abstractions can improve generalization and bridge deep learning with symbolic AI.

The Consciousness Prior: Implications for Representation Learning and AI

In "The Consciousness Prior," Yoshua Bengio explores the intriguing intersection between cognitive neuroscience, particularly theories of consciousness, and machine learning. Bengio posits a novel prior aimed at learning high-level representations of concepts akin to those manipulated in natural language. This consciousness prior draws inspiration from cognitive models like the Global Workspace Theory, suggesting that consciousness serves as a bottleneck that selects and broadcasts a limited number of elements that condition subsequent processing. This selection and broadcast mechanism utilizes an attention process similar to those used in contemporary neural network architectures.

Theoretical Foundations

The consciousness prior hypothesis proposes that high-level cognition—analogous to what Kahneman describes as "System 2" processes—can be modeled in ways that reflect aspects of conscious thought. This high-level cognition is characterized by the manipulation of abstract, declarative knowledge, expressed in a sparse, connected graph of concepts. Each node in this graph represents a high-level variable or concept, and the edges denote strong dependencies between them.

Bengio argues for a sparse factor graph in this high-dimensional concept space. Here, the sparseness corresponds to the idea that conscious thoughts usually involve only a small number of variables or concepts—akin to how sentences are structured in natural language. The interactions are limited but meaningful, capturing essential dependencies that show strong implications in predicting states or actions. This formulation contrasts with a dense graph, allowing for more precise and efficient information processing.

Implications for Machine Learning

Bengio emphasizes that the proposed consciousness prior can aid machine learning models in disentangling abstract factors, thereby leading to representations that reflect the underlying structure of the world more accurately. By focusing attention on a small subset of relevant variables drawn from a potentially vast unconscious representation (akin to a neural memory), a learning agent can make more robust predictions and decisions. This aligns well with recent advances in attention mechanisms and the development of architectures like Transformers, which leverage attention to handle variable interactions effectively.

Additionally, the paper explores how integrating the consciousness prior could bridge the divide between contemporary deep learning approaches and classical symbolic AI. While deep learning has in many cases excelled at perceptual tasks, adding a consciousness-inspired layer of abstraction could enhance its applicability to reasoning and planning tasks traditionally dominated by symbolic methods.

Training and Evaluation Considerations

From an experimental standpoint, the paper suggests starting with simple environments to validate the theory. Such environments should allow for rapid iteration and insightful evaluation of different attention and representation-learning mechanisms. This strategy aims to isolate the consciousness prior's effects on discovering high-level abstractions without confounding factors like linguistic input or supervised objectives initially.

Moreover, leveraging reinforcement learning environments with an unsupervised or intrinsic reward structure might highlight the consciousness prior's capacity to discover and represent predictive abstractions effectively. These environments could facilitate testing the hypothesis that focusing on sparse but high-predictive-power abstractions can lead to better generalization and sample efficiency—key metrics of interest in machine learning.

Conclusion

"The Consciousness Prior" paper does not just introduce a theoretical construct but also paves the way for new methodologies in AI that align more closely with human cognitive processes. While implementing and testing these ideas in real-world systems poses formidable challenges, the potential benefits—stronger generalization, improved interpretability, and deeper cognitive representational capabilities—make investigating this theory a worthy pursuit. Future research may well explore extensions of these ideas, offering a possibly richer integration of machine learning with cognitive and neural sciences to advance artificial intelligence markedly.