Building Machines That Learn and Think Like People (1604.00289v3)

Abstract: Recent progress in AI has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Cognitive Ingredients for Human-Like AI

The paper "Building Machines That Learn and Think Like People" by Lake et al. investigates the limitations of current AI systems that primarily rely on deep learning while proposing additional cognitive components essential for human-like learning and thinking. Deep learning has achieved impressive success in pattern recognition tasks such as object recognition and speech recognition, sometimes surpassing human performance. However, these systems fall short in replicating the efficiency and richness of human learning, particularly when it comes to rapid generalization from sparse data.

Key Concepts: Mortar for Human-Like Learning

The authors argue that achieving human-level AI necessitates a shift beyond pattern recognition to embody deeper cognitive processes, merging recent advances in neural networks with insights from cognitive science. Here are the core ingredients that form the foundation of their proposal:

1. Causal Model Building: True human-like AI should generate causal models that enable explanation and understanding rather than merely classifying patterns. This approach reflects the human capacity to infer underlying structure and relationships, akin to the scientist's task of organizing data into coherent theories.
2. Intuitive Physics and Psychology: Leveraging intuitive theories of physical and psychological domains facilitates the grounding of what is learned. Intuitive physics allows for prediction and understanding of object interactions and physical laws, while intuitive psychology supports reasoning about goals, intentions, and mental states of agents.
3. Compositionality: The ability to compose complex concepts from simpler elements is fundamental to human thought. It provides a structured, productive way to generate new ideas and understand novel situations. Compositionality fosters the transfer and reuse of knowledge across different contexts, accelerating learning.
4. Learning-to-Learn: Human learners exhibit exceptional capability to learn from limited examples, heavily predicated on leveraging prior experience. This meta-learning enhances the efficiency of acquiring new tasks by adapting existing knowledge structures quickly and flexibly.
5. Combination of Model-Based and Model-Free Reinforcement Learning: Fast-thinking systems should integrate the precision of model-based decision-making with the speed of model-free approaches. Combining these methodologies offers a means to optimize behavior efficiently and adaptively.

Case Studies: Character Recognition and Atari Gaming

To illustrate the limitations of existing models and the effectiveness of their proposed ingredients, the authors present challenges using handwritten character recognition and an Atari game, Frostbite. These tasks highlight the profound differences in learning efficiency and generalization capabilities between humans and state-of-the-art AI systems. For instance, humans need only limited exposures to novel stimuli to form robust generalizations, whereas AI models typically require extensive datasets and training time. The paper emphasizes how incorporating causal reasoning, compositional and learning-to-learn capacities could narrow this gap significantly.

Future Directions and Implications

The implications of this research are vast, suggesting pathways toward AI systems that better emulate the flexibility and adaptability of human cognition. In practical terms, this could lead to significant advances in domains such as autonomous navigation, creative design, and human-computer interaction.

Continued exploration should focus on the integration of these cognitive ingredients with deep learning frameworks. This could involve developing neural networks that learn generative causal models or more sophisticated hierarchical representations aligned with human common-sense reasoning. The authors propose differentiable programming, which combines the benefits of AI and cognitive science perspectives, as a promising frontier.

Ultimately, the quest for AI with human-like cognitive abilities not only enriches our understanding of intelligence but opens up new avenues for both practical applications and theoretical insights. This research invites a convergence of disciplines, where cognitive science enriches AI, and in turn, AI offers tools for testing theories of cognition.