Much of this progress has come from recent advances in “deep learning,” characterized by learning large neural network-style models with multiple layers of representation (see Glossary in Table 1).

We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Īrtificial intelligence (AI) has been a story of booms and busts, yet by any traditional measure of success, the last few years have been marked by exceptional progress. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. 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. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. 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 that of humans in some respects. Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people.

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