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Building machines that learn and think like people | Behavioral and Brain Sciences | Cambridge Core

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Recent progress in artificial intelligence 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 that of 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 (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 c

Building machines that learn and think like people | Behavioral and Brain Sciences | Cambridge Core Search Institution Login Search Hostname: page-component-5db58dd55d-mhzq2 Total loading time: 0 Render date: 2026-06-19T08:35:45.981Z Has data issue: false hasContentIssue false Home > Journals > Behavioral and Brain Sciences > Volume 40 > Building machines that learn and think like people English Français Behavioral and Brain Sciences Article contents Abstract Introduction Cognitive and neural inspiration in artificial intelligence Challenges for building more human-like machines Core ingredien

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