"Recent progress in artificial intelligence (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."