Building machines that learn and think like people
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1604.00289.pdf
Size
3.58 MB
Format
Adobe PDF
Checksum (MD5)
6a31c28f6b3f11e4a08abe1460b1a479
Author(s)
Lake, Brenden M.
Ullman, Tomer David
Tenenbaum, Joshua B
Gershman, Samuel J
Date Issued
November 2016
Journal
Behavioral and Brain Sciences
Publisher
Cambridge University Press
Citation
Lake, Brenden M. et al. “Building Machines That Learn and Think Like People.” Behavioral and Brain Sciences 40 (November 2016): e253 © 2016 Cambridge University Press
Version
Original manuscript
Abstract
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.
MIT Department
Center for Brains, Minds, and Machines
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1017/S0140525X16001837
