
Building machines that learn and think like people - tonybeltramelli
http://arxiv.org/abs/1604.00289
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laudney
"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."

