"How do humans come to know so much about the world from so little data? Even young children can infer the meanings of words, the hidden properties of objects, or the existence of causal relations from just one or a few relevant observations — far outstripping the capabilities of conventional learning machines. How do they do it — and how can we bring machines closer to these human-like learning abilities? I will argue that people’s everyday inductive leaps can be understood in terms of (approximations to) probabilistic inference over generative models of the world."
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