As Gopnik wrote about in her earlier book, The Scientist in the Crib, from the time they are babies, children learn by testing their theories of the world. And there’s evidence from the work in Gopnik’s lab that young children are quite good at calculating statistical likelihood and updating their beliefs like any good Bayesian scientist. They are driven by curiosity to develop a causal theory of the world.
Until recently, both curiosity and causality were curiously left out of the standard machine learning toolkit. In this sense, Gopnik has become an influential person in artifical intelligence (AI) circles for being an early proponent of early human development as a source of models for machine intelligence. She wrote a chapter for John Brockman’s recent book Possible Minds on ”AIs Versus Four-Year-Olds: Looking at what children do may give programmers useful hints about directions for computer learning,” joining a virtual who’s-who of AI luminaries.
This is only a snippet of a Productivity article written by Anthony Wing Kosner>
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