Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik - #548
Today we close out the 2021 NeurIPS series joined by Alison Gopnik, a professor at UC Berkeley and an invited speaker at the Causal Inference & Machine Learning: Why now? Workshop. In our conversation with Alison, we explore the question, “how is it that we can know so much about the world around us from so little information?,” and how her background in psychology, philosophy, and epistemology has guided her along the path to finding this answer through the actions of children. We discuss the role of causality as a means to extract representations of the world and how the “theory theory” came about, and how it was demonstrated to have merit. We also explore the complexity of causal relationships that children are able to deal with and what that can tell us about our current ML models, how the training and inference stages of the ML lifecycle are akin to childhood and adulthood, and much more!
The complete show notes for this episode can be found at twimlai.com/go/548
Alison Gopnik on the different (and similar) ways robots and children learn
In episode eleven of The Robot Brains Podcast we are joined by Alison Gopnik, professor of psychology at UC Berkeley and author of the "Mind and Matter" science column for the Wall Street Journal. She has written numerous books about developmental psychology and researching the ways children learn. Her TED Talk: "What do babies think?" has been seen over 4.2 million times. During her conversation with our host, Pieter Abbeel, we discussed the similarities and differences between the way robots and human children learn. We also covered some the methods for testing what a child and a robot actually knows, the theory of the mind, and whether a robot can ever truly be curious? Host: Pieter Abbeel | Executive Producers: Ricardo Reyes & Henry Tobias Jones | Audio Production: Kieron Matthew Banerji | Title Music: Alejandro Del Pozo
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#153 — Possible Minds
Sam Harris introduces John Brockman's new anthology, "Possible Minds: 25 Ways of Looking at AI," in conversation with three of its authors: George Dyson, Alison Gopnik, and Stuart Russell.
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