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Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik - #548

2021-12-27 · 37 min · episode 548 · 13 entities

episode page audio

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    Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik - #548
  • → hosted by Sam Charrington person
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    Feed author/publisher: Sam Charrington
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  • → hosted by TWIML company
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    Feed author/publisher: TWIML

Entities found in this episode

concepts 5

  • mentioned Science concept
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    Feed category: Science
  • mentioned Tech News concept
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    Feed category: Tech News
  • discusses Science concept
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    evidence rules-v4
    Feed category: Science
  • discusses News concept
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    evidence rules-v4
    Feed category: News
  • discusses Tech News concept
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    evidence rules-v4
    Feed category: Tech News

companys 4

  • mentioned TWIML company
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    Feed author/publisher: TWIML
  • mentioned Technology company
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    Feed category: Technology
  • discusses Technology company
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    Feed category: Technology
  • hosted by TWIML company
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    Feed author/publisher: TWIML

persons 3

  • mentioned Alison Gopnik person
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    Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik - #548
  • mentioned Sam Charrington person
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    evidence rules-v4
    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
    0.55
    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
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