The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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

Dec 27, 2021

In this engaging discussion, Alison Gopnik, a UC Berkeley professor known for her work in psychology and philosophy, delves into how children learn about the world through causal inference. She reveals how kids' exploration mirrors the scientific method, highlighting parallels between their learning and advancements in AI. Gopnik emphasizes the importance of understanding complex causal relationships and encourages using insights from children's learning to improve machine learning models and address social biases in AI design.

36:54

Creator website

Episode guests

Alison Gopnik

Highlights

AI Chapters

Episode notes

How Do We Know So Much About the World Around Us?

03:37

Is There a Predictive Approach to Predicting Children's Learning?

02:47

The Natural Machine Learning Cycle Is Like Childhood Adulthood

01:56

Intro

2min

From Philosophy to AI: Insights on Causal Learning

8min

Understanding Children's Learning: The Theory Theory and Its Alternatives

3min

Children as Active Learners: Causal Understanding and AI

21min

Causal Inference and Child Learning: Insights for AI Development

3min

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

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