Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik - #548
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Children as Active Learners: Causal Understanding and AI
This chapter examines how children act as active learners, developing causal theories through exploration and experimentation, akin to Bayesian hypothesis testing. It highlights the parallels between children's learning processes and advancements in machine learning, especially in terms of causal inference and exploration-exploitation dynamics. The discussion also touches on the implications of children's reasoning for AI development, particularly in understanding social biases and enhancing algorithm design.
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