Reinforcement learning: Fast and slow - Matthew Botvinick
Matthew Botvinick’s work straddles the boundaries between cognitive psychology, computational and experimental neuroscience and artificial intelligence. In this talk Dr. Botvinick will review recent developments in deep reinforcement learning (RL), showing how deep RL can proceed rapidly, and also have interesting potential implications for our understanding of human learning and neural function.

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TypeScript in Express – TypeScript Tutorial

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David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning | Lex Fridman Podcast #86

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Free Energy Principle — Karl Friston

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Matthew Botvinick, DeepMind: Meta-learning in brains and machines

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The Prefrontal Cortex as a Meta-Reinforcement Learning System

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Policy Gradient Theorem Explained - Reinforcement Learning

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Stephen Meyer, John Lennox, and James Tour: Three Scientists on the Origins of Everything

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Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Economics of Generative AI

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From Neurons to Newtons: What the Brain Can Teach Us About Building Physics | Alexei Koulakov

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#49 - Meta-Gradients in RL - Dr. Tom Zahavy (DeepMind)

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Stanford CS547 HCI Seminar | Spring 2026 | Toward Ontological Multiplicity in AI and Computing

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A History of Reinforcement Learning - Prof. A.G. Barto

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Matt Botvinick - Holy Grail Questions at the Intersection of Neuroscience and AI

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