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.

Modern AI and the state of interdisciplinary exchange with neuroscience - Greg Corrado
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Modern AI and the state of interdisciplinary exchange with neuroscience - Greg Corrado

Sam Ritter: Meta-Learning to Make Smart Inferences from Small Data
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Sam Ritter: Meta-Learning to Make Smart Inferences from Small Data

"A.I. and Our Economic Future," Professor Chad Jones
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"A.I. and Our Economic Future," Professor Chad Jones

The PROBLEM with Capitalism - Smarter Every Day 316
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The PROBLEM with Capitalism - Smarter Every Day 316

TypeScript in Express – TypeScript Tutorial
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TypeScript in Express – TypeScript Tutorial

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

Free Energy Principle — Karl Friston
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Free Energy Principle — Karl Friston

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

The Prefrontal Cortex as a Meta-Reinforcement Learning System
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The Prefrontal Cortex as a Meta-Reinforcement Learning System

Training Sand to Think: Artificial General Intelligence & Future of Physics
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Training Sand to Think: Artificial General Intelligence & Future of Physics

Policy Gradient Theorem Explained - Reinforcement Learning
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Policy Gradient Theorem Explained - Reinforcement Learning

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

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

James L. McClelland - Comparing Human and Artificial Intelligence
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James L. McClelland - Comparing Human and Artificial Intelligence

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

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

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

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

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

An introduction to Policy Gradient methods - Deep Reinforcement Learning
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An introduction to Policy Gradient methods - Deep Reinforcement Learning