DeepMind's Richard Sutton - The Long-term of AI & Temporal-Difference Learning
Link to the slides: http://videolectures.net/site/normal_... DeepMind announced in July, 2017 that Prof. Richard Sutton would be leading DeepMind Alberta. Richard S. Sutton is a Canadian computer scientist. Currently he is professor of Computer Science and iCORE chair at the University of Alberta. Dr. Sutton is considered one of the founding fathers of modern computational reinforcement learning, having several significant contributions to the field, including temporal difference learning, policy gradient methods, the Dyna architecture. Recorded: July 2017

▶︎
Rich Sutton, The OaK Architecture: A Vision of SuperIntelligence from Experience - RLC 2025

▶︎
Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton

▶︎
Terence Tao: Nobody Understands Why AI Actually Works

▶︎
A systems neuroscience approach to building AGI - Demis Hassabis, Singularity Summit 2010

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

▶︎
KONTRA #27 Rymanowski, Bartosiak, Bosak: Co dalej z Ukrainą?

▶︎
NUS120 Distinguished Speaker Series | Professor Richard Sutton

▶︎
Richard Sutton’s new path for AI | Approximately Correct #AI Podcast

▶︎
Artificial Intelligence, the History and Future - with Chris Bishop

▶︎
Deepmind AlphaZero - Mastering Games Without Human Knowledge

▶︎
TURING AWARD WINNER Richard S. Sutton in Conversation with Cam Linke | No Authorities in Science

▶︎
Richard Sutton - Humanity Never Had Control in the First Place (Worthy Successor Series, Episode 2)

▶︎
prg.ai meetup: Tomáš Mikolov in a conversation with Rich Sutton

▶︎
From Deep Learning of Disentangled Representations to Higher-level Cognition

▶︎
Visualizing transformers and attention | Talk for TNG Big Tech Day '24

▶︎
Early days of reinforcement learning with Rich Sutton | Michael Littman and Lex Fridman

▶︎
Yann LeCun | Self-Supervised Learning, JEPA, World Models, and the future of AI

▶︎
I Talked with Rich Sutton

▶︎
Geoffrey Hinton: The Foundations of Deep Learning

▶︎
