Podcast

LatestTopDiscussions

There will be a scientific theory of deep learning

1:33:22

Malleable software and human agency

1:32:07

From lawless spaces to true liberty: rethinking AI's role in society

1:38:37

Rylan Schaeffer, Stanford: Investigating emergent abilities of LLMs

1:02:51

Ari Morcos, DatologyAI: Leveraging data to democratize model training

1:34:19

Percy Liang, Stanford: How foundation models work

1:01:55

Seth Lazar, Australian National University: The political philosophy of AI

1:55:45

Tri Dao, Stanford: FlashAttention and efficient training

1:20:29

Jamie Simon, UC Berkeley: Theoretical principles for deep neural networks

1:01:54

Bill Thompson, UC Berkeley: How cultural evolution shapes knowledge acquisition

1:15:24

Ben Eysenbach, CMU: Designing simpler, more principled RL algorithms

1:45:56

Jim Fan, NVIDIA: Foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant

1:26:45

Sergey Levine, UC Berkeley: Bottlenecks to generalization in reinforcement learning

1:34:49

Noam Brown, FAIR: Building human-level AIs in poker and Diplomacy

1:44:54

Sugandha Sharma, MIT: Biologically inspired neural architectures

1:44:00

Nicklas Hansen, UCSD: Why algorithms don't drive research progress

1:49:18

Jack Parker-Holder, DeepMind: Open-endedness and evolving agents

1:56:43

Celeste Kidd, UC Berkeley: Attention, curiosity, and how we form beliefs

1:52:35

Archit Sharma, Stanford: Unsupervised and autonomous reinforcement learning

1:38:13

Chelsea Finn, Stanford: Biggest bottlenecks in robotics and reinforcement learning

Hattie Zhou, Mila: Supermasks, iterative learning, and fortuitous forgetting

1:47:28

Minqi Jiang, UCL: Environment and curriculum design for general RL agents

1:53:59

Oleh Rybkin, UPenn: Exploration and planning with world models

2:00:40

Andrew Lampinen, DeepMind: Symbolic behavior, mental time travel, and insights from psychology

1:59:07