# Podcast

## LatestTopDiscussions

### [There will be a scientific theory of deep learning](https://ideas.imbue.com/p/learning-mechanics)

1:33:22

### [Malleable software and human agency](https://ideas.imbue.com/p/geoffrey-litt)

1:32:07

### [From lawless spaces to true liberty: rethinking AI's role in society](https://ideas.imbue.com/p/matt-boulos)

1:38:37

### [Rylan Schaeffer, Stanford: Investigating emergent abilities of LLMs](https://ideas.imbue.com/p/episode-37-rylan-schaeffer-stanford-de4)

1:02:51

### [Ari Morcos, DatologyAI: Leveraging data to democratize model training](https://ideas.imbue.com/p/episode-36-ari-morcos-datologyai-e92)

1:34:19

### [Percy Liang, Stanford: How foundation models work](https://ideas.imbue.com/p/episode-35-percy-liang-stanford-on-68d)

1:01:55

### [Seth Lazar, Australian National University: The political philosophy of AI](https://ideas.imbue.com/p/episode-34-seth-lazar-australian-185)

1:55:45

### [Tri Dao, Stanford: FlashAttention and efficient training](https://ideas.imbue.com/p/episode-33-tri-dao-stanford-on-flashattention-4d3)

1:20:29

### [Jamie Simon, UC Berkeley: Theoretical principles for deep neural networks](https://ideas.imbue.com/p/episode-32-jamie-simon-uc-berkeley-6be)

1:01:54

### [Bill Thompson, UC Berkeley: How cultural evolution shapes knowledge acquisition](https://ideas.imbue.com/p/episode-31-bill-thompson-uc-berkeley-16a)

1:15:24

### [Ben Eysenbach, CMU: Designing simpler, more principled RL algorithms](https://ideas.imbue.com/p/episode-30-ben-eysenbach-cmu-on-designing-f15)

1:45:56

### [Jim Fan, NVIDIA: Foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant](https://ideas.imbue.com/p/episode-29-jim-fan-nvidia-on-foundation-7b2)

1:26:45

### [Sergey Levine, UC Berkeley: Bottlenecks to generalization in reinforcement learning](https://ideas.imbue.com/p/episode-28-sergey-levine-uc-berkeley-f5e)

1:34:49

### [Noam Brown, FAIR: Building human-level AIs in poker and Diplomacy](https://ideas.imbue.com/p/episode-27-noam-brown-fair-on-achieving-3e4)

1:44:54

### [Sugandha Sharma, MIT: Biologically inspired neural architectures](https://ideas.imbue.com/p/episode-26-sugandha-sharma-mit-on-24e)

1:44:00

### [Nicklas Hansen, UCSD: Why algorithms don't drive research progress](https://ideas.imbue.com/p/episode-25-nicklas-hansen-ucsd-on-cb0)

1:49:18

### [Jack Parker-Holder, DeepMind: Open-endedness and evolving agents](https://ideas.imbue.com/p/episode-24-jack-parker-holder-deepmind-e17)

1:56:43

### [Celeste Kidd, UC Berkeley: Attention, curiosity, and how we form beliefs](https://ideas.imbue.com/p/episode-23-celeste-kidd-uc-berkeley-9a0)

1:52:35

### [Archit Sharma, Stanford: Unsupervised and autonomous reinforcement learning](https://ideas.imbue.com/p/episode-22-archit-sharma-stanford-54a)

1:38:13

### [Chelsea Finn, Stanford: Biggest bottlenecks in robotics and reinforcement learning](https://ideas.imbue.com/p/episode-21-chelsea-finn-stanford-04a)

### [Hattie Zhou, Mila: Supermasks, iterative learning, and fortuitous forgetting](https://ideas.imbue.com/p/episode-20-hattie-zhou-mila-on-supermasks-566)

1:47:28

### [Minqi Jiang, UCL: Environment and curriculum design for general RL agents](https://ideas.imbue.com/p/episode-19-minqi-jiang-ucl-on-environment-5c3)

1:53:59

### [Oleh Rybkin, UPenn: Exploration and planning with world models](https://ideas.imbue.com/p/episode-18-oleh-rybkin-upenn-on-exploration-14a)

2:00:40

### [Andrew Lampinen, DeepMind: Symbolic behavior, mental time travel, and insights from psychology](https://ideas.imbue.com/p/episode-17-andrew-lampinen-deepmind-37b)

1:59:07