Ideas

There Will Be a Scientific Theory of Deep Learning

Transforming AI agents into personal collaborators: Kanjun on the NVIDIA AI Podcast

Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas

Ari Morcos, DatologyAI: On leveraging data to democratize model training

Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models

Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI

Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference

Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize

Bill Thompson, UC Berkeley: On how cultural evolution shapes knowledge acquisition

Ben Eysenbach, CMU: On designing simpler and more principled RL algorithms

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

Sergey Levine, UC Berkeley: On the bottlenecks to generalization, why simulation is doomed to succeed, and picking good research problems

Noam Brown, FAIR: On achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time

Sugandha Sharma, MIT: On biologically inspired neural architectures, how memories can be implemented, and control theory

Nicklas Hansen, UCSD: On long-horizon planning and why algorithms don't drive research progress

Jack Parker-Holder, DeepMind: On open-endedness, evolving agents and environments, online adaptation, and offline learning

Celeste Kidd, UC Berkeley: On attention and curiosity, how we form beliefs, and where certainty comes from

Archit Sharma, Stanford: On unsupervised and autonomous reinforcement learning

Chelsea Finn, Stanford: On the biggest bottlenecks in robotics and reinforcement learning

Hattie Zhou, Mila: On supermasks, iterative learning, and fortuitous forgetting

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

Oleh Rybkin, UPenn: On exploration and planning with world models

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

Yilun Du, MIT: On energy-based models, implicit functions, and modularity

Martin Arjovsky, INRIA: On benchmarks for robustness and geometric information theory

Yash Sharma, MPI-IS: On generalizability, causality, and disentanglement

Jonathan Frankle, MIT: On the lottery ticket hypothesis and the science of deep learning

Jacob Steinhardt, UC Berkeley: On machine learning safety, alignment and measurement

Vincent Sitzmann, MIT: On neural scene representations for computer vision and more general AI

Dylan Hadfield-Menell, UC Berkeley/MIT: On the value alignment problem in AI

Drew Linsley, Brown: On inductive biases for vision and generalization

Giancarlo Kerg, Mila: On approaching deep learning from mathematical foundations

Yujia Huang, Caltech: On neuro-inspired generative models

Julian Chibane, MPI-INF: On 3D reconstruction using implicit functions

Katja Schwarz, MPI-IS: On GANs, implicit functions, and 3D scene understanding

Joel Lehman, OpenAI: On evolving intelligence, open-endedness, and reinforcement learning

Cinjon Resnick, NYU: On activity and scene understanding

Sarah Jane Hong, Latent Space: On neural rendering & research process

Kelvin Guu, Google AI: On language models & overlooked problems

Generally Intelligent #0: A podcast for deep learning researchers