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Podcast

Conversations with deep learning researchers about behind-the-scenes ideas, opinions, and intuitions that they normally wouldn't have the space to share in a typical paper or talk.

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Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas
PodcastSeptember 19, 2024
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Ari Morcos, DatologyAI: On leveraging data to democratize model training
PodcastJuly 11, 2024
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Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models
PodcastMay 9, 2024
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Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI
PodcastMarch 12, 2024
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Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference
PodcastFebruary 8, 2024
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Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize
PodcastJune 29, 2023
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Bill Thompson, UC Berkeley: On how cultural evolution shapes knowledge acquisition
PodcastMarch 29, 2023
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Ben Eysenbach, CMU: On designing simpler and more principled RL algorithms
PodcastMarch 22, 2023
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Jim Fan, NVIDIA: On foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant
PodcastMarch 8, 2023
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Sergey Levine, UC Berkeley: On the bottlenecks to generalization, why simulation is doomed to succeed, and picking good research problems
PodcastMarch 1, 2023
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Noam Brown, FAIR: On achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time
PodcastFebruary 9, 2023
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Sugandha Sharma, MIT: On biologically inspired neural architectures, how memories can be implemented, and control theory
PodcastJanuary 11, 2023
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Nicklas Hansen, UCSD: On long-horizon planning and why algorithms don't drive research progress
PodcastDecember 16, 2022
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Jack Parker-Holder, DeepMind: On open-endedness, evolving agents and environments, online adaptation, and offline learning
PodcastDecember 6, 2022
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Celeste Kidd, UC Berkeley: On attention and curiosity, how we form beliefs, and where certainty comes from
PodcastNovember 22, 2022
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Archit Sharma, Stanford: On unsupervised and autonomous reinforcement learning
PodcastNovember 17, 2022
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Chelsea Finn, Stanford: On the biggest bottlenecks in robotics and reinforcement learning
PodcastNovember 3, 2022
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Hattie Zhou, Mila: On supermasks, iterative learning, and fortuitous forgetting
PodcastOctober 14, 2022
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Minqi Jiang, UCL: On environment and curriculum design for general RL agents
PodcastJuly 19, 2022
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Oleh Rybkin, UPenn: On exploration and planning with world models
PodcastJuly 11, 2022
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Andrew Lampinen, DeepMind: On symbolic behavior, mental time travel, and insights from psychology
PodcastFebruary 28, 2022
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Yilun Du, MIT: On energy-based models, implicit functions, and modularity
PodcastDecember 21, 2021
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Martin Arjovsky, INRIA: On benchmarks for robustness and geometric information theory
PodcastOctober 14, 2021
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Yash Sharma, MPI-IS: On generalizability, causality, and disentanglement
PodcastSeptember 24, 2021
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Jacob Steinhardt, UC Berkeley: On machine learning safety, alignment and measurement
PodcastJune 17, 2021
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Jonathan Frankle, MIT: On the lottery ticket hypothesis and the science of deep learning
PodcastJune 1, 2021
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Vincent Sitzmann, MIT: On neural scene representations for computer vision and more general AI
PodcastMay 19, 2021
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Dylan Hadfield-Menell, UC Berkeley/MIT: On the value alignment problem in AI
PodcastMay 11, 2021
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Drew Linsley, Brown: On inductive biases for vision and generalization
PodcastApril 1, 2021
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Giancarlo Kerg, Mila: On approaching deep learning from mathematical foundations
PodcastMarch 26, 2021
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Yujia Huang, Caltech: On neuro-inspired generative models
PodcastMarch 18, 2021
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Julian Chibane, MPI-INF: On 3D reconstruction using implicit functions
PodcastMarch 5, 2021
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Katja Schwarz, MPI-IS: On GANs, implicit functions, and 3D scene understanding
PodcastFebruary 23, 2021
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Joel Lehman, OpenAI: On evolving intelligence, open-endedness, and reinforcement learning
PodcastFebruary 16, 2021
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Cinjon Resnick, NYU: On activity and scene understanding
PodcastJanuary 31, 2021
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Sarah Jane Hong, Latent Space: On neural rendering & research process
PodcastJanuary 6, 2021
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Kelvin Guu, Google AI: On language models & overlooked problems
PodcastDecember 16, 2020
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Generally Intelligent #0: A podcast for deep learning researchers
PodcastDecember 6, 2020
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