Generally Intelligent

En podcast av Kanjun Qiu

37 Avsnitt

Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas
Publicerades: 2024-09-18
Episode 36: Ari Morcos, DatologyAI: On leveraging data to democratize model training
Publicerades: 2024-07-11
Episode 35: Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models
Publicerades: 2024-05-09
Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI
Publicerades: 2024-03-12
Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference
Publicerades: 2023-08-09
Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize
Publicerades: 2023-06-22
Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition
Publicerades: 2023-03-29
Episode 30: Ben Eysenbach, CMU, on designing simpler and more principled RL algorithms
Publicerades: 2023-03-23
Episode 29: Jim Fan, NVIDIA, on foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant
Publicerades: 2023-03-09
Episode 28: Sergey Levine, UC Berkeley, on the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems
Publicerades: 2023-03-01
Episode 27: Noam Brown, FAIR, on achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time
Publicerades: 2023-02-09
Episode 26: Sugandha Sharma, MIT, on biologically inspired neural architectures, how memories can be implemented, and control theory
Publicerades: 2023-01-17
Episode 25: Nicklas Hansen, UCSD, on long-horizon planning and why algorithms don't drive research progress
Publicerades: 2022-12-16
Episode 24: Jack Parker-Holder, DeepMind, on open-endedness, evolving agents and environments, online adaptation, and offline learning
Publicerades: 2022-12-06
Episode 23: Celeste Kidd, UC Berkeley, on attention and curiosity, how we form beliefs, and where certainty comes from
Publicerades: 2022-11-22
Episode 22: Archit Sharma, Stanford, on unsupervised and autonomous reinforcement learning
Publicerades: 2022-11-17
Episode 21: Chelsea Finn, Stanford, on the biggest bottlenecks in robotics and reinforcement learning
Publicerades: 2022-11-03
Episode 20: Hattie Zhou, Mila, on supermasks, iterative learning, and fortuitous forgetting
Publicerades: 2022-10-14
Episode 19: Minqi Jiang, UCL, on environment and curriculum design for general RL agents
Publicerades: 2022-07-19
Episode 18: Oleh Rybkin, UPenn, on exploration and planning with world models
Publicerades: 2022-07-11

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Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.

Visit the podcast's native language site

37 Avsnitt

Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas

Episode 36: Ari Morcos, DatologyAI: On leveraging data to democratize model training

Episode 35: Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models

Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI

Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference

Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize

Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition

Episode 30: Ben Eysenbach, CMU, on designing simpler and more principled RL algorithms

Episode 29: Jim Fan, NVIDIA, on foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant

Episode 28: Sergey Levine, UC Berkeley, on the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems

Episode 27: Noam Brown, FAIR, on achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time

Episode 26: Sugandha Sharma, MIT, on biologically inspired neural architectures, how memories can be implemented, and control theory

Episode 25: Nicklas Hansen, UCSD, on long-horizon planning and why algorithms don't drive research progress

Episode 24: Jack Parker-Holder, DeepMind, on open-endedness, evolving agents and environments, online adaptation, and offline learning

Episode 23: Celeste Kidd, UC Berkeley, on attention and curiosity, how we form beliefs, and where certainty comes from

Episode 22: Archit Sharma, Stanford, on unsupervised and autonomous reinforcement learning

Episode 21: Chelsea Finn, Stanford, on the biggest bottlenecks in robotics and reinforcement learning

Episode 20: Hattie Zhou, Mila, on supermasks, iterative learning, and fortuitous forgetting

Episode 19: Minqi Jiang, UCL, on environment and curriculum design for general RL agents

Episode 18: Oleh Rybkin, UPenn, on exploration and planning with world models