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Generally Intelligent

En podcast av: Kanjun Qiu

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

Our American listeners can also listen to Generally Intelligent at our American site

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

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

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