Generally Intelligent

A podcast by Kanjun Qiu

37 Episodio

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

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

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37 Episodio

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