Generally Intelligent

Generally Intelligent

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Generally Intelligent

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

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

July 11, 2024

01:34:19

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

May 9, 2024

01:01:55

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

March 12, 2024

01:55:45

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

Aug. 9, 2023

01:20:29

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

June 22, 2023

01:01:54

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

March 29, 2023

01:15:24

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

March 23, 2023

01:45:56

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

March 9, 2023

01:26:45

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

March 1, 2023

01:34:49

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

Feb. 9, 2023

01:44:54

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