Generally Intelligent

Un pódcast de Kanjun Qiu

prueba podimo gratis durante 60 días!

Miles de audiolibros y podcasts exclusivos, haz clic aquí para probar

37 Episodo

Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas
Publicado: 18/9/2024
Episode 36: Ari Morcos, DatologyAI: On leveraging data to democratize model training
Publicado: 11/7/2024
Episode 35: Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models
Publicado: 9/5/2024
Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI
Publicado: 12/3/2024
Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference
Publicado: 9/8/2023
Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize
Publicado: 22/6/2023
Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition
Publicado: 29/3/2023
Episode 30: Ben Eysenbach, CMU, on designing simpler and more principled RL algorithms
Publicado: 23/3/2023
Episode 29: Jim Fan, NVIDIA, on foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant
Publicado: 9/3/2023
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
Publicado: 1/3/2023
Episode 27: Noam Brown, FAIR, on achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time
Publicado: 9/2/2023
Episode 26: Sugandha Sharma, MIT, on biologically inspired neural architectures, how memories can be implemented, and control theory
Publicado: 17/1/2023
Episode 25: Nicklas Hansen, UCSD, on long-horizon planning and why algorithms don't drive research progress
Publicado: 16/12/2022
Episode 24: Jack Parker-Holder, DeepMind, on open-endedness, evolving agents and environments, online adaptation, and offline learning
Publicado: 6/12/2022
Episode 23: Celeste Kidd, UC Berkeley, on attention and curiosity, how we form beliefs, and where certainty comes from
Publicado: 22/11/2022
Episode 22: Archit Sharma, Stanford, on unsupervised and autonomous reinforcement learning
Publicado: 17/11/2022
Episode 21: Chelsea Finn, Stanford, on the biggest bottlenecks in robotics and reinforcement learning
Publicado: 3/11/2022
Episode 20: Hattie Zhou, Mila, on supermasks, iterative learning, and fortuitous forgetting
Publicado: 14/10/2022
Episode 19: Minqi Jiang, UCL, on environment and curriculum design for general RL agents
Publicado: 19/7/2022
Episode 18: Oleh Rybkin, UPenn, on exploration and planning with world models
Publicado: 11/7/2022

1 / 2

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 Episodo

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