Generally Intelligent

37 Epizód

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

   Közzétéve: 2024. 09. 18.
2. Episode 36: Ari Morcos, DatologyAI: On leveraging data to democratize model training

   Közzétéve: 2024. 07. 11.
3. Episode 35: Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models

   Közzétéve: 2024. 05. 09.
4. Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI

   Közzétéve: 2024. 03. 12.
5. Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference

   Közzétéve: 2023. 08. 09.
6. Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize

   Közzétéve: 2023. 06. 22.
7. Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition

   Közzétéve: 2023. 03. 29.
8. Episode 30: Ben Eysenbach, CMU, on designing simpler and more principled RL algorithms

   Közzétéve: 2023. 03. 23.
9. Episode 29: Jim Fan, NVIDIA, on foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant

   Közzétéve: 2023. 03. 09.
10. 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

    Közzétéve: 2023. 03. 01.
11. Episode 27: Noam Brown, FAIR, on achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time

    Közzétéve: 2023. 02. 09.
12. Episode 26: Sugandha Sharma, MIT, on biologically inspired neural architectures, how memories can be implemented, and control theory

    Közzétéve: 2023. 01. 17.
13. Episode 25: Nicklas Hansen, UCSD, on long-horizon planning and why algorithms don't drive research progress

    Közzétéve: 2022. 12. 16.
14. Episode 24: Jack Parker-Holder, DeepMind, on open-endedness, evolving agents and environments, online adaptation, and offline learning

    Közzétéve: 2022. 12. 06.
15. Episode 23: Celeste Kidd, UC Berkeley, on attention and curiosity, how we form beliefs, and where certainty comes from

    Közzétéve: 2022. 11. 22.
16. Episode 22: Archit Sharma, Stanford, on unsupervised and autonomous reinforcement learning

    Közzétéve: 2022. 11. 17.
17. Episode 21: Chelsea Finn, Stanford, on the biggest bottlenecks in robotics and reinforcement learning

    Közzétéve: 2022. 11. 03.
18. Episode 20: Hattie Zhou, Mila, on supermasks, iterative learning, and fortuitous forgetting

    Közzétéve: 2022. 10. 14.
19. Episode 19: Minqi Jiang, UCL, on environment and curriculum design for general RL agents

    Közzétéve: 2022. 07. 19.
20. Episode 18: Oleh Rybkin, UPenn, on exploration and planning with world models

    Közzétéve: 2022. 07. 11.