Ask Generally Intelligent

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

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

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

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

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

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

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

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

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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

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

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Episode 24: Jack Parker-Holder, DeepMind, on open-endedness, evolving agents and environments, online adaptation, and offline learning

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Episode 22: Archit Sharma, Stanford, on unsupervised and autonomous reinforcement learning

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Episode 21: Chelsea Finn, Stanford, on the biggest bottlenecks in robotics and reinforcement learning

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Episode 20: Hattie Zhou, Mila, on supermasks, iterative learning, and fortuitous forgetting