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7 件
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53:44EN
Sequoia Capital· 2026年8月18日ポッドキャストThe Biggest Bug in Today’s Models Is That the Weights Never ChangeTuring Award winner Rich Sutton and Khurram Javed founded Oak Lab to build agents that keep learning after they ship — and they call synthetic data a big mistake.エージェントContinual learningReinforcement Learning+3
38:45EN
Sequoia Capital· 2026年6月16日ポッドキャストThe 25-agent town that became a Fortune 500 decision sandboxOn the day before Valentine's Day in a Stanford simulation, cafe owner Isabella decided to throw a party — no human wrote the plot.エージェントMulti-agent systemsエージェント工学+3
41:36EN
Sequoia Capital· 2025年11月18日ポッドキャストFour Months to Drive Tokyo: How Wayve Bet the Company on One End-to-End NetworkWayve CEO Alex Kendall tells Sequoia’s Training Data why AV 2.0 ditches HD maps and hand-written stacks for a foundation model that can adapt to a new car and a new country in weeks.モデルと学習Autonomous DrivingWorld models+3
38:25EN
Sequoia Capital· 2025年5月27日ポッドキャストA thousand airplanes will never train an AI engineerFormer Airbus CTO Paul Eremenko explains why physical-world engineering still has no coding-AI moment, and how P-1 AI's agent Archie is supposed to get there anyway.エージェントエージェント工学Synthetic data+3
55:40EN
Sequoia Capital· 2025年4月29日ポッドキャストTo Solve All Diseases, Isomorphic Labs Needs a Half-Dozen AlphaFoldsMax Jaderberg on turning DeepMind's game-playing research into a general AI engine for drug design—and why prediction alone cannot search a 10^60 molecular universe.企業と戦略AI for scienceSynthetic data+3
52:30EN
Sequoia Capital· 2025年1月28日ポッドキャストMove 37 looked like a bug. LLMs still need their AlphaZero momentIoannis Antonoglou, from AlphaGo to Reflection AI, on self-play, reliability, and why compute can still buy intelligence.エージェントReinforcement Learningエージェント工学+3
39:45EN
Sequoia Capital· 2024年9月24日ポッドキャストHarmonic’s Bet: Teach AI Math Directly, and the Riemann Hypothesis Falls by 2029Robinhood’s Vlad Tenev and Harmonic co-founder Tudor Achim tell Sequoia why math is reasoning, why Lean is the verifier, and why they refuse to wait for mathematical skill to emerge from scale.モデルと学習Synthetic dataReinforcement Learning+3