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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
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
1:00:26EN
Sequoia Capital· 2025年11月6日ポッドキャストSeven Million Videos a Day, and Most Return Visits Are Spent CreatingOpenAI's Sora 2 leads walk through diffusion transformers, physics that rebounds off the backboard, and a feed designed against mindless scrolling.マルチモーダルとメディアGenerative media画像と動画+3
43:09EN
Sequoia Capital· 2025年9月9日ポッドキャストA $100 Arm and a $300 Robot: Hugging Face Wants an App Store for Physical AIThomas Wolf tells Sequoia's Training Data why LeRobot, cheap hardware, and open datasets are Hugging Face's bet that software developers can become roboticists.オープンソースとコミュニティRoboticsOpen-source ecosystems+2
46:35EN
Sequoia Capital· 2024年11月13日ポッドキャストThey trained a real-time video model in 20 hours and skipped the game engineDecart CEO Dean Leitersdorf tells Sequoia how Oasis runs live on H100s, why ChatGPT is a fallen wall rather than a pain point, and why the next trillion-dollar company may be a generated experience.マルチモーダルとメディアGenerative media画像と動画+3
49:14EN
Sequoia Capital· 2024年9月17日ポッドキャストHumanoid robots fell from $1.5 million to $30,000. The GPT-3 moment is still a verbNvidia’s Jim Fan tells Sequoia how three kinds of data, a humanoid bet, and a System-1 motor-control model could unlock general-purpose robots.エージェントRoboticsScaling Laws+3
45:12EN
Sequoia CapitalポッドキャストInside a Robot Data Factory: How Mecka Sensorizes Humans to Train RobotsMecka AI founder Josh Gao explains how a company collects, quality-checks and licenses egocentric human data, and why robotics will arrive as a steady unlock of tasks rather than a single breakthrough.モデルと学習RoboticsData quality & curation+2