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95 件
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2:51:18EN
Zhang Xiaojun Podcast· 2026年10月15日ポッドキャスト · 2 人Lindon Gao on Building Dyna Robotics and Finding a Path Through the Fogゲスト Lindon Gao · ホスト 张小珺Dyna Robotics CEO Lindon Gao explains why his company builds intelligence through real workflows, how hardware and research must evolve together, and how a founder can find clarity by letting go of the need to be right.RoboticsProduct Strategyモデルと学習+2
38:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2026年9月18日ポッドキャスト · 2 人Diffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom Siliconホスト Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.モデルと学習InferenceAI cost & performance+3
スライド 7 枚1:16:29EN
JSJane Street· 2026年8月26日Grant Sanderson and Alok Puranik: Positional Encodings Through Group TheoryGrant Sanderson, Alok Puranik · 3Blue1BrownStarting with assumptions about attention, they derive matrix exponentials and connect rotations, decay and Jordan blocks to RoPE and ALiBi.Transformers & attentionMathematics機械学習の基礎+1
2:59:03EN
Zhang Xiaojun Podcast· 2026年7月28日ポッドキャスト · 2 人You Kaichao on How vLLM Grew from Open-Source Project to AI Infrastructure Companyゲスト You Kaichao · ホスト 张小珺Inferact co-founder You Kaichao traces vLLM’s path from Berkeley research to a company, then explains why model, inference, hardware and agent design increasingly have to move together.Company BuildingAI startups & small teamsAI research+2
3:46:12EN
Zhang Xiaojun Podcast· 2026年7月16日ポッドキャスト · 2 人Kay Ke on Physical Intelligence, Robot Learning, and the Future of Roboticsゲスト Kay Ke · ホスト 张小珺Physical Intelligence researcher Kay Ke traces the field's research lineages and explains Pi's progression from π₀ to π*₀.₆. She also reflects on education, fiction, ambition, and what it means to build robots that work beyond a demo.RoboticsReinforcement LearningAI research+2
36:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年10月9日ポッドキャスト · 2 人STaR's Author Wants Models With EQ, Not Just More IQホスト Sarah Guo, Elad GilEric Zelikman, the Stanford and xAI researcher behind STaR and Quiet-STaR, launches humans& on the bet that three years of IQ scaling left models emotionally unintelligent.モデルと学習Post-trainingReinforcement Learning+3
スライド 4 枚13:30EN
3Blue1Brown· 2025年8月17日How AlphaGeometry Combines Logical Proof with Creative ConstructionAleph0Aleph0 explains how a symbolic geometry solver gets surprisingly far without AI, and how a language model supplies the auxiliary constructions that take it to Olympiad level.MathematicsReasoning & test-time computeAI research+2
1:07:41EN
20VC with Harry Stebbings· 2025年7月21日ポッドキャスト · 2 人Edwin Chen on Building Surge and the Data Behind AI Modelsゲスト Edwin Chen · ホスト Harry StebbingsThe Surge founder discusses a bootstrapped business, training-data quality and the limits he sees in AI benchmarks and synthetic data.Data quality & curationCompany Buildingモデルと学習+2
47:05EN
MBMatthew Berman· 2025年7月21日Logan Kilpatrick on AI Developers, Interfaces, and the Future of ModelsMatthew Berman, Logan Kilpatrick · Forward FutureIn a July 2025 conversation, Google’s Logan Kilpatrick discusses Windsurf, agent interfaces, coding skills, Veo, and the shifting boundary between models and application scaffolding.AI researchコーディングエージェントエージェント+2
49:55EN
MBMatthew Berman· 2025年7月17日Aravind Srinivas on Comet, AI Browsers and the Future of WorkMatthew Berman, Aravind Srinivas · Forward FuturePerplexity’s CEO explains why the company built a browser, how it evaluates models and why he expects AI to disrupt jobs.エージェントProduct StrategyAI Strategy+2
1:09EN
MBMatthew Berman· 2025年7月3日A Discussion of GPT-4.5’s Scaling and TrainingA speaker offers an assessment of GPT-4.5’s cost, speed and alleged training challenges.モデルと学習Scaling Laws
スライド 9 枚22:05EN
MBMatthew Berman· 2025年6月9日Apple’s “Illusion of Thinking”: Puzzle Limits and a Code-Assisted TestMatthew Berman, Ilya Sutskever · Forward FutureMatthew Berman reviews Apple’s reasoning-model experiments and tests a different way to solve Tower of Hanoi using generated code.Reasoning & test-time computeBenchmarks評価(Evals)+1
38:10EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年5月1日ポッドキャスト · 2 人o3 should wait exactly as long as the answer takesホスト Sarah Guo, Elad GilOpenAI researchers Eric Mitchell and Brandon McKinzie tell No Priors why o3's leap is a chain of tools during reasoning — and why you should send the same prompt a hundred times.モデルと学習Reasoning & test-time computeReinforcement Learning+3
35:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年3月20日ポッドキャスト · 2 人Robots Have No Wikipedia. Chelsea Finn Is Starting With Three Buildings.ホスト Sarah Guo, Elad GilPhysical Intelligence’s cofounder tells No Priors why a generalist robot model has to be trained across embodiments, why open-sourcing hardware is a bet against the problem going unsolved, and why humanoids are a little overrated.モデルと学習RoboticsData quality & curation+3
1:02:41EN
20VC with Harry Stebbings· 2025年3月3日ポッドキャスト · 2 人Mike Krieger on AI Products, Model Competition and the Future of Codingゲスト Mike Krieger · ホスト Harry StebbingsAnthropic’s chief product officer explains how he thinks about product design, model differentiation and the limits of current AI tools.AI StrategyProduct Strategyモデルと学習+2
57:41EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年2月25日ポッドキャスト · 2 人A Hundred Million Cells, Four People, Three Days: Biology's ImageNet Momentホスト Sarah Guo, Elad GilVevo Therapeutics and the Arc Institute open-sourced Tahoe-100M and a 230-million-cell atlas in the same week — and argued that virtual cells, not just protein folders, are the next language model of biology.モデルと学習AI for scienceData quality & curation+3
1:18:09EN
20VC with Harry Stebbings· 2025年2月24日ポッドキャスト · 2 人Steeve Morin on AI Inference, Chips and the Cost of Switching Hardwareゲスト Steeve Morin · ホスト Harry StebbingsThe ZML founder explains the difference between training and production inference and offers a competitive view of the AI hardware market.インフラとデプロイInferenceAI cost & performance+2
2:36:13ZH
Zhang Xiaojun Podcast· 2025年2月24日ポッドキャスト · 2 人松琳解读 DeepSeek、Kimi 与 MiniMax 的注意力新论文ゲスト Songlin Yang(松琳) · ホスト 张小珺从动态稀疏注意力到线性注意力混合架构:三条长上下文路线如何兼顾训练、推理与硬件效率Transformers & attentionGPU programming & kernelsモデルと学習+2
1:25:52EN
20VC with Harry Stebbings· 2025年2月17日ポッドキャスト · 2 人Jonathan Ross on Groq, Inference Economics and the AI Infrastructure Raceゲスト Jonathan Ross · ホスト Harry StebbingsThe Groq CEO explains his bet on inference chips, compares his company’s architecture with GPUs, and discusses the financing and power needed to scale.インフラとデプロイモデルと学習AI cost & performance+2
22:32EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年2月7日ポッドキャスト · 2 人The $5.5 Million Was Only the Last Mileホスト Sarah Guo, Elad GilOn No Priors' 100th episode, Sarah Guo and Elad Gil separate DeepSeek's paper cost from the compute behind it, then walk through OpenAI's Deep Research, Stargate, and their bets for 2025.モデルと学習AI StrategyAI cost & performance+3
2:49:35ZH
Zhang Xiaojun Podcast· 2025年2月4日ポッドキャスト · 2 人潘嘉怡解读 R1、o1 与 Kimi K1.5:强化学习如何教模型学会推理ゲスト 潘嘉怡 · ホスト 张小珺伯克利博士生潘嘉怡带读 DeepSeek、OpenAI 与 Kimi 的推理模型报告,拆解长思维链、GRPO、奖励设计与蒸馏。R1-Zero 的“顿悟时刻”展示了推理策略如何在强化学习中涌现。Reinforcement Learningモデルと学習AI research+2
1:27:13ZH
Zhang Xiaojun Podcast· 2024年12月29日ポッドキャスト · 2 人李想:汽车将从交通工具进化为空间机器人ゲスト 李想 · ホスト 张小珺理想汽车 CEO 李想谈公司为何从造车走向人工智能:基座模型是 AI 时代的操作系统,汽车是进入物理世界的重要载体。对他而言,技术路线最终要落实为更好的用户体验、能力与长期价值。AI StrategyProduct StrategyAutonomous Driving+2
5:15:00EN
Lex Fridman· 2024年11月11日ポッドキャスト · 2 人Dario Amodei Has Seen This Scaling Movie Enough Timesゲスト Dario Amodei · ホスト Lex FridmanAnthropic’s CEO puts numbers on frontier training costs and AGI, then hands the mic to the people who shape Claude’s character and look inside its weights.モデルと学習安全性とガバナンスScaling Laws+3
1:19:00EN
20VC with Harry Stebbings· 2024年10月7日ポッドキャスト · 2 人Eiso Kant on Poolside, Compute and AI for Software Developmentゲスト Eiso Kant · ホスト Harry StebbingsPoolside’s CTO explains code-execution feedback, the cost of model development and his company’s ambitions in the AI race.AI researchコーディングエージェントモデルと学習+2
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