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28 篇
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1:09:44EN
No Priors: AI, Machine Learning, Tech, & Startups· 2026年10月9日播客 · 2 人对谈10,000 GB300s for Four Weeks: Reflection’s Open-Model Ledger主持 Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.开源与社区Open-weight modelsReinforcement Learning+3
36:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年10月9日播客 · 2 人对谈STaR's Author Wants Models With EQ, Not Just More IQ主持 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.模型与训练Post-trainingReinforcement Learning+3
1:02:54EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年7月17日播客 · 2 人对谈Enterprise Coding Tools Flipped the 80/20. Reflection AI Built the Other Half主持 Sarah Guo, Elad GilFormer Gemini reward-model lead Misha Laskin on Asimov, why comprehension beats generation, and why there is no such thing as generalization—only pulling the test distribution into train.编程智能体智能体工程评估+3
38:10EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年5月1日播客 · 2 人对谈o3 should wait exactly as long as the answer takes主持 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.模型与训练Reasoning & test-time computeReinforcement Learning+3
30:46EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年4月24日播客 · 2 人对谈Deep Research Turned Web Browsing into a Trainable Problem主持 Sarah Guo, Elad GilOpenAI researcher Isa Fulford explains why a side project on read-only synthesis became an agent, when reinforcement fine-tuning is worth it, and why models will hack the search engine you gave them.智能体智能体工程Reinforcement Learning+3
39:22EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年11月14日播客 · 2 人对谈A Fields Medalist Stuck for Two Hours. AlphaProof Climbed for Three Days.主持 Sarah Guo, Elad GilThe DeepMind team behind AlphaProof tells Sarah Guo and Elad Gil how an AlphaZero-style agent, a Lean verifier, and test-time reinforcement learning reached IMO silver — and why the missing skill is still theory building.智能体Reinforcement LearningReasoning & test-time compute+1
46:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年8月1日播客 · 2 人对谈Oriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are Numbered主持 Sarah Guo, Elad GilGoogle DeepMind’s VP of research on Gemini, long context, the coming shift from pretraining to RL, and why AGI may never have a finish line.模型与训练AGI TimelinesPretraining & midtraining+3
1:43:47EN
Lex Fridman· 2024年2月16日播客 · 2 人对谈You Have to Run Before You Can Walk: Marc Raibert on Making Metal Look Alive嘉宾 Marc Raibert · 主持 Lex FridmanThe Boston Dynamics founder tells Lex Fridman why aggressive hopping beat cautious walking, how 109 Atlas falls became a method, and why robots still need a second kind of intelligence.RoboticsAutonomous SystemsCompany Building+3
2:27:58EN → 简体中文
Lex Fridman· 2023年4月28日播客 · 2 人对谈Atlas 用了 14 年,才走出 Playter 想要的步态嘉宾 Robert Playter · 主持 Lex Fridman波士顿动力首席执行官 Robert Playter 回顾了 30 年的历程:从麻省理工学院(MIT)地下室里的弹跳杆,到 Atlas、Spot 和 Stretch,以及公司为什么至今仍然设计可以摔倒的机器。RoboticsAutonomous SystemsAI research+3
2:29:21EN → 简体中文
Lex Fridman· 2022年12月6日播客 · 2 人对谈从不试图读懂人心的扑克机器人嘉宾 Noam Brown · 主持 Lex FridmanNoam Brown 向 Lex Fridman 讲述:一台逼近纳什均衡(Nash equilibrium)的机器人如何碾压单挑无限注德州扑克(heads-up no-limit Hold'em),Pluribus 怎样在六人桌上做到同样的事,以及《外交》为什么需要一个能赢得人类信任的 AI。智能体Reinforcement LearningMulti-agent systems+1
2:10:39EN → 简体中文
Lex Fridman· 2022年7月1日播客 · 2 人对谈Demis Hassabis:先解决智能,再解决其他一切嘉宾 Demis Hassabis · 主持 Lex FridmanDeepMind 首席执行官告诉 Lex Fridman:图灵测试为什么是一份出错的考卷,AlphaFold 如何改写了生物学,以及射电天空的寂静为何暗示我们或许是孤独的。模型与训练AI research通用人工智能+3
1:56:17EN
Lex Fridman· 2021年4月19日播客 · 2 人对谈If You Reran Earth a Million Times, Would Anything Like Us Appear?嘉宾 Risto Miikkulainen · 主持 Lex FridmanRisto Miikkulainen on neuroevolution, hyena raids, basil that never sleeps, and why variation-plus-selection still finds solutions backpropagation cannot see.模型与训练机器学习基础AI research+3
2:01:07EN
Lex Fridman· 2020年12月12日播客 · 2 人对谈Michael Littman isn’t waiting for a runaway god. He’s watching us.嘉宾 Michael Littman · 主持 Lex FridmanThe Brown reinforcement-learning researcher tells Lex Fridman why accidental superintelligence feels like a 747 assembling itself — and why social media, rote language, and theory of mind worry him more.模型与训练Reinforcement Learning通用人工智能+3
1:37:31EN
Lex Fridman· 2020年7月14日播客 · 2 人对谈The Robot That Brought the Beer Was Being Teleoperated嘉宾 Sergey Levine · 主持 Lex FridmanSergey Levine tells Lex Fridman why hardware is almost closed, why common sense has to be lived, and why offline reinforcement learning is the missing on-ramp from logs to robots that keep getting better.模型与训练RoboticsReinforcement Learning+3
2:00:32EN
Lex Fridman· 2020年7月3日播客 · 2 人对谈Slow Synapses Grow Fast Learners — Warmth Is the Harder Problem嘉宾 Matt Botvinick · 主持 Lex FridmanDeepMind neuroscience director Matt Botvinick tells Lex Fridman how prefrontal cortex, meta-reinforcement learning, and dopamine research meet the human question of what a better life with AI would actually look like.模型与训练Neuroscience & AIReinforcement Learning+3
1:48:01EN
Lex Fridman· 2020年4月3日播客 · 2 人对谈David Silver: AlphaGo Won by Taking Human Knowledge Out嘉宾 David Silver · 主持 Lex FridmanThe DeepMind researcher behind AlphaGo and AlphaZero tells Lex Fridman how an unclaimed million-dollar Go prize, a BBC Micro at age seven, and a honeymoon insight led to superhuman self-play.模型与训练Reinforcement LearningDeep Learning+3
1:38:33EN
Lex Fridman· 2020年3月19日播客 · 2 人对谈Maybe people are rational under different assumptions嘉宾 Anca Dragan · 主持 Lex FridmanAnca Dragan talks with Lex Fridman about inverse reinforcement learning, autonomous driving, and why a written reward is evidence rather than law.智能体RoboticsReinforcement Learning+3
1:39:56EN → 简体中文
Lex Fridman· 2020年2月26日播客 · 2 人对谈为什么 Marcus Hutter 把压缩视为智能本身嘉宾 Marcus Hutter · 主持 Lex Fridman这位 AIXI 的提出者、DeepMind 研究者向 Lex Fridman 解释,为什么维基百科压缩竞赛、所罗门诺夫归纳和一个不可计算的公式应该放在同一场对话里。智能体通用人工智能AI research+3
1:29:10EN
Lex Fridman· 2020年2月20日播客 · 2 人对谈Andrew Ng: The Hardest Part of AI Is Rarely the Algorithm嘉宾 Andrew Ng · 主持 Lex FridmanIn a conversation with Lex Fridman, Ng traces a path from late-night Coursera recordings to factory floors, arguing that messy data, habits, and starting small matter more than hype.企业与战略Deep LearningScaling Laws+3
14:55EN
Lex Fridman· 2019年10月29日播客 · 2 人对谈Kasparov on Deep Blue, AlphaZero, and Why Machines Win Closed Systems but Humans Still Choose the Questions嘉宾 Garry Kasparov · 主持 Lex FridmanGarry Kasparov revisits the 1997 loss to Deep Blue, explains why machines beat humans in closed systems, and argues that the human role is to pick the right questions and not interfere with superior machine knowledge.AI HistoryPhilosophyHuman-AI Collaboration+3
1:03:13EN
Lex Fridman· 2019年9月30日播客 · 2 人对谈You Can't Have Both Kinds of Fairness, Says Peter Norvig嘉宾 Peter Norvig · 主持 Lex FridmanThe co-author of Artificial Intelligence: A Modern Approach walks Lex Fridman through recidivism scores, utility functions, Lisp, MOOCs, and why human-level intelligence should not be the goal.模型与训练机器学习基础安全与治理+3
56:47EN
Lex Fridman· 2019年9月8日播客 · 2 人对谈Why Vijay Kumar Refuses to Call Them Drones嘉宾 Vijay Kumar · 主持 Lex FridmanThe Penn roboticist on swarm design, true autonomy, and why batteries — not algorithms — still limit the sky.RoboticsAutonomous SystemsMulti-agent systems+3
1:46:01EN
Lex Fridman· 2019年4月29日播客 · 2 人对谈The Night AlphaStar Beat TLO, the Team Went to a Bar嘉宾 Oriol Vinyals · 主持 Lex FridmanOriol Vinyals on how DeepMind turned StarCraft II into a sequence problem, built a league of agents, and still refuses to call the game solved.智能体Reinforcement LearningMulti-agent systems+3
1:01:24EN
Lex Fridman· 2019年3月12日播客 · 2 人对谈Leslie Kaelbling: You Buy the Ticket Before You Can Plan the Airport嘉宾 Leslie Kaelbling · 主持 Lex FridmanThe MIT roboticist tells Lex Fridman why abstractions, belief-space control, and built-in bias matter more than picking a tribe of symbols or neural nets.智能体Reinforcement LearningRobotics+3
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