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51:06EN
20VC with Harry Stebbings· 18 juil. 2025Podcast · 2 voixScott Wu on Cognition's Windsurf Deal and the Future of AI CodingInvité : Scott Wu · Anime : Harry StebbingsThe Cognition CEO explains the fast-moving acquisition, the role of AI agents in engineering, and his predictions for software development.Agents de codeSoftware engineeringAcquisition Strategy+1
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No Priors: AI, Machine Learning, Tech, & Startups· 17 juil. 2025Podcast · 2 voixEnterprise Coding Tools Flipped the 80/20. Reflection AI Built the Other HalfAnime : 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.Agents de codeIngénierie d’agentsÉvaluations+3
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Cognitive Revolution "How AI Changes Everything"· 16 juil. 2025PodcastPlus Five for Format, Plus Ten if It CompilesAnime : Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.Modèles et entraînementFine-tuningPost-training+3
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Unsupervised Learning: With Jacob Effron· 22 mai 2025PodcastThe New Ruler for Models Is How Long They Can WorkAnime : Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.Agents de codeIngénierie d’agentsÉvaluations+3
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No Priors: AI, Machine Learning, Tech, & Startups· 1 mai 2025Podcast · 2 voixo3 should wait exactly as long as the answer takesAnime : 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.Modèles et entraînementReasoning & test-time computeReinforcement Learning+3
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No Priors: AI, Machine Learning, Tech, & Startups· 24 avr. 2025Podcast · 2 voixDeep Research Turned Web Browsing into a Trainable ProblemAnime : 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.AgentsIngénierie d’agentsReinforcement Learning+3
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Cognitive Revolution "How AI Changes Everything"· 5 avr. 2025PodcastThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGIAnime : Nathan LabenzDeepMind's thinking-and-inference lead explains why RL-for-reasoning felt sudden, why Gemini shows raw chain of thought, and why a million-token window still is not memory.Modèles et entraînementReasoning & test-time computeReinforcement Learning+3
1:56:38EN
Cognitive Revolution "How AI Changes Everything"· 8 mars 2025Podcasto3 took ARC from 20% to 87%. Nathan Labenz sketches early superintelligenceAnime : Nathan LabenzOn Consistently Candid, the Cognitive Revolution host argues that math and coding will be superhuman by 2026, and that a reasoning core plus specialist “intuitive physics” may already be enough.Modèles et entraînementReinforcement LearningDistributed training & inference+3
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Sequoia Capital· 25 févr. 2025PodcastDon't draw the flowchart: OpenAI's Deep Research team on training agents end to endIsa Fulford and Josh Tobin explain why Deep Research was trained with reinforcement learning on hard browsing tasks instead of a human-written graph of model calls.AgentsIngénierie d’agentsReinforcement Learning+3
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Unsupervised Learning: With Jacob Effron· 19 févr. 2025PodcastThe Cheaper Intelligence Gets, the More of It We'll BuyAnime : Jacob EffronAmazon AGI Lab head David Luan on what DeepSeek actually meant, why next-token prediction is not enough, and why agents are still stuck on reliability.AgentsIngénierie d’agentsReinforcement Learning+3
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MBMatthew Berman· 11 févr. 2025Sam Altman’s 2025 AI Coding Forecast and Three ObservationsMatthew Berman, Sam Altman · Forward FutureMatthew Berman examines predictions about reasoning models, software agents and AI economics, distinguishing competitive-programming results from broader work.Agents de codeScaling LawsReinforcement Learning+2
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Cognitive Revolution "How AI Changes Everything"· 5 févr. 2025PodcastA 10B Model Trained Across Three Continents — and an H200 for $1.49 an HourAnime : Nathan LabenzPrime Intellect's founders lay out a four-step plan to turn spare global GPUs into collectively owned intelligence, and argue that reinforcement learning just made their job easier.Infrastructure et déploiementDistributed training & inferenceAI cost & performance+3
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Zhang Xiaojun Podcast· 4 févr. 2025Podcast · 2 voix潘嘉怡解读 R1、o1 与 Kimi K1.5:强化学习如何教模型学会推理Invité : 潘嘉怡 · Anime : 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 LearningModèles et entraînementAI research+2
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Sequoia Capital· 28 janv. 2025PodcastMove 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.AgentsReinforcement LearningIngénierie d’agents+3
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Cognitive Revolution "How AI Changes Everything"· 25 janv. 2025PodcastFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the TableAnime : Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.Modèles et entraînementReinforcement LearningReasoning & test-time compute+3
2 diapositives30:29EN
MBMatthew Berman· 3 janv. 2025How Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.Reinforcement LearningModèles et entraînementAI research+1
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Cognitive Revolution "How AI Changes Everything"· 3 déc. 2024PodcastA 70B Model Booked Dinner 20% of the Time. MultiOn Took It to 95% in a DayAnime : Nathan LabenzMultiOn CEO Div Garg returns to The Cognitive Revolution to recap 18 months of agent hype, the AgentQ stack, and why 2025 might still be the year of vertical agents.AgentsIngénierie d’agentsComputer use+3
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No Priors: AI, Machine Learning, Tech, & Startups· 14 nov. 2024Podcast · 2 voixA Fields Medalist Stuck for Two Hours. AlphaProof Climbed for Three Days.Anime : 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.AgentsReinforcement LearningReasoning & test-time compute+1
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Sequoia Capital· 2 oct. 2024PodcastAlphaGo Thought 30 Seconds a Move. o1 Made “Think Longer” GeneralOpenAI researchers Noam Brown, Ilge Akkaya, and Hunter Lightman on teaching language models to reason, the generator-verifier gap, and a second scaling curve at inference time.Modèles et entraînementReasoning & test-time computeReinforcement Learning+3
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Sequoia Capital· 24 sept. 2024PodcastHarmonic’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.Modèles et entraînementSynthetic dataReinforcement Learning+3
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Sequoia Capital· 17 sept. 2024PodcastHumanoid 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.AgentsRoboticsScaling Laws+3
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Sequoia Capital· 10 sept. 2024PodcastAGI Has One Problem Left: Spend a Million Times More on a TokenMagic founder Eric Steinberger tells Sequoia’s Training Data why he trains his own models, why a software engineer is the MVP of AGI, and why 96% still is not a colleague.Agents de codeAGIReasoning & test-time compute+3
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Zhang Xiaojun Podcast· 5 sept. 2024Podcast · 3 voix广密:Self-play RL 可能开启 AGI 的新 Scaling LawInvité : 广密 · Anime : 张小珺广密认为,纯靠语言预训练扩展模型的边际收益可能正在减弱,self-play 强化学习是更值得押注的新路径。它能否泛化到代码、视频、机器人和更广的领域,将决定下一阶段的 AGI 竞赛。Reinforcement LearningAGIScaling Laws+2
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Sequoia Capital· 20 août 2024PodcastThe 40% data-center save started with an email that ended in a question markPhaidra founder Jim Gao tells Sequoia how an AlphaGo-inspired bet at Google became reinforcement learning that runs billion-dollar plants.Infrastructure et déploiementReinforcement LearningData quality & curation+3
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