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1:29:30EN
Cognitive Revolution "How AI Changes Everything"· 14 oct. 2025Podcast2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New AntibioticsAnime : Nathan LabenzMIT's Jim Collins tells Nathan Labenz how a bootlegged 2018 project, a binary growth-inhibition label, and Chemprop graph networks pulled novel antibiotics out of chemical space that labs could never screen by hand.Modèles et entraînementAI for scienceDrug Discovery+3
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No Priors: AI, Machine Learning, Tech, & Startups· 9 oct. 2025Podcast · 2 voixSTaR's Author Wants Models With EQ, Not Just More IQAnime : 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.Modèles et entraînementPost-trainingReinforcement Learning+3
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Cognitive Revolution "How AI Changes Everything"· 2 oct. 2025PodcastChatGPT's Co-Creator Left to Put Nature in the Reward LoopAnime : Nathan LabenzLiam Fedus and Ekin Dogus Cubuk on why language models still fail at physics, why literature data is too noisy to train on, and why Periodic Labs is aiming at a 200K superconductor.Modèles et entraînementAI for scienceReinforcement Learning+3
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Cognitive Revolution "How AI Changes Everything"· 25 sept. 2025PodcastEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%Anime : Nathan LabenzEmily Sands, Stripe’s head of data and AI, explains how a domain-specific payments model, shared embeddings, and mixed rules turn a $1.4 trillion network into compounding advantage.Modèles et entraînementIA en financeEmbeddings+3
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Cognitive Revolution "How AI Changes Everything"· 27 août 2025PodcastSAEs Find the Nouns. Parameter Decomposition Looks for the VerbsAnime : Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.Modèles et entraînementModel InterpretabilityAI research+3
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3Blue1Brown· 17 août 2025How 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
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Unsupervised Learning: With Jacob Effron· 13 août 2025PodcastThey Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.Anime : Jacob EffronJoshua Meier on three waves of AI drug discovery, why Chai-2 behaves like Photoshop for molecules, and why a 1% hit rate was supposed to be enough to change the industry.Modèles et entraînementAI for scienceComputational Biology+3
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Sequoia Capital· 30 juil. 2025PodcastFrom 12 Percent on AIME to IMO Gold in Two MonthsAlex Wei, Sheryl Hsu, and Noam Brown tell Sequoia's Sonya Huang how a three-person OpenAI sprint used general-purpose reinforcement learning, not Lean, to reach gold-level on the 2025 International Mathematical Olympiad.Modèles et entraînementReasoning & test-time computeBenchmarks+3
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跟李沐学AI· 23 juil. 2025李沐:给文本大模型加入一千万小时语音,开源 Higgs Audio李沐 · Boson AI李沐介绍团队如何在文本大语言模型的训练里加入大量语音数据,做成能听能说的 AudioLLM,并把 Higgs Audio 开源。他讲了架构取舍、token 压缩、数据从哪来,以及怎么在 3090 上跑起来。IA vocaleMultimodal AIModèles et entraînement+2
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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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Cognitive Revolution "How AI Changes Everything"· 8 juil. 2025PodcastEvery Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It GoesAnime : Nathan LabenzLabelbox CEO Manu Sharma on the $15 billion Scale deal, RL “gyms,” $250,000 trainers, and why post-training now eats the budget that used to belong to pre-training.Modèles et entraînementPost-trainingData quality & curation+3
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Cognitive Revolution "How AI Changes Everything"· 19 juin 2025PodcastAn Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a LieAnime : Nathan LabenzJesse Hoogland and Daniel Murfet of Timaeus argue that singularities in the loss landscape — not the smooth valleys in every slide deck — are what actually govern how neural networks learn, generalize, and go wrong.Modèles et entraînementModel InterpretabilityFondamentaux du ML+3
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Sequoia Capital· 17 juin 2025PodcastThe Breakthroughs for AGI Are Already Done, Says OpenAI’s Former Research HeadBob McGrew tells Sequoia’s Training Data that 2025 is the year of reasoning, agents will be priced at the cost of compute, and nothing fundamental will be added to the stack by 2035.Modèles et entraînementReasoning & test-time computePretraining & midtraining+3
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Cognitive Revolution "How AI Changes Everything"· 29 mai 2025PodcastWhy Anthropic's first check went to a company building microscopes for neural netsAnime : Nathan LabenzGoodfire's Dan Balsam and Tom McGrath argue that mechanistic interpretability now has a proto-paradigm, two stubborn gaps, and three products worth shipping today.Modèles et entraînementModel InterpretabilityAI research+3
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Cognitive Revolution "How AI Changes Everything"· 15 mai 2025PodcastGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million TokensAnime : Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.Modèles et entraînementAgent memoryIngénierie du contexte+3
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Unsupervised Learning: With Jacob Effron· 8 mai 2025PodcastAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a DecadeAnime : Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.Modèles et entraînementÉvaluationsPost-training+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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Sequoia Capital· 15 avr. 2025PodcastPatrick Hsu Wants an App Store for Biology, Not Just Better DrugsThe Arc Institute co-founder argues that Evo 2, trained on genomes rather than proteins, should underpin tools for every step of the scientific method.Modèles et entraînementAI for sciencePretraining & midtraining+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
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Cognitive Revolution "How AI Changes Everything"· 23 mars 2025PodcastA Year to Grow a Brain Organoid, an Hour to Simulate ItAnime : Nathan LabenzStanford postdoc Siyu He tells Nathan Labenz how Squidiff and CORAL move single-cell and spatial experiments into the computer — and where the models still cannot go.Modèles et entraînementAI for scienceComputational Biology+3
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No Priors: AI, Machine Learning, Tech, & Startups· 20 mars 2025Podcast · 2 voixRobots Have No Wikipedia. Chelsea Finn Is Starting With Three Buildings.Anime : 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.Modèles et entraînementRoboticsData quality & curation+3
1:10:23EN
Unsupervised Learning: With Jacob Effron· 17 mars 2025PodcastA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still UntappedAnime : Jacob EffronThe Transformer co-inventor and Gemini researcher Jack Rae walk through test-time compute, Ilya Sutskever's AGI critique, and a four-year-old who now lectures his teacher about lizards.Modèles et entraînementReasoning & test-time computeScaling Laws+3
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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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No Priors: AI, Machine Learning, Tech, & Startups· 25 févr. 2025Podcast · 2 voixA Hundred Million Cells, Four People, Three Days: Biology's ImageNet MomentAnime : 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.Modèles et entraînementAI for scienceData quality & curation+3
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