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2:51:18EN
Zhang Xiaojun Podcast· 15 oct. 2026Podcast · 2 voixLindon Gao on Building Dyna Robotics and Finding a Path Through the FogInvité : Lindon Gao · Anime : 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 StrategyModèles et entraînement+2
5 diapositives10:36EN
Matthew Berman· 13 oct. 2026Meta Muse Wonder Tested: Strong Benchmarks, Mixed Demos and Quota FrictionMatthew Berman · Forward FutureMatthew Berman tests Meta’s models on browser demos and reviews their published benchmark scores, availability and cost.Modèles et entraînementBenchmarksAI cost & performance+1
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Sequoia Capital· 13 oct. 2026PodcastAlphaGo Took Two Years. AlphaZero Took Hours. Silver Is Making the Same Bet on SuperintelligenceDavid Silver and Lasse Espeholt left DeepMind to build Ineffable Intelligence, a twenty-person lab that wants a superlearner to discover science, math, and language from experience rather than from a pre-training run.Modèles et entraînementReinforcement LearningRL environments & simulators+3
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Unsupervised Learning: With Jacob Effron· 6 oct. 2026PodcastPost-Training Wins Inference: Why Owning the Weights Beats Betting on One LabAnime : Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.Modèles et entraînementPost-trainingReinforcement Learning+3
5 diapositives16:57EN
Matthew Berman· 22 sept. 2026Grok 4.7 Compared: Coding, Knowledge Work and Cost per TaskMatthew Berman · Forward FutureMatthew Berman examines reported benchmarks and prices while noting missing competitors and limited hands-on testing.BenchmarksAI cost & performanceAgents de code+1
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No Priors: AI, Machine Learning, Tech, & Startups· 18 sept. 2026Podcast · 2 voixDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom SiliconAnime : 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.Modèles et entraînementInferenceAI cost & performance+3
7 diapositives1:16:29EN
Jane Street· 26 août 2026Grant 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 & attentionMathematicsFondamentaux du ML+1
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Cognitive Revolution "How AI Changes Everything"· 8 août 2026PodcastThe Periodic Table Was Already in the WeightsAnime : Nathan LabenzGoodfire CTO Dan Balsam tells Nathan Labenz how concept manifolds, predictive data debugging, and a $1,000-a-month research agent are turning interpretability into a product.Modèles et entraînementModel InterpretabilityIngénierie d’agents+3
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Sequoia Capital· 4 août 2026PodcastFrom One in a Thousand to 15 Percent: Chai Discovery Treats Drug Design as a Scaling ProblemJoshua Meier and Matthew McPartlon tell Sequoia’s Training Data why antibody design is an engineering problem, not a bespoke science, and why they sell a molecular CAD suite instead of their own drugs.Modèles et entraînementAI for scienceScaling Laws+3
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Sequoia Capital· 29 juil. 2026PodcastThe People Writing the Transformer’s Eulogy Want a Lab That Can Take Vacation Without Themo1’s reasoning lead and a Gemini pre-training lead argue the architecture, not more scale, is now the bottleneck — and that replacing it starts with automating kernel generation.Modèles et entraînementAI researchPretraining & midtraining+3
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Zhang Xiaojun Podcast· 28 juil. 2026Podcast · 2 voixYou Kaichao on How vLLM Grew from Open-Source Project to AI Infrastructure CompanyInvité : You Kaichao · Anime : 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
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Zhang Xiaojun Podcast· 16 juil. 2026Podcast · 2 voixKay Ke on Physical Intelligence, Robot Learning, and the Future of RoboticsInvité : Kay Ke · Anime : 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
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Cognitive Revolution "How AI Changes Everything"· 9 juil. 2026PodcastZero the J-Space and a Model's Hidden Goal Lights Up on Token OneAnime : Nathan LabenzA weekly Cognitive Revolution cut of Anthropic's global-workspace paper, AI superforecasters, open video world models, and a chip that treats inference as data movement.Modèles et entraînementModel InterpretabilitySécurité et gouvernance+3
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Unsupervised Learning: With Jacob Effron· 9 juil. 2026PodcastA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen SchmidhuberAnime : Jacob EffronThe AI pioneer tells Unsupervised Learning's Jacob Effron why he is wildly optimistic about the technology and deeply pessimistic about the model companies buying the chips.Modèles et entraînementAGIAI research+3
Diapositives0:24EN
Matthew Berman· 7 juil. 2026Claude Fable 5 Access Extended Through July 12, 2026Matthew Berman · Forward FutureMatthew Berman reports a short extension for paid Claude subscribers, without a reset of their existing usage quota.Modèles et entraînement
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Cognitive Revolution "How AI Changes Everything"· 4 juil. 2026Podcast19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipAnime : Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.Modèles et entraînementLocal inferenceScaling Laws+3
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Cognitive Revolution "How AI Changes Everything"· 3 juin 2026PodcastTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Anime : Nathan LabenzGoogle researcher Ali Behrouz tells Nathan Labenz how stacking update frequencies — not just layers — and an offline sleep phase could give language models genuine continual learning.Modèles et entraînementContinual learningTransformers & attention+3
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Unsupervised Learning: With Jacob Effron· 3 juin 2026PodcastA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still MissAnime : Jacob EffronThe Attention Is All You Need co-author tells Unsupervised Learning that transformers will probably keep winning — and that they still do not learn the way people do.Modèles et entraînementAI researchAgents de code+3
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Unsupervised Learning: With Jacob Effron· 22 mai 2026PodcastThe GPT moment for video hasn’t arrived, but AGI may already be hereAnime : Jacob EffronA day after Google I/O 2026, Gemini co-lead Oriol Vinyals argues that world models, file-system memory, and infinite-complexity RL environments will define the next era of frontier models.Modèles et entraînementWorld modelsReinforcement Learning+3
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Unsupervised Learning: With Jacob Effron· 15 mai 2026PodcastYann LeCun Left Meta Because LLMs Will Not Get You to IntelligenceAnime : Jacob EffronThe Turing Award winner tells Jacob Effron why he spun AMI Labs out of Meta, why JEPA-style world models beat pixel generators, and why he thinks Hinton changed his mind in 2023.Modèles et entraînementWorld modelsAI research+3
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Cognitive Revolution "How AI Changes Everything"· 1 mai 2026PodcastThe reward jumped from 3 to 180. Every Hacker News title was a Google layoff.Anime : Nathan LabenzCoreWeave's Kyle Corbitt walks Nathan Labenz through GRPO, rubrics, RL environments, and why most reward hacks are easy to catch once you know to look.Modèles et entraînementReinforcement LearningPost-training+3
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Unsupervised Learning: With Jacob Effron· 9 avr. 2026PodcastTwo Dates from OpenAI's Chief Scientist: Intern by September, Automated Researcher by 2028Anime : Jacob EffronJakub Pachocki tells Jacob Effron why those timelines still hold, why continual learning is not a detour, and why showing chain of thought in the product would train the monitor away.Modèles et entraînementContinual learningReinforcement Learning+3
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Cognitive Revolution "How AI Changes Everything"· 5 mars 2026PodcastDon't Fight Backprop: Goodfire's Bet on Designing What Models LearnAnime : Nathan LabenzDan Balsam and Tom McGrath return to The Cognitive Revolution with a $150 million Series B, a new research pillar called intentional design, and a rule for anyone who wants to steer training: stop trying to out-muscle gradient descent.Modèles et entraînementModel InterpretabilitySécurité et gouvernance+3
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Cognitive Revolution "How AI Changes Everything"· 18 févr. 2026PodcastThree IMO Golds Missed the Same Problem. Harmonic Wants Math to CompileAnime : Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.Modèles et entraînementReasoning & test-time computeAI research+3
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