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1:42:06EN
Cognitive Revolution "How AI Changes Everything"· Jul 8, 2025PodcastEvery Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It GoesHost 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.Models & trainingPost-trainingData quality & curation+3
1:09EN
Matthew Berman· Jul 3, 2025A Discussion of GPT-4.5’s Scaling and TrainingA speaker offers an assessment of GPT-4.5’s cost, speed and alleged training challenges.Models & trainingScaling Laws
1:37:05EN
Cognitive Revolution "How AI Changes Everything"· Jun 19, 2025PodcastAn Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a LieHost 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.Models & trainingModel InterpretabilityML fundamentals+3
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Sequoia Capital· Jun 17, 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.Models & trainingReasoning & test-time computePretraining & midtraining+3
9 slides22:05EN
Matthew Berman· Jun 9, 2025Apple’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 computeBenchmarksEvals+1
8 slides10:09EN
Matthew Berman· May 30, 2025DeepSeek R1 0528: Benchmark Gains and Two Coding TestsMatthew Berman · Forward FutureMatthew Berman examines the May 2025 DeepSeek reasoning-model update, comparing reported benchmark gains with his own Rubik’s Cube and Snake programming tests.Models & trainingBenchmarksOpen-weight models+2
1:54:08EN
Cognitive Revolution "How AI Changes Everything"· May 29, 2025PodcastWhy Anthropic's first check went to a company building microscopes for neural netsHost 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.Models & trainingModel InterpretabilityAI research+3
2:12:00EN
Cognitive Revolution "How AI Changes Everything"· May 15, 2025PodcastGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million TokensHost Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.Models & trainingAgent memoryContext engineering+3
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Unsupervised Learning: With Jacob Effron· May 8, 2025PodcastAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a DecadeHost 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.Models & trainingEvalsPost-training+3
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No Priors: AI, Machine Learning, Tech, & Startups· May 1, 2025Podcast · 2 speakerso3 should wait exactly as long as the answer takesHost 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.Models & trainingReasoning & test-time computeReinforcement Learning+3
Slides0:34EN
Matthew Berman· May 1, 2025DeepSeek-Prover-V2 and Its Formal Math ResultsMatthew Berman · Forward FutureMatthew Berman highlights DeepSeek’s reported theorem-proving results on miniF2F, PutnamBench and formalized AIME problems.MathematicsAI researchBenchmarks+1
7 slides14:04EN
Matthew Berman· Apr 29, 2025Qwen3: Open Weights, Adjustable Reasoning and Tool UseMatthew Berman · Forward FutureMatthew Berman reviews the April 2025 Qwen3 family, its benchmark claims, training pipeline and local demonstration.Open-weight modelsModels & trainingReasoning & test-time compute+2
10 slides26:35EN
Matthew Berman· Apr 23, 2025Testing Gemini 2.5 Flash on Coding and Video UnderstandingMatthew Berman · Forward FutureMatthew Berman compares Google’s faster model with Gemini 2.5 Pro through interactive simulations, games and questions about a long video.Models & trainingCoding agentsMultimodal AI+2
3 slides15:45EN
Matthew Berman· Apr 18, 2025AI News: Gemini 2.5 Flash, OpenAI Models, Claude Research and Kling 2.0Matthew Berman · Forward FutureMatthew Berman reviews a week of 2025 model releases, research tools and agent announcements, including historical price and benchmark comparisons.Models & trainingAI cost & performanceAgents
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Sequoia Capital· Apr 15, 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.Models & trainingAI for sciencePretraining & midtraining+3
2:29:41ZH → English
Zhang Xiaojun Podcast· Apr 7, 2025Podcast · 4 speakersChen Jianyu: How Robot Foundation Models Can Become General-Purpose VLA SystemsGuest 陈建宇 · Host 张小珺From SayCan and RT-2 to Diffusion Policy, Tsinghua assistant professor and Epoch Robotics founder Chen Jianyu traces the path from borrowing language models to end-to-end VLA. The hard problems remain diverse data, cross-embodiment generalization, and high-frequency action control.RoboticsModels & trainingMultimodal AI+2
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Cognitive Revolution "How AI Changes Everything"· Apr 5, 2025PodcastThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGIHost 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.Models & trainingReasoning & test-time computeReinforcement Learning+3
5 slides26:05EN
Matthew Berman· Mar 31, 2025Inside Claude: What Interpretability Research Reveals—and What It Cannot Yet ExplainMatthew Berman · Forward FutureMatthew Berman examines Anthropic’s case studies on multilingual features, planning, arithmetic, hallucinations and safety.AI researchModels & trainingAI Safety+1
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Cognitive Revolution "How AI Changes Everything"· Mar 23, 2025PodcastA Year to Grow a Brain Organoid, an Hour to Simulate ItHost 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.Models & trainingAI for scienceComputational Biology+3
35:14EN
No Priors: AI, Machine Learning, Tech, & Startups· Mar 20, 2025Podcast · 2 speakersRobots Have No Wikipedia. Chelsea Finn Is Starting With Three Buildings.Host 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.Models & trainingRoboticsData quality & curation+3
1:10:23EN
Unsupervised Learning: With Jacob Effron· Mar 17, 2025PodcastA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still UntappedHost 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.Models & trainingReasoning & test-time computeScaling Laws+3
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Cognitive Revolution "How AI Changes Everything"· Mar 8, 2025Podcasto3 took ARC from 20% to 87%. Nathan Labenz sketches early superintelligenceHost 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.Models & trainingReinforcement LearningDistributed training & inference+3
6 slides12:10EN
Matthew Berman· Mar 6, 2025Mercury Coder and Diffusion Language Models: Speed, Demos and LimitsMatthew Berman · Forward FutureMatthew Berman explores Inception’s 2025 coding model, comparing iterative text refinement with autoregressive generation and examining speed claims.Models & trainingInferenceCoding agents+2
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20VC with Harry Stebbings· Mar 3, 2025Podcast · 2 speakersMike Krieger on AI Products, Model Competition and the Future of CodingGuest Mike Krieger · Host Harry StebbingsAnthropic’s chief product officer explains how he thinks about product design, model differentiation and the limits of current AI tools.AI StrategyProduct StrategyModels & training+2
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