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1:42:06EN
Cognitive Revolution "How AI Changes Everything"· 2025년 7월 8일팟캐스트Every Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It Goes진행자 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.모델과 학습Post-trainingData quality & curation+3
1:09EN
Matthew Berman· 2025년 7월 3일A Discussion of GPT-4.5’s Scaling and TrainingA speaker offers an assessment of GPT-4.5’s cost, speed and alleged training challenges.모델과 학습Scaling Laws
1:37:05EN
Cognitive Revolution "How AI Changes Everything"· 2025년 6월 19일팟캐스트An Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a Lie진행자 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.모델과 학습Model Interpretability머신러닝 기초+3
48:51EN
Sequoia Capital· 2025년 6월 17일팟캐스트The 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.모델과 학습Reasoning & test-time computePretraining & midtraining+3
슬라이드 9장22:05EN
Matthew Berman· 2025년 6월 9일Apple’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 computeBenchmarks평가(Evals)+1
슬라이드 8장10:09EN
Matthew Berman· 2025년 5월 30일DeepSeek 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.모델과 학습BenchmarksOpen-weight models+2
1:54:08EN
Cognitive Revolution "How AI Changes Everything"· 2025년 5월 29일팟캐스트Why Anthropic's first check went to a company building microscopes for neural nets진행자 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.모델과 학습Model InterpretabilityAI research+3
2:12:00EN
Cognitive Revolution "How AI Changes Everything"· 2025년 5월 15일팟캐스트Gradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokens진행자 Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.모델과 학습Agent memory컨텍스트 엔지니어링+3
47:00EN
Unsupervised Learning: With Jacob Effron· 2025년 5월 8일팟캐스트An Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a Decade진행자 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.모델과 학습평가(Evals)Post-training+3
38:10EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 5월 1일팟캐스트 · 2명o3 should wait exactly as long as the answer takes진행자 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.모델과 학습Reasoning & test-time computeReinforcement Learning+3
슬라이드0:34EN
Matthew Berman· 2025년 5월 1일DeepSeek-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
슬라이드 7장14:04EN
Matthew Berman· 2025년 4월 29일Qwen3: 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 models모델과 학습Reasoning & test-time compute+2
슬라이드 10장26:35EN
Matthew Berman· 2025년 4월 23일Testing 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.모델과 학습코딩 에이전트Multimodal AI+2
슬라이드 3장15:45EN
Matthew Berman· 2025년 4월 18일AI 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.모델과 학습AI cost & performance에이전트
58:11EN
Sequoia Capital· 2025년 4월 15일팟캐스트Patrick 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.모델과 학습AI for sciencePretraining & midtraining+3
2:29:41ZH
Zhang Xiaojun Podcast· 2025년 4월 7일팟캐스트 · 4명陈建宇:机器人基座模型如何走向通用 VLA게스트 陈建宇 · 진행자 张小珺从 SayCan、RT-2 到 Diffusion Policy,清华助理教授、星动纪元创始人陈建宇梳理机器人模型从借用语言模型到端到端 VLA 的路线。关键难题仍是多样化数据、跨本体泛化和高频动作控制。Robotics모델과 학습Multimodal AI+2
1:14:11EN
Cognitive Revolution "How AI Changes Everything"· 2025년 4월 5일팟캐스트The Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGI진행자 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.모델과 학습Reasoning & test-time computeReinforcement Learning+3
슬라이드 5장26:05EN
Matthew Berman· 2025년 3월 31일Inside 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 research모델과 학습AI Safety+1
1:43:54EN
Cognitive Revolution "How AI Changes Everything"· 2025년 3월 23일팟캐스트A Year to Grow a Brain Organoid, an Hour to Simulate It진행자 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.모델과 학습AI for scienceComputational Biology+3
35:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 3월 20일팟캐스트 · 2명Robots Have No Wikipedia. Chelsea Finn Is Starting With Three Buildings.진행자 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.모델과 학습RoboticsData quality & curation+3
1:10:23EN
Unsupervised Learning: With Jacob Effron· 2025년 3월 17일팟캐스트A Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still Untapped진행자 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.모델과 학습Reasoning & test-time computeScaling Laws+3
1:56:38EN
Cognitive Revolution "How AI Changes Everything"· 2025년 3월 8일팟캐스트o3 took ARC from 20% to 87%. Nathan Labenz sketches early superintelligence진행자 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.모델과 학습Reinforcement LearningDistributed training & inference+3
슬라이드 6장12:10EN
Matthew Berman· 2025년 3월 6일Mercury 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.모델과 학습Inference코딩 에이전트+2
1:02:41EN
20VC with Harry Stebbings· 2025년 3월 3일팟캐스트 · 2명Mike Krieger on AI Products, Model Competition and the Future of Coding게스트 Mike Krieger · 진행자 Harry StebbingsAnthropic’s chief product officer explains how he thinks about product design, model differentiation and the limits of current AI tools.AI StrategyProduct Strategy모델과 학습+2
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