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344 件
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1:42:06EN
Cognitive Revolution "How AI Changes Everything"· 2025年7月8日ポッドキャストEvery Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It Goesホスト 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.モデルと学習Post-trainingData quality & curation+3
1:09EN
Matthew Berman· 2025年7月3日A Discussion of GPT-4.5’s Scaling and TrainingA speaker offers an assessment of GPT-4.5’s cost, speed and alleged training challenges.モデルと学習Scaling Laws
1:37:05EN
Cognitive Revolution "How AI Changes Everything"· 2025年6月19日ポッドキャストAn Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a Lieホスト 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.モデルと学習Model Interpretability機械学習の基礎+3
48:51EN
Sequoia Capital· 2025年6月17日ポッドキャストThe 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.モデルと学習Reasoning & test-time computePretraining & midtraining+3
スライド 9 枚22:05EN
Matthew Berman· 2025年6月9日Apple’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 computeBenchmarks評価(Evals)+1
スライド 8 枚10:09EN
Matthew Berman· 2025年5月30日DeepSeek 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.モデルと学習BenchmarksOpen-weight models+2
1:54:08EN
Cognitive Revolution "How AI Changes Everything"· 2025年5月29日ポッドキャストWhy Anthropic's first check went to a company building microscopes for neural netsホスト 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.モデルと学習Model InterpretabilityAI research+3
2:12:00EN
Cognitive Revolution "How AI Changes Everything"· 2025年5月15日ポッドキャストGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokensホスト Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.モデルと学習Agent memoryコンテキストエンジニアリング+3
47:00EN
Unsupervised Learning: With Jacob Effron· 2025年5月8日ポッドキャストAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a Decadeホスト 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.モデルと学習評価(Evals)Post-training+3
38:10EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年5月1日ポッドキャスト · 2 人o3 should wait exactly as long as the answer takesホスト 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.モデルと学習Reasoning & test-time computeReinforcement Learning+3
スライド0:34EN
Matthew Berman· 2025年5月1日DeepSeek-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
スライド 7 枚14:04EN
Matthew Berman· 2025年4月29日Qwen3: 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 modelsモデルと学習Reasoning & test-time compute+2
スライド 10 枚26:35EN
Matthew Berman· 2025年4月23日Testing 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.モデルと学習コーディングエージェントMultimodal AI+2
スライド 3 枚15:45EN
Matthew Berman· 2025年4月18日AI 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.モデルと学習AI cost & performanceエージェント
58:11EN
Sequoia Capital· 2025年4月15日ポッドキャストPatrick 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.モデルと学習AI for sciencePretraining & midtraining+3
2:29:41ZH
Zhang Xiaojun Podcast· 2025年4月7日ポッドキャスト · 4 人陈建宇:机器人基座模型如何走向通用 VLAゲスト 陈建宇 · ホスト 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。Roboticsモデルと学習Multimodal AI+2
1:14:11EN
Cognitive Revolution "How AI Changes Everything"· 2025年4月5日ポッドキャストThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGIホスト 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.モデルと学習Reasoning & test-time computeReinforcement Learning+3
スライド 5 枚26:05EN
Matthew Berman· 2025年3月31日Inside 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 researchモデルと学習AI Safety+1
1:43:54EN
Cognitive Revolution "How AI Changes Everything"· 2025年3月23日ポッドキャストA Year to Grow a Brain Organoid, an Hour to Simulate Itホスト 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.モデルと学習AI for scienceComputational Biology+3
35:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年3月20日ポッドキャスト · 2 人Robots Have No Wikipedia. Chelsea Finn Is Starting With Three Buildings.ホスト 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.モデルと学習RoboticsData quality & curation+3
1:10:23EN
Unsupervised Learning: With Jacob Effron· 2025年3月17日ポッドキャストA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still Untappedホスト 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.モデルと学習Reasoning & test-time computeScaling Laws+3
1:56:38EN
Cognitive Revolution "How AI Changes Everything"· 2025年3月8日ポッドキャストo3 took ARC from 20% to 87%. Nathan Labenz sketches early superintelligenceホスト 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.モデルと学習Reinforcement LearningDistributed training & inference+3
スライド 6 枚12:10EN
Matthew Berman· 2025年3月6日Mercury 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.モデルと学習Inferenceコーディングエージェント+2
1:02:41EN
20VC with Harry Stebbings· 2025年3月3日ポッドキャスト · 2 人Mike Krieger on AI Products, Model Competition and the Future of Codingゲスト Mike Krieger · ホスト Harry StebbingsAnthropic’s chief product officer explains how he thinks about product design, model differentiation and the limits of current AI tools.AI StrategyProduct Strategyモデルと学習+2
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