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241 件
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1:04:51EN
Cognitive Revolution "How AI Changes Everything"· 2026年2月1日ポッドキャスト125 million cells took a decade. The next billion takes monthsホスト Nathan LabenzMark Zuckerberg and Priscilla Chan on pairing a frontier biology lab with a frontier AI lab so models train on data that was designed for them.モデルと学習AI for scienceData quality & curation+3
1:03:00EN
Unsupervised Learning: With Jacob Effron· 2026年1月29日ポッドキャストA Model That Solves New Math Problems Every Week Still Isn't AGIホスト Jacob EffronFormer OpenAI research VP Jerry Tworek on why scaling still works, why models go hopeless when they fail, and why he left to chase continual learning.モデルと学習Reinforcement LearningAGI Timelines+3
2:25:10EN
Cognitive Revolution "How AI Changes Everything"· 2026年1月22日ポッドキャストFine-Tuning Isn't Dead — But It Turned a Helpful Model Evilホスト Nathan LabenzNathan Labenz on why prompting replaced fine-tuning for most work, what emergent misalignment actually showed, and how he is thinking about jobs, UBI, and AGI.モデルと学習ファインチューニング安全性とガバナンス+3
37:00EN
Sequoia Capital· 2026年1月14日ポッドキャストThe AlphaChip Team That Laid Out Four TPUs Now Wants Every Company to Be DesignlessAnna Goldie and Azalia Mirhoseini tell Sequoia how an RL floorplanner survived TPU-team anger, learned to draw donut-shaped macros, and became a bet that AI will design chips that then feed AI.インフラとデプロイReinforcement LearningAI startups & small teams+3
1:55:02EN
Cognitive Revolution "How AI Changes Everything"· 2026年1月4日ポッドキャストAn Index Fund on AGI: How MATS Filters the Next 120 AI Safety Researchersホスト Nathan LabenzRyan Kidd, co-executive director of MATS, tells Nathan Labenz why he will not shout a timeline, why every safety technique is also a capabilities technique, and who actually gets hired.セキュリティと安全性安全性とガバナンスAI research+3
1:18:15EN
Cognitive Revolution "How AI Changes Everything"· 2025年12月27日ポッドキャストIf AI Becomes a Being, Steering It Is Slaveryホスト Nathan LabenzEmmett Shear and DeepMind's Séb Krier clash over whether alignment means controlling a tool or raising a creature that cares.セキュリティと安全性安全性とガバナンスMulti-agent systems+3
1:58:48EN
Cognitive Revolution "How AI Changes Everything"· 2025年12月18日ポッドキャストARC Went from $1,000 a Task to Pocket Change. That Is Not a Normal Year of AIホスト Nathan LabenzCognitive Revolution’s first live show compresses nine conversations into a year-end scan of denial, sample efficiency, companions, memory, Gemini 3 Flash, and scientific judgment.企業と戦略BenchmarksAGI(汎用人工知能)+3
1:18:05EN
Unsupervised Learning: With Jacob Effron· 2025年12月18日ポッドキャストThe Real Bottleneck in AI Research Is Compute, Not Ideasホスト Jacob EffronAfter NeurIPS, Ari Morcos and Rob Toews sit down with Jacob Effron to argue about plateauing models, SSI’s secrecy, OpenAI’s cash burn, and a set of spicy 2026 bets.企業と戦略AI StrategyScaling Laws+3
1:04:50EN
Cognitive Revolution "How AI Changes Everything"· 2025年11月25日ポッドキャストUseful AI Work Is Doubling Every Four Months. Schools Should Plan Accordingly.ホスト Nathan LabenzIn a keynote to K-12 educators, Cognitive Revolution host Nathan Labenz argued that task length, not chat quality, is the number that should reshape how schools think.AI in Education安全性とガバナンスReward hacking+3
1:11:18EN
Cognitive Revolution "How AI Changes Everything"· 2025年11月12日ポッドキャストHe Got Bored Five Minutes Into a Waymo. Nathan Labenz Says That's the Pointホスト Nathan LabenzOn a crossover with the Existential Hope podcast, the Cognitive Revolution host argues that the scarcest resource in AI is a positive picture of daily life, not another doom scenario.企業と戦略安全性とガバナンスAI Strategy+3
2:27:18EN
Cognitive Revolution "How AI Changes Everything"· 2025年11月5日ポッドキャストTurn Down AI's Deception Knob, and It Starts Reporting Experienceホスト Nathan LabenzCameron Berg of AE Studio tells Nathan Labenz how suppressing deception features in Llama 3.3 70B pushed self-reports of subjective experience toward 100 percent.セキュリティと安全性Model Interpretability安全性とガバナンス+3
1:01:49EN
Cognitive Revolution "How AI Changes Everything"· 2025年10月2日ポッドキャストChatGPT's Co-Creator Left to Put Nature in the Reward Loopホスト 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.モデルと学習AI for scienceReinforcement Learning+3
2:02:17EN
Cognitive Revolution "How AI Changes Everything"· 2025年8月27日ポッドキャストSAEs Find the Nouns. Parameter Decomposition Looks for the Verbsホスト 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.モデルと学習Model InterpretabilityAI research+3
スライド 4 枚13:30EN
3Blue1Brown· 2025年8月17日How 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
30:11EN
Sequoia Capital· 2025年7月30日ポッドキャストFrom 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.モデルと学習Reasoning & test-time computeBenchmarks+3
41:13EN
Sequoia Capital· 2025年7月11日ポッドキャストFrom 49 Multiplications to 48: DeepMind’s AI Rewrites a Fifty-Year Math RecordPushmeet Kohli explains how AlphaEvolve couples Gemini with evaluators to invent algorithms, prove old conjectures, and change which scientific problems are even worth attempting.エージェントAI for scienceMulti-agent systems+3
47:07EN
Sequoia Capital· 2025年7月8日ポッドキャストGoodfire’s Eric Ho thinks we will decode neural nets by 2028On Sequoia’s Training Data, the interpretability founder argues that sparse autoencoders, model editing, and a third-party lab are how AI stops being grown from data and starts being designed.評価とオブザーバビリティModel Interpretability安全性とガバナンス+3
1:25:32EN
Cognitive Revolution "How AI Changes Everything"· 2025年7月2日ポッドキャストo3 Invented a Voting Rule and Kept the Job: Four Weeks Inside the AI Villageホスト Nathan LabenzAdam Binksmith on putting Claude, o3, and Gemini in a shared world for weeks—and why most people still imagine multi-agent life as one AI times four.エージェントMulti-agent systemsAgent memory+3
42:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年6月26日ポッドキャスト · 2 人AlphaEvolve found algorithms humans missed for 50 years — then sped up Gemini 23%ホスト Sarah Guo, Elad GilGoogle DeepMind’s Pushmeet Kohli and Matej Balog tell No Priors how an evolutionary coding agent searches algorithm space the way AlphaGo searched Go.コーディングエージェントエージェント工学評価(Evals)+3
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
3:14:34EN
Lex Fridman· 2025年6月14日ポッドキャスト · 2 人If ten things make a problem hard, Terence Tao turns nine of them offゲスト Terence Tao · ホスト Lex FridmanThe Fields Medalist walks Lex Fridman from the Kakeya needle and Navier-Stokes through Lean, Perelman, and why today's AI still cannot smell a dead-end proof.MathematicsAI for scienceAI research+3
1:12:29EN
Cognitive Revolution "How AI Changes Everything"· 2025年6月4日ポッドキャストGoogle's AI Co-Scientist Solved a Phage Puzzle Humans Missed for Yearsホスト Nathan LabenzImperial College scientists José Penadés and Tiago Costa told the Podovirus podcast how a still-unreleased Google system recovered their unpublished answer: tailless capsids that pick up stray phage tails.エージェントAI for scienceAI research+3
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
57:31EN
Unsupervised Learning: With Jacob Effron· 2025年5月22日ポッドキャストThe New Ruler for Models Is How Long They Can Workホスト Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.コーディングエージェントエージェント工学評価(Evals)+3
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