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23 件
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1:47:38EN
Cognitive Revolution "How AI Changes Everything"· 2026年7月4日ポッドキャスト19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chipホスト 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.モデルと学習Local inferenceScaling Laws+3
1:13:33EN
Unsupervised Learning: With Jacob Effron· 2026年6月3日ポッドキャストA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still Missホスト 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.モデルと学習AI researchコーディングエージェント+3
4:25:13EN
Lex Fridman· 2026年1月31日ポッドキャストThe Architecture Barely Changed. Data, Cost, and Open Weights Did.ホスト Lex FridmanNathan Lambert and Sebastian Raschka walk Lex Fridman through a year in which training methods and datasets moved more than model design, and 2026 looks like a fight over inference cost, agents, and who owns truly open models.モデルと学習Scaling LawsOpen-weight models+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
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
1:39:05EN
Cognitive Revolution "How AI Changes Everything"· 2025年10月29日ポッドキャストThe Summer GPT-5 Disappointed Does Not Mean AI Progress Has Stalledホスト Nathan LabenzNathan Labenz tells Erik Torenberg why the GPT-5 backlash mixed up product mishaps, naming chaos, and a real jump in capabilities.企業と戦略Scaling LawsReasoning & test-time compute+3
1:02:05EN
Unsupervised Learning: With Jacob Effron· 2025年9月24日ポッドキャスト$100 Million Researchers and a $200 Billion xAI: An AI Roundtable Does the Mathホスト Jacob EffronDatology’s Ari Morcos and Radical Ventures’ Rob Toews argue over whether models are plateauing, whether RL-environment startups will last, and why Google is the lab they would pick.企業と戦略AI StrategyAI startups & small teams+3
51:08EN
Unsupervised Learning: With Jacob Effron· 2025年4月15日ポッドキャストThe Transformer Co-Author Who Says Scaling Hit a Wallホスト Jacob EffronCohere CEO Aidan Gomez tells Redpoint that a $100 million model is still an amnesiac genius, and that enterprise AI money is concentrating in support, research, and the back office.企業と戦略エンタープライズ AIAI Strategy+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
48:18EN
Unsupervised Learning: With Jacob Effron· 2024年12月6日ポッドキャストNoam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of Headroomホスト Jacob EffronA ChatGPT query costs about a penny. The problems society cares about most, he says, are worth a million dollars.モデルと学習Reasoning & test-time computeAGI(汎用人工知能)+3
2:40:58EN
Cognitive Revolution "How AI Changes Everything"· 2024年11月16日ポッドキャストZvi Mowshowitz Still Puts P(doom) at 60%—and Says Not Updating Is Self-Deceptionホスト Nathan LabenzA six-month recap with the Don't Worry About the Vase author covering Ilya’s SSI, OpenAI’s o1, Claude’s computer use, the SB 1047 veto, and Trump’s return.企業と戦略安全性とガバナンスAI Strategy+3
1:18:32EN
Cognitive Revolution "How AI Changes Everything"· 2024年9月20日ポッドキャストA DNA Model That Can Tell Which Genes You Cannot Touchホスト Nathan LabenzStanford's Brian Hie walks through hybrid architectures, a genome-scale language model trained only on DNA, and an antibody that binds 25 times tighter than nature.モデルと学習AI for scienceComputational Biology+3
1:23:57EN
Cognitive Revolution "How AI Changes Everything"· 2024年9月4日ポッドキャストA Training Run Is a Particle Collider—and the China Contest Is a Fifty-Year Raceホスト Nathan LabenzSamo Burja tells Nathan Labenz why ChatGPT's virality may have slowed AI science, and why Washington should treat Beijing as an economic rival rather than a military one.企業と戦略GeopoliticsAI Strategy+3
1:01:55EN
Cognitive Revolution "How AI Changes Everything"· 2024年6月1日ポッドキャストA $9 AI nurse, an 86 on the medical boards, and a 10% extinction lineホスト Nathan LabenzNathan Labenz’s solo scouting report walks from medical state of the art through how models actually learn, then to live players, scaling trends, and a control plan that still does not exist.企業と戦略AI StrategyScaling Laws+3
1:01:39EN
Cognitive Revolution "How AI Changes Everything"· 2024年4月13日ポッドキャストGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?ホスト Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.モデルと学習Scaling LawsSynthetic data+3
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· 2023年10月18日ポッドキャスト1,000 H100s Take 115 Days; 22,000 Take Fiveホスト Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.モデルと学習AI researchModel Interpretability+3
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 2023年7月6日ポッドキャストThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understandホスト Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.モデルと学習機械学習の基礎Scaling Laws+3
1:32:22EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月29日ポッドキャストA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer Awayホスト Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.モデルと学習Multimodal AITransformers & attention+3
26:02EN
Lex Fridman· 2020年2月9日ポッドキャストMoore’s Law Is Not Dead—It Is a Cascade of S-Curvesホスト Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.インフラとデプロイSemiconductorsScaling Laws+3
1:10:19EN
Unsupervised Learning: With Jacob EffronポッドキャストThe Robot That Folded the Laundry: Physical Intelligence’s Three Frontiersホスト Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.モデルと学習RoboticsAI research+3
1:14:59EN
Lenny's PodcastポッドキャストWhy Anthropic's Ben Mann Thinks the Exponential Hasn't Slowed, and Why He Still Worries About Safetyホスト Lenny RachitskyThe Anthropic co-founder on the AI talent war, scaling laws, the economic Turing test, jobs, leaving OpenAI, constitutional AI and how he forecasts the risk of superintelligence.セキュリティと安全性安全性とガバナンスAGI Timelines+3
1:19:48EN
Lex FridmanポッドキャストBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep Learningホスト Lex FridmanIn a 2016 Deep Learning School talk, Andrew Ng distills lessons from leading Baidu's AI team into a workflow for diagnosing models, handling data and building a career.モデルと学習機械学習の基礎Deep Learning+3
1:13:40EN
Unsupervised Learning: With Jacob EffronポッドキャストThe Long Tail, the World Model, and the 1995 Problem: Inside Waymo's Scaling Phase and Robotics' Missing Momentホスト Jacob EffronVincent, a distinguished engineer at Waymo, explains how foundation models became teachers for self-driving, why the long tail and causality are the real frontier, and where robotics still lacks its generalist proof point.Autonomous DrivingRoboticsMultimodal AI+3