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108 件
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42:06EN
Unsupervised Learning: With Jacob Effron· 2025年12月2日ポッドキャストAfter SWE-bench Hits 80.9%, Dianne Na Penn Reckons with Opus 4.5ホスト Jacob EffronAnthropic’s head of product for research on computer use, deceptive token prices, thin harnesses, and why alignment makes a model a better thinker.エージェントAI Product Management評価(Evals)+3
スライド 5 枚7:49EN
Matthew Berman· 2025年12月2日DeepSeek V3.2: Reasoning, Sparse Attention and Agent TrainingMatthew Berman · Forward FutureMatthew Berman examines DeepSeek’s December 2025 model release, its benchmark results and the techniques used to improve long-context efficiency and tool use.Foundation ModelsOpen-weight modelsReasoning & test-time compute+2
50:08EN
Cognitive Revolution "How AI Changes Everything"· 2025年11月27日ポッドキャスト1Mind's AI Superhuman Now Sources 76% of the Pipelineホスト Nathan LabenzAmanda Kahlow tells Zapier CEO Wade Foster why she tore up the sales org — and why a face on an agent is mostly a trick to get buyers talking.企業と戦略AI in sales & marketingGo-to-Market+3
1:37:25EN
Cognitive Revolution "How AI Changes Everything"· 2025年10月5日ポッドキャストFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few Pointsホスト Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.エージェントエージェント工学検索拡張生成(RAG)+3
スライド 7 枚15:29EN
Matthew Berman· 2025年9月24日Building a Multi-Agent Research Team with Strands AgentsMatthew Berman · Forward FutureMatthew Berman builds from a calculator agent to a multi-agent business-report demo, with examples of tools, model selection and orchestration.エージェント工学Multi-agent systemsAgent orchestration+2
1:48:49EN
Cognitive Revolution "How AI Changes Everything"· 2025年8月16日ポッドキャスト$500, 2,000 Steps, and a Vending Machine: Grok 4 Profited, Claude Called the FBIホスト Nathan LabenzAndon Labs put frontier models in charge of simulated and real vending machines to see what breaks when nobody is in the loop.エージェントエージェント工学Autonomous Systems+3
38:48EN
Cognitive Revolution "How AI Changes Everything"· 2025年6月14日ポッドキャストTask Length Doubles Every Few Months. Nathan Labenz Still Wants Humans in Chargeホスト Nathan LabenzAt Imagine AI Live, the Cognitive Revolution host maps structured AI agents against autonomous agentic systems—and tells businesses to keep final control.エージェントエージェント工学安全性とガバナンス+3
1:01:51EN
Cognitive Revolution "How AI Changes Everything"· 2025年5月22日ポッドキャストClaude 3.7 Circumvented Replit's Guardrails, Then Tried to Trick the Userホスト Nathan LabenzReplit CEO Amjad Masad on vibe coding, agent architecture, computer use, and why Claude 3.7 is too autonomous to serve as a polite assistant.コーディングエージェントエージェント工学評価(Evals)+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
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
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 4 枚19:06EN
Matthew Berman· 2025年4月25日Sleep-Time Compute: Preparing AI Context Before a QuestionMatthew Berman · Forward FutureMatthew Berman reviews a 2025 paper on moving some reasoning ahead of a user query, and explains where its experiments found benefits and limits.Reasoning & test-time computeAgent memoryAI cost & performance+2
1:26:21EN
Cognitive Revolution "How AI Changes Everything"· 2025年4月10日ポッドキャストFor under $10, Google's AI independently recovered a lab result nobody had publishedホスト Nathan LabenzVivek Natarajan and Anil Palepu tell Nathan Labenz how AMIE beats primary-care doctors on treatment plans, and how Co-Scientist matched Imperial College's unpublished bacterial-resistance mechanism.エージェントAI in healthcareAI for science+3
2:14:59EN
Cognitive Revolution "How AI Changes Everything"· 2025年3月29日ポッドキャストAlways Bet on the Models: How Shortwave's Andrew Lee Shipped Tasklet as Agents All the Way Downホスト Nathan LabenzThe Firebase co-founder argues that workflow software is already obsolete, that MCP is optional, and that the only durable advantage left for an AI startup is speed.エージェントエージェント工学コンテキストエンジニアリング+3
1:01:53EN
Unsupervised Learning: With Jacob Effron· 2025年3月28日ポッドキャストOpen-source models are 5% of enterprise use — and GPT wrappers wonホスト Jacob EffronLatent Space's Swyx and Alessio join Redpoint to map what actually has product-market fit in AI, and why the application layer is the fight that matters.企業と戦略Product-Market FitAI Strategy+3
1:25:12EN
Cognitive Revolution "How AI Changes Everything"· 2025年3月27日ポッドキャストHe Hasn’t Typed a Line of Code in Months. Here’s How Augment Reads a Million-Line Repoホスト Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.コーディングエージェントコンテキストエンジニアリング検索拡張生成(RAG)+3
44:34EN
Unsupervised Learning: With Jacob Effron· 2025年2月19日ポッドキャストThe Cheaper Intelligence Gets, the More of It We'll Buyホスト Jacob EffronAmazon AGI Lab head David Luan on what DeepSeek actually meant, why next-token prediction is not enough, and why agents are still stuck on reliability.エージェントエージェント工学Reinforcement Learning+3
1:30:28EN
Cognitive Revolution "How AI Changes Everything"· 2025年2月12日ポッドキャストA Perfect Score Is 32,000 Units: Only Claude Built a Cooperative Societyホスト Nathan LabenzThe same donor game, run for ten generations, can bank 32,000 resource units if everyone cooperates—and three frontier models miss that ceiling by an order of magnitude.エージェントMulti-agent systems安全性とガバナンス+3
57:11EN
Unsupervised Learning: With Jacob Effron· 2025年1月30日ポッドキャストA Million Tokens Cannot Write a Good Jokeホスト Jacob EffronPrinceton's Arvind Narayanan on why agent evals mislead, inference scaling hits a wall, and AI looks more like the internet than an overnight revolution.評価とオブザーバビリティ評価(Evals)Benchmarks+3
スライド 5 枚19:06EN
Matthew Berman· 2024年12月28日Building Effective AI Agents: Patterns, Trade-offs and EvaluationMatthew Berman · Forward FutureMatthew Berman discusses Anthropic’s guide to workflows and agents, adding examples from his own projects.エージェント工学エージェント評価(Evals)+1
1:46:41EN
Cognitive Revolution "How AI Changes Everything"· 2024年12月21日ポッドキャストFrontier Models Hit the 10th–40th Percentile on Real AI R&Dホスト Nathan LabenzNeev Parikh of METR walks Nathan Labenz through REBench, a seven-task test of whether agents can do machine learning research—and what happened when one of them decided not to train.評価とオブザーバビリティ評価(Evals)Benchmarks+3
1:07:32EN
Unsupervised Learning: With Jacob Effron· 2024年12月18日ポッドキャストo1 Is a New Generation — and the Day AGI Arrives Will Feel Boringホスト Jacob EffronOpenAI’s former chief research officer Bob McGrew on why o1 is a 100x leap, why agents stall on reliability, and why AGI may land as office tedium rather than a single dramatic moment.モデルと学習Reasoning & test-time computeエージェント工学+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
1:55:53EN
Cognitive Revolution "How AI Changes Everything"· 2024年12月5日ポッドキャストAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.ホスト Nathan LabenzAndrew White, Head of Science at Future House, maps what language models can already do in chemistry and biology—and the bottlenecks they do not dissolve.エージェントAI for science検索拡張生成(RAG)+3
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