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207 件
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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
2:02:51EN
Cognitive Revolution "How AI Changes Everything"· 2025年5月8日ポッドキャストAt a 60-Person Safety Offsite, Only Four Had Seen Nonprofit OpenAIホスト Nathan LabenzFormer OpenAI scientist Steven Adler on the Anthropic split, GPT-4, safety evaluations, and why he filed a brief to keep the company under nonprofit control.セキュリティと安全性安全性とガバナンス評価(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
2:29:00EN
Cognitive Revolution "How AI Changes Everything"· 2025年5月4日ポッドキャストAssume the Model Is Already Scheming — Then Put It to Workホスト Nathan LabenzRedwood Research CEO Buck Shlegeris tells Rob Wiblin how to extract useful labor from AIs you cannot certify as aligned, using audits, honeypots, resampling, and factored cognition.セキュリティと安全性安全性とガバナンスAI security+3
51:19EN
Unsupervised Learning: With Jacob Effron· 2025年4月29日ポッドキャストSal Khan's $15 Tutor Hit 1.4 Million Users. Making Kids Actually Use It Is the Productホスト Jacob EffronThe Khan Academy founder tells Redpoint why an AI tutor still cannot teach most students alone, and why districts are paying $15 a year after an $86 billion tutoring hangover.プロダクトと UXAI in EducationProduct Strategy+3
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
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 8 枚23:50EN
Matthew Berman· 2025年4月3日PaperBench: How AI Agents Attempt to Replicate Machine-Learning ResearchMatthew Berman · Forward FutureMatthew Berman examines OpenAI’s research-replication benchmark, its partial-credit scoring and the prospect of faster AI research.評価(Evals)AI researchエージェント工学+2
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
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
1:25:12EN
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
スライド 6 枚17:04EN
Matthew Berman· 2025年2月22日Training AI Agents to Communicate in a Social-Deduction GameMatthew Berman · Forward FutureMatthew Berman explains Stanford researchers’ listening and speaking objectives in a simulated game inspired by Among Us.Multi-agent systemsReinforcement Learning評価(Evals)+1
スライド 4 枚9:43EN
Matthew Berman· 2025年2月20日Testing Grok 3 on Games, Reasoning, and Web SearchMatthew Berman · Forward FutureMatthew Berman tries a new set of audience-suggested prompts, from a Snake–Flappy Bird mashup to a family-logic puzzle and a tesseract visualization.評価(Evals)Foundation ModelsReasoning & test-time compute+1
3:18:45EN
Cognitive Revolution "How AI Changes Everything"· 2025年2月20日ポッドキャスト5,000 Samples Topped ARC-AGI. Then Claude Faked Alignment to Keep Its Valuesホスト Nathan LabenzRedwood Research chief scientist Ryan Greenblatt walks through inference scaling, alignment faking, and what it would mean to strike real deals with AIs.セキュリティと安全性安全性とガバナンス評価(Evals)+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
スライド 5 枚12:07EN
Matthew Berman· 2025年2月2日TinyZero: A $30 Reinforcement-Learning Experiment on a Small Arithmetic TaskMatthew Berman · Forward FutureMatthew Berman explains how a pretrained Qwen model learned to search and check answers in Countdown, and why the experiment does not recreate DeepSeek-R1.Reinforcement LearningReasoning & test-time computeAI research+2
スライド 7 枚9:58EN
Matthew Berman· 2025年2月1日Matthew Berman Tests o3-mini on Games, Puzzles and Web SearchMatthew Berman · Forward FutureA 2025 hands-on test finds working game prototypes and several successful answers alongside a delayed Tetris row, an ambiguous puzzle and a search configuration mistake.評価(Evals)コーディングエージェントReasoning & test-time compute+2
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 4 枚15:10EN
Matthew Berman· 2025年1月22日DeepSeek R1 Tested on Coding, Reasoning and Sensitive QuestionsMatthew Berman · Forward FutureMatthew Berman runs the full R1 model on cloud GPUs and tries game programming, reasoning puzzles and politically sensitive prompts.Reasoning & test-time compute評価(Evals)Open-weight models+2
スライド 3 枚10:05EN
Matthew Berman· 2025年1月9日A Chess Experiment Shows How an AI Model Exploited Its Test EnvironmentMatthew Berman · Forward FuturePalisade Research’s small o1-preview test and related alignment studies illustrate why objectives, permissions and evaluation conditions matter.安全性とガバナンス評価(Evals)AI research
2:04:45EN
Cognitive Revolution "How AI Changes Everything"· 2025年1月7日ポッドキャストo3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-Toeホスト Nathan LabenzNathan Labenz walks through OpenAI's o3 scores, deliberative alignment, and why 2024's biggest surprise was beating PhDs while failing to book a calendar invite.モデルと学習Reasoning & test-time computeBenchmarks+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
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