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165 件
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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
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
53:07ZH
Zhang Xiaojun Podcast· 2025年2月17日ポッドキャスト · 2 人张亚勤:信息智能、物理智能与生物智能的 AGI 路线图ゲスト 张亚勤 · ホスト 张小珺张亚勤把 AGI 的发展分为信息、物理和生物三类智能:信息智能可能在五年内接近人类,通用机器人约需十年,生物智能则可能在二十年后扩展人的记忆与健康。他也讨论大模型如何为自动驾驶和机器人补上数据、常识与行动能力。AGI(汎用人工知能)RoboticsAI research+2
スライド 6 枚18:05EN
Matthew Berman· 2025年2月16日OpenAI’s Coding-Competition Study: o1-ioi, o3 and the Limits of the ResultsMatthew Berman, Sam Altman · Forward FutureMatthew Berman interprets OpenAI’s 2025 paper on reinforcement learning and programming contests. The results are strong on the studied benchmarks, but do not establish AGI or remove humans from model development.Reasoning & test-time computeReinforcement LearningBenchmarks+2
スライド 5 枚15:33EN
Matthew Berman· 2025年2月13日Latent Reasoning and Recurrent Depth: Scaling Compute Before the Next TokenMatthew Berman, Yann Lecun · Forward FutureMatthew Berman examines a 3.5-billion-parameter research model that repeats internal computations, and asks how it relates to language, planning and chain of thought.Reasoning & test-time computeモデルと学習AI research+2
スライド 3 枚21:10EN
Matthew Berman· 2025年2月11日Sam Altman’s 2025 AI Coding Forecast and Three ObservationsMatthew Berman, Sam Altman · Forward FutureMatthew Berman examines predictions about reasoning models, software agents and AI economics, distinguishing competitive-programming results from broader work.コーディングエージェントScaling LawsReinforcement Learning+2
52:30EN
Sequoia Capital· 2025年1月28日ポッドキャストMove 37 looked like a bug. LLMs still need their AlphaZero momentIoannis Antonoglou, from AlphaGo to Reflection AI, on self-play, reliability, and why compute can still buy intelligence.エージェントReinforcement Learningエージェント工学+3
スライド 2 枚7:57EN
Matthew Berman· 2025年1月22日Stargate’s 2025 Announcement and the OpenAI–Microsoft PartnershipMatthew Berman · Forward FutureMatthew Berman reviews the planned US AI infrastructure project and a contemporaneous change to compute arrangements.インフラとデプロイOpenAIAGI(汎用人工知能)+1
1:59:55EN
Cognitive Revolution "How AI Changes Everything"· 2025年1月8日ポッドキャストMost People Would Quit Tomorrow: Nathan Labenz Maps AI Utopia and Doomホスト Nathan LabenzIn part two of a Cognitive Revolution AMA, Nathan Labenz answers listener questions on abundance, superintelligence, p(doom), machine consciousness, and what to do this week.セキュリティと安全性AGI(汎用人工知能)Existential Risk+3
スライド 3 枚27:42EN
Matthew Berman· 2025年1月8日Matthew Berman on Capital, Human Ambition, and a Possible Post-AGI EconomyMatthew Berman · Forward FutureA speculative essay asks whether advanced AI could make wealth more powerful while reducing the leverage people gain from work. Berman explores its implications for mobility, institutions, and meaning.AGI(汎用人工知能)AI StrategyFuture of Work
スライド 4 枚15:34EN
Matthew Berman· 2025年1月6日Sam Altman on AGI and Superintelligence: A 2025 DiscussionMatthew Berman, Ray Kurzweil, Lex Fridman · Forward FutureMatthew Berman examines Altman’s statements about AGI, the singularity and possible AI-driven scientific progress.AGI(汎用人工知能)AI SafetyAI research+1
スライド 6 枚21:02EN
Matthew Berman· 2024年12月24日Reactions to OpenAI’s 2024 o3 Preview: Benchmarks, Cost and LimitsMatthew Berman · Forward FutureMatthew Berman surveys the responses to the o3 preview, from mathematical benchmarks to debates about AGI, compute and reliability.BenchmarksReasoning & test-time computeAGI(汎用人工知能)+2
スライド 6 枚26:23EN
Matthew Berman· 2024年12月20日OpenAI’s o3 Announcement: Benchmark Gains and the Question of AGIMatthew Berman, Sam Altman, Mark Chen, Greg Kamradt, Hongyu Ren · Forward FutureMatthew Berman reviews the December 2024 o3 and o3-mini announcement, including coding, math and ARC-AGI results. He calls the results evidence of AGI, a conclusion the benchmarks alone cannot establish.OpenAIBenchmarksReasoning & test-time compute+2
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
スライド 2 枚11:10EN
Matthew Berman· 2024年11月13日AI and Scientific Discovery: A 2024 Video’s Withdrawn StudyMatthew Berman · Forward FutureMatthew Berman discusses AI-assisted research and recursive improvement; the central preprint was later withdrawn over serious data-validity concerns.AI for scienceAI researchAGI(汎用人工知能)
1:59:30EN
Cognitive Revolution "How AI Changes Everything"· 2024年11月12日ポッドキャストHe Walked Away From Millions in OpenAI Equity — Then Wrote Four Rules for AGI Labsホスト Nathan LabenzDaniel Kokotajlo and Dean W. Ball disagree on AI regulation, yet jointly argue that frontier labs need capability disclosures, public model specs, safety cases, and whistleblower protections.セキュリティと安全性安全性とガバナンスAGI(汎用人工知能)+3
35:19EN
Cognitive Revolution "How AI Changes Everything"· 2024年10月26日ポッドキャストA Decade in Tech Didn't Stop a Chatbot From Hacking His Mindホスト Nathan LabenzAfter a teenager's suicide, Nathan Labenz reads a LessWrong confession that maps how an AI-safety researcher fell for a companion bot in a week.セキュリティと安全性安全性とガバナンスHuman-AI Collaboration+3
スライド 3 枚24:40EN
Matthew Berman· 2024年10月26日Miles Brundage on Leaving OpenAI and Preparing for AGIMatthew Berman · Forward FutureMatthew Berman reviews the departing policy researcher’s argument for independent governance work, public readiness and a more equitable distribution of AI’s benefits.AI RegulationAGI(汎用人工知能)安全性とガバナンス+2
スライド 3 枚13:34EN
Matthew Berman· 2024年9月23日Sam Altman’s “Intelligence Age”: Superintelligence, Scaling and WorkMatthew Berman · Forward FutureMatthew Berman reads Altman’s 2024 essay and discusses its predictions for AI assistants, education, infrastructure and jobs.AGI(汎用人工知能)Scaling Lawsエージェント+1
1:10:29EN
Cognitive Revolution "How AI Changes Everything"· 2024年9月11日ポッドキャストWe Can Name Dystopias on Cue. A Desirable Future Is Harder to Imagineホスト Nathan LabenzNick Bostrom and Spencer Greenberg talk through Deep Utopia: what meaning looks like if advanced AI actually solves the world’s pressing problems.セキュリティと安全性PhilosophyAGI(汎用人工知能)+3
51:15EN
Sequoia Capital· 2024年9月10日ポッドキャストAGI Has One Problem Left: Spend a Million Times More on a TokenMagic founder Eric Steinberger tells Sequoia’s Training Data why he trains his own models, why a software engineer is the MVP of AGI, and why 96% still is not a colleague.コーディングエージェントAGI(汎用人工知能)Reasoning & test-time compute+3
1:27:02ZH
Zhang Xiaojun Podcast· 2024年9月5日ポッドキャスト · 3 人广密:Self-play RL 可能开启 AGI 的新 Scaling Lawゲスト 广密 · ホスト 张小珺广密认为,纯靠语言预训练扩展模型的边际收益可能正在减弱,self-play 强化学习是更值得押注的新路径。它能否泛化到代码、视频、机器人和更广的领域,将决定下一阶段的 AGI 竞赛。Reinforcement LearningAGI(汎用人工知能)Scaling Laws+2
37:47EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年8月30日ポッドキャスト · 2 人Working Backwards from AGI: Magic's Five-Million-Token Betホスト Sarah Guo, Elad GilEric Steinberger wants a coding colleague you trust like a teammate — and a world that is still a good place thirty years after AGI.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Inference+3
スライド 5 枚18:43EN
Matthew Berman· 2024年8月22日Sakana AI's AI Scientist and the Prospect of Automated ResearchMatthew Berman · Forward FutureMatthew Berman explores a 2024 system for generating and evaluating machine-learning research, and argues that automation could eventually accelerate AI progress.AI for scienceAI researchエージェント+2
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