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2:00:51EN
Cognitive Revolution "How AI Changes Everything"· 2026년 10월 10일팟캐스트Resample Every Token and You Can Watch the Model Decide진행자 Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.에이전트Prompting & in-context learningReasoning & test-time compute+3
슬라이드 29장51:55EN
3Blue1Brown· 2026년 9월 18일The last IMO problem AI could not solveGrant Sanderson · 3Blue1BrownGrant Sanderson walks through IMO 2025 Problem 6 — a tiling puzzle that frontier models missed — and the visual patience it takes to prove a construction is optimal.MathematicsAI researchReasoning & test-time compute+2
1:53EN
Matthew Berman· 2026년 7월 8일What Anthropic’s J-Space Research Suggests About AI ReasoningMatthew Berman · Forward FutureMatthew Berman discusses internal model representations, interventions and the question of consciousness, while the research itself describes important limits.Model InterpretabilityReasoning & test-time computeConsciousness & Intelligence
1:34:29EN
Cognitive Revolution "How AI Changes Everything"· 2026년 2월 18일팟캐스트Three IMO Golds Missed the Same Problem. Harmonic Wants Math to Compile진행자 Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.모델과 학습Reasoning & test-time computeAI research+3
슬라이드 5장7:49EN
Matthew Berman· 2025년 12월 2일DeepSeek 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
1:39:05EN
Cognitive Revolution "How AI Changes Everything"· 2025년 10월 29일팟캐스트The Summer GPT-5 Disappointed Does Not Mean AI Progress Has Stalled진행자 Nathan LabenzNathan Labenz tells Erik Torenberg why the GPT-5 backlash mixed up product mishaps, naming chaos, and a real jump in capabilities.기업과 전략Scaling LawsReasoning & test-time compute+3
슬라이드 3장11:38EN
Matthew Berman· 2025년 6월 12일AI News in June 2025: Magistral, Voice Models and Image GenerationMatthew Berman · Forward FutureMatthew Berman reviews Mistral’s reasoning models, voice and browser updates, reports on Meta and Scale AI, and FLUX.1 Kontext.Reasoning & test-time compute음성 AIGenerative media+2
슬라이드 7장15:13EN
Matthew Berman· 2025년 5월 10일Absolute Zero: Training a Reasoner on Self-Generated Coding TasksMatthew Berman · Forward FutureMatthew Berman examines a 2025 paper in which a pretrained language model proposes and solves code tasks using verifiable feedback.Reinforcement LearningSynthetic dataReasoning & test-time compute+2
슬라이드 4장19:06EN
Matthew Berman· 2025년 4월 25일Sleep-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
슬라이드 7장15:15EN
Matthew Berman· 2025년 4월 19일Early Reactions to OpenAI o3 and o4-mini: Tool Use, Math and MistakesMatthew Berman · Forward FutureMatthew Berman reviews 2025 demos and benchmark reports, from location guessing and coding to simple failures that challenge the hype.OpenAIBenchmarksReasoning & test-time compute+2
1:14:11EN
Cognitive Revolution "How AI Changes Everything"· 2025년 4월 5일팟캐스트The Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGI진행자 Nathan LabenzDeepMind's thinking-and-inference lead explains why RL-for-reasoning felt sudden, why Gemini shows raw chain of thought, and why a million-token window still is not memory.모델과 학습Reasoning & test-time computeReinforcement Learning+3
1:10:23EN
Unsupervised Learning: With Jacob Effron· 2025년 3월 17일팟캐스트A Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still Untapped진행자 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.모델과 학습Reasoning & test-time computeScaling Laws+3
슬라이드 4장9:35EN
Matthew Berman· 2025년 2월 25일Claude 3.7 Sonnet and Claude Code: An Early Coding TestMatthew Berman · Forward FutureMatthew Berman reviews Anthropic’s 2025 launch benchmarks and builds an AI-controlled Snake game, then tests math and recent information.코딩 에이전트BenchmarksAnthropic+1
3:18:45EN
Cognitive Revolution "How AI Changes Everything"· 2025년 2월 20일팟캐스트5,000 Samples Topped ARC-AGI. Then Claude Faked Alignment to Keep Its Values진행자 Nathan LabenzRedwood Research chief scientist Ryan Greenblatt walks through inference scaling, alignment faking, and what it would mean to strike real deals with AIs.보안과 안전안전과 거버넌스평가(Evals)+3
슬라이드 4장9:58EN
Matthew Berman· 2025년 1월 31일OpenAI o3-mini Launch: Reasoning Effort, Benchmarks and a Snake TestMatthew Berman · Forward FutureIn January 2025, Matthew Berman reviews OpenAI’s reported results and tries two coding examples with different reasoning settings.OpenAIReasoning & test-time computeBenchmarks+1
1:48:20EN
Cognitive Revolution "How AI Changes Everything"· 2025년 1월 25일팟캐스트From 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the Table진행자 Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.모델과 학습Reinforcement LearningReasoning & test-time compute+3
슬라이드 5장8:52EN
Matthew Berman· 2025년 1월 21일Industry Reactions to DeepSeek R1’s 2025 ReleaseMatthew Berman · Forward FutureMatthew Berman reviews early reactions to DeepSeek R1, from open-weight reasoning models and local hardware to benchmark results and API pricing.Open-weight modelsReasoning & test-time computeBenchmarks+2
1:33:01EN
Cognitive Revolution "How AI Changes Everything"· 2025년 1월 15일팟캐스트Rare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already Missed진행자 Nathan LabenzDr. Catherine Brownstein of Boston Children's Hospital explains why cheap genomes made her job harder, why data sharing still beats any model, and where ChatGPT Pro actually gives her hours back.AI in healthcareReasoning & test-time computePrompting & in-context learning+3
2:04:45EN
Cognitive Revolution "How AI Changes Everything"· 2025년 1월 7일팟캐스트o3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-Toe진행자 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.모델과 학습Reasoning & test-time computeBenchmarks+3
1:07:32EN
Unsupervised Learning: With Jacob Effron· 2024년 12월 18일팟캐스트o1 Is a New Generation — and the Day AGI Arrives Will Feel Boring진행자 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.모델과 학습Reasoning & test-time compute에이전트 엔지니어링+3
슬라이드 9장24:08EN
Matthew Berman· 2024년 12월 12일What Apollo’s 2024 Scheming Tests Found in Language ModelsMatthew Berman · Forward FutureMatthew Berman reviews experiments on deceptive actions, concealment and sandbagging, with the study’s staged goals and sandbox limitations in view.AI Safety평가(Evals)AI Alignment+1
48:18EN
Unsupervised Learning: With Jacob Effron· 2024년 12월 6일팟캐스트Noam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of Headroom진행자 Jacob EffronA ChatGPT query costs about a penny. The problems society cares about most, he says, are worth a million dollars.모델과 학습Reasoning & test-time computeAGI(범용 인공지능)+3
슬라이드 6장26:43EN
Matthew Berman· 2024년 10월 10일Testing OpenAI o1 on Planning, Rules and Spatial ReasoningMatthew Berman · Forward FutureMatthew Berman reviews a 2024 pilot study comparing o1-preview, o1-mini and GPT-4 on six planning domains.평가(Evals)Reasoning & test-time computeAI research+2
1:06:48EN
Unsupervised Learning: With Jacob Effron· 2024년 10월 3일팟캐스트o1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning Point진행자 Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.모델과 학습Reasoning & test-time computeAI research+3
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