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114 件
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スライド 4 枚6:53EN
Matthew Berman· 2025年7月24日Qwen3-Coder: An Open-Weight Coding Model and Its Command-Line AgentMatthew Berman · Forward FutureMatthew Berman examines Qwen’s July 2025 coding model, its training and benchmark results, and tries its command-line tool on a Snake game.コーディングエージェントOpen-weight modelsモデルと学習+2
51:06EN
20VC with Harry Stebbings· 2025年7月18日ポッドキャスト · 2 人Scott Wu on Cognition's Windsurf Deal and the Future of AI Codingゲスト Scott Wu · ホスト Harry StebbingsThe Cognition CEO explains the fast-moving acquisition, the role of AI agents in engineering, and his predictions for software development.コーディングエージェントSoftware engineeringAcquisition Strategy+1
1:02:54EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年7月17日ポッドキャスト · 2 人Enterprise Coding Tools Flipped the 80/20. Reflection AI Built the Other Halfホスト Sarah Guo, Elad GilFormer Gemini reward-model lead Misha Laskin on Asimov, why comprehension beats generation, and why there is no such thing as generalization—only pulling the test distribution into train.コーディングエージェントエージェント工学評価(Evals)+3
38:10EN
Cognitive Revolution "How AI Changes Everything"· 2025年7月16日ポッドキャストPlus Five for Format, Plus Ten if It Compilesホスト Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.モデルと学習ファインチューニングPost-training+3
スライド 7 枚19:40EN
Matthew Berman· 2025年7月10日Grok 4: Benchmark Results, Multi-Agent Reasoning and the Launch DemosMatthew Berman, Elon Musk · Forward FutureMatthew Berman examines xAI’s July 2025 model launch, including its reported reasoning scores, Heavy interface, demonstrations and pricing.Foundation ModelsBenchmarksReinforcement Learning+2
スライド 8 枚15:55EN
Matthew Berman· 2025年5月28日AI Shutdown Resistance and Blackmail in Controlled Safety TestsMatthew Berman · Forward FutureMatthew Berman examines Palisade’s shutdown experiment, Apollo’s simulated scheming evaluations and Anthropic’s Claude 4 system card.AI SafetyAI AlignmentReward hacking+2
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
スライド 7 枚15:13EN
Matthew Berman· 2025年5月10日Absolute 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
38:10EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年5月1日ポッドキャスト · 2 人o3 should wait exactly as long as the answer takesホスト Sarah Guo, Elad GilOpenAI researchers Eric Mitchell and Brandon McKinzie tell No Priors why o3's leap is a chain of tools during reasoning — and why you should send the same prompt a hundred times.モデルと学習Reasoning & test-time computeReinforcement Learning+3
30:46EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年4月24日ポッドキャスト · 2 人Deep Research Turned Web Browsing into a Trainable Problemホスト Sarah Guo, Elad GilOpenAI researcher Isa Fulford explains why a side project on read-only synthesis became an agent, when reinforcement fine-tuning is worth it, and why models will hack the search engine you gave them.エージェントエージェント工学Reinforcement Learning+3
1:14:11EN
Cognitive Revolution "How AI Changes Everything"· 2025年4月5日ポッドキャストThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGIホスト 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.モデルと学習Reasoning & test-time computeReinforcement Learning+3
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
スライド 4 枚12:30EN
Matthew Berman· 2025年3月7日QwQ-32B: A Smaller Reasoning Model and Its TradeoffsMatthew Berman · Forward FutureMatthew Berman examines Qwen’s reinforcement-learning recipe, tests a coding task, and compares benchmark scores and hardware requirements with DeepSeek-R1.Reasoning & test-time computeReinforcement LearningOpen Source+2
32:46EN
Sequoia Capital· 2025年2月25日ポッドキャストDon't draw the flowchart: OpenAI's Deep Research team on training agents end to endIsa Fulford and Josh Tobin explain why Deep Research was trained with reinforcement learning on hard browsing tasks instead of a human-written graph of model calls.エージェントエージェント工学Reinforcement Learning+3
スライド 2 枚17:20EN
Matthew Berman· 2025年2月23日Vibe Coding a Login Page and an AI Tetris PlayerMatthew Berman · Forward FutureMatthew Berman experiments with AI-written code, iterates through failures and trains a basic game-playing agent.Vibe CodingコーディングエージェントReinforcement Learning+1
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
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
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 3 枚6:50EN
Matthew Berman· 2025年2月12日DeepScaleR: A Small Reasoning Model Tested on Competition MathMatthew Berman · Forward FutureMatthew Berman reviews a 1.5B model’s reported benchmark results and runs a quantized version locally on one AIME problem.Reinforcement LearningBenchmarksLocal inference+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
2:16:17EN
Cognitive Revolution "How AI Changes Everything"· 2025年2月5日ポッドキャストA 10B Model Trained Across Three Continents — and an H200 for $1.49 an Hourホスト Nathan LabenzPrime Intellect's founders lay out a four-step plan to turn spare global GPUs into collectively owned intelligence, and argue that reinforcement learning just made their job easier.インフラとデプロイDistributed training & inferenceAI cost & performance+3
スライド 5 枚10:01EN
Matthew Berman· 2025年2月5日DeepSeek R1 Code Optimization and a $3 Visual Counting ExperimentMatthew Berman · Forward FutureMatthew Berman examines a human-guided coding improvement and a small reinforcement-learning experiment, then considers their implications for AI research.Reinforcement LearningGPU programming & kernelsAI research+2
2:49:35ZH
Zhang Xiaojun Podcast· 2025年2月4日ポッドキャスト · 2 人潘嘉怡解读 R1、o1 与 Kimi K1.5:强化学习如何教模型学会推理ゲスト 潘嘉怡 · ホスト 张小珺伯克利博士生潘嘉怡带读 DeepSeek、OpenAI 与 Kimi 的推理模型报告,拆解长思维链、GRPO、奖励设计与蒸馏。R1-Zero 的“顿悟时刻”展示了推理策略如何在强化学习中涌现。Reinforcement Learningモデルと学習AI research+2
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
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