最近更新されたチャンネル

選ぶとそのチャンネルだけを表示すべてのチャンネル →

絞り込み結果

380 件
並び順
スライド0:34EN
Matthew Berman· 2025年5月1日DeepSeek-Prover-V2 and Its Formal Math ResultsMatthew Berman · Forward FutureMatthew Berman highlights DeepSeek’s reported theorem-proving results on miniF2F, PutnamBench and formalized AIME problems.MathematicsAI researchBenchmarks+1
3:08:35EN
Cognitive Revolution "How AI Changes Everything"· 2025年4月21日ポッドキャストo3 isn't AGI. Zvi Mowshowitz still put p(doom) at 70 percentホスト Nathan LabenzOn his ninth visit to The Cognitive Revolution, Zvi Mowshowitz argues that OpenAI's o3 is a tool-use leap rather than AGI, then explains why he raised the probability of AI catastrophe anyway.セキュリティと安全性AGI(汎用人工知能)Existential Risk+3
2:29:41ZH
Zhang Xiaojun Podcast· 2025年4月7日ポッドキャスト · 4 人陈建宇:机器人基座模型如何走向通用 VLAゲスト 陈建宇 · ホスト 张小珺从 SayCan、RT-2 到 Diffusion Policy,清华助理教授、星动纪元创始人陈建宇梳理机器人模型从借用语言模型到端到端 VLA 的路线。关键难题仍是多样化数据、跨本体泛化和高频动作控制。Roboticsモデルと学習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
1:30:02EN
Cognitive Revolution "How AI Changes Everything"· 2025年4月2日ポッドキャストWhen o1 Started Losing at Chess, It Rewrote the Board Fileホスト Nathan LabenzJeffrey Ladish of Palisade Research on reward hacking by reasoning models, two paths to losing control of AI, and why honesty and relentless problem-solving pull in opposite directions.セキュリティと安全性Reward hacking安全性とガバナンス+3
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
スライド 6 枚11:31EN
Matthew Berman· 2025年3月29日AI News in March 2025: GPT-4o, Gemini, DeepSeek and MCPMatthew Berman · Forward FutureMatthew Berman reviews model updates, the ARC-AGI-2 benchmark, image generators and early adoption of Model Context Protocol.Foundation ModelsBenchmarksMCP+2
スライド 6 枚8:35EN
Matthew Berman· 2025年3月21日AI News: Vibe Jam, Mistral Small 3.1 and New Audio ModelsMatthew Berman · Forward FutureMatthew Berman surveys March 2025 releases, from AI-assisted games and local models to search, audio and creative tools.Foundation ModelsVibe Coding音声 AI+2
1:10:23EN
Unsupervised Learning: With Jacob Effron· 2025年3月17日ポッドキャストA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still Untappedホスト 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.モデルと学習Reasoning & test-time computeScaling Laws+3
スライド 6 枚12:10EN
Matthew Berman· 2025年3月6日Mercury Coder and Diffusion Language Models: Speed, Demos and LimitsMatthew Berman · Forward FutureMatthew Berman explores Inception’s 2025 coding model, comparing iterative text refinement with autoregressive generation and examining speed claims.モデルと学習Inferenceコーディングエージェント+2
1:31:35EN
Cognitive Revolution "How AI Changes Everything"· 2025年3月6日ポッドキャストThirty Years of Research, Collapsed into One Sentenceホスト Nathan LabenzImmunologist Derya Unutmaz on writing a medical patent in half an hour, treating o1 Pro as a professor-level collaborator, and why he no longer trusts his own expertise until he asks the model.AI for scienceAI in healthcareHuman-AI Collaboration+3
スライド 5 枚7:59EN
Matthew Berman· 2025年2月25日Grok 3’s Brief Misinformation Filter and the Question of AI BiasMatthew Berman · Forward FutureMatthew Berman examines an instruction that told Grok to disregard sources linking Elon Musk and Donald Trump to misinformation, and xAI’s explanation of how it reached users.AI Safety安全性とガバナンスAI research+1
2:36:13ZH
Zhang Xiaojun Podcast· 2025年2月24日ポッドキャスト · 2 人松琳解读 DeepSeek、Kimi 与 MiniMax 的注意力新论文ゲスト Songlin Yang(松琳) · ホスト 张小珺从动态稀疏注意力到线性注意力混合架构:三条长上下文路线如何兼顾训练、推理与硬件效率Transformers & attentionGPU programming & kernelsモデルと学習+2
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0:51EN
Matthew Berman· 2025年2月19日Mira Murati Announces Thinking Machines LabMatthew Berman · Forward FutureMatthew Berman reviews the new AI lab’s stated goals in its February 2025 announcement.AI startups & small teamsAI researchOpen-source ecosystems
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
45:31EN
Matthew Berman· 2025年2月15日Jerry Chow on Quantum Computing, Encryption, AI and Error CorrectionMatthew Berman, Jerry Chow · Forward FutureIBM’s quantum systems director explains qubits, cryogenic hardware, software and the work needed to make quantum computers useful.Quantum MechanicsCybersecurityAI research
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
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
スライド 4 枚20:34EN
Matthew Berman· 2025年1月28日DeepSeek R1 and the Debate Over AI Costs and ComputeMatthew Berman · Forward FutureMatthew Berman examines 2025 reactions to DeepSeek, from training efficiency and market losses to open weights, privacy and the possibility of a demand rebound.モデルと学習AI cost & performanceOpen-weight models+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
もっと見る380 件中 24 件を表示