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349 件
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57:41EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年2月25日ポッドキャスト · 2 人A Hundred Million Cells, Four People, Three Days: Biology's ImageNet Momentホスト Sarah Guo, Elad GilVevo Therapeutics and the Arc Institute open-sourced Tahoe-100M and a 230-million-cell atlas in the same week — and argued that virtual cells, not just protein folders, are the next language model of biology.モデルと学習AI for scienceData quality & curation+3
1:18:09EN
20VC with Harry Stebbings· 2025年2月24日ポッドキャスト · 2 人Steeve Morin on AI Inference, Chips and the Cost of Switching Hardwareゲスト Steeve Morin · ホスト Harry StebbingsThe ZML founder explains the difference between training and production inference and offers a competitive view of the AI hardware market.インフラとデプロイInferenceAI cost & performance+2
1:25:52EN
20VC with Harry Stebbings· 2025年2月17日ポッドキャスト · 2 人Jonathan Ross on Groq, Inference Economics and the AI Infrastructure Raceゲスト Jonathan Ross · ホスト Harry StebbingsThe Groq CEO explains his bet on inference chips, compares his company’s architecture with GPUs, and discusses the financing and power needed to scale.インフラとデプロイモデルと学習AI cost & performance+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
22:32EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年2月7日ポッドキャスト · 2 人The $5.5 Million Was Only the Last Mileホスト Sarah Guo, Elad GilOn No Priors' 100th episode, Sarah Guo and Elad Gil separate DeepSeek's paper cost from the compute behind it, then walk through OpenAI's Deep Research, Stargate, and their bets for 2025.モデルと学習AI StrategyAI cost & performance+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 6 枚24:22EN
Matthew Berman· 2025年1月31日Dario Amodei on DeepSeek, AI Costs and Chip Export ControlsMatthew Berman, Jonathan Ross, David Sacks · Forward FutureMatthew Berman examines the Anthropic CEO’s 2025 essay, the limited evidence behind distillation allegations and the economics of model training.モデルと学習AI cost & performanceGeopolitics+2
1:50EN
Matthew Berman· 2025年1月30日Five AI Trends Discussed by Coveo’s Sébastien PaquetMatthew Berman · Forward FutureMatthew Berman summarizes a 2025 interview on production systems, smaller models, agents and trust.エンタープライズ AIエージェントモデルと学習+1
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
1:48:20EN
Cognitive Revolution "How AI Changes Everything"· 2025年1月25日ポッドキャストFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the Tableホスト 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.モデルと学習Reinforcement LearningReasoning & test-time compute+3
1:40:22EN
Cognitive Revolution "How AI Changes Everything"· 2025年1月22日ポッドキャストTwenty-atom crystals taught an AI the physics of a heartbeatホスト Nathan LabenzOrbital Materials' Jonathan Godwin and Tim Duignan tell Nathan Labenz how a neural network trained on tiny crystals simulated a potassium ion channel on a single 2017 GPU.モデルと学習AI for science機械学習の基礎+3
スライド 3 枚16:25EN
Matthew Berman· 2025年1月18日Why Test-Time Compute Could Reshape AI InferenceMatthew Berman, Lisa Su, Jensen Huang · Forward FutureMatthew Berman explains how extra computation at inference can improve model outputs, and why hardware executives expect a much larger inference market.AI researchモデルと学習AI cost & performance+1
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 2 枚30:29EN
Matthew Berman· 2025年1月3日How Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.Reinforcement Learningモデルと学習AI research+1
1:07:32EN
Unsupervised Learning: With Jacob Effron· 2024年12月18日ポッドキャストo1 Is a New Generation — and the Day AGI Arrives Will Feel Boringホスト 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.モデルと学習Reasoning & test-time computeエージェント工学+3
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 7 枚27:54EN
Matthew Berman· 2024年12月5日AI News in December 2024: Genie 2, World Models, MCP and New AI ReleasesMatthew Berman · Forward FutureMatthew Berman surveys early interactive world models, voice and video tools, QwQ, distributed training and Amazon’s Nova announcements.World modelsOpen-weight modelsAI agents+1
1:49:41EN
Cognitive Revolution "How AI Changes Everything"· 2024年11月21日ポッドキャストA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Baseホスト Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.モデルと学習Post-trainingData quality & curation+3
スライド 7 枚14:50EN
Matthew Berman· 2024年11月19日AI News in November 2024: Mistral, Gemini, Nuclear Safety and Medical AIMatthew Berman · Forward FutureMatthew Berman surveys Le Chat updates, model rankings, nuclear safeguards and a diagnostic reasoning study.AI StrategyBenchmarksAI Safety+2
スライド 4 枚14:05EN
Matthew Berman· 2024年11月15日Test-Time Training Improves ARC Puzzle Results in an MIT StudyMatthew Berman · Forward FutureMatthew Berman explains a 2024 paper that adapts a language model to each puzzle at inference time and combines its predictions with program synthesis.AI researchReasoning & test-time computeBenchmarks+1
5:15:00EN
Lex Fridman· 2024年11月11日ポッドキャスト · 2 人Dario Amodei Has Seen This Scaling Movie Enough Timesゲスト Dario Amodei · ホスト Lex FridmanAnthropic’s CEO puts numbers on frontier training costs and AGI, then hands the mic to the people who shape Claude’s character and look inside its weights.モデルと学習安全性とガバナンスScaling Laws+3
0:35EN
Matthew Berman· 2024年11月6日An IBM Visit: Granite Models, InstructLab and Quantum ComputingMatthew Berman · Forward FutureMatthew Berman previews a longer discussion of IBM’s small AI models, model customization and quantum research.モデルと学習AI research
スライド 3 枚11:56EN
Matthew Berman· 2024年11月3日IBM Granite 3.0 and InstructLab: Small Models for Enterprise AIMatthew Berman · Forward FutureMatthew Berman reviews IBM's 2024 model family and its approach to adding domain knowledge. IBM partnered on the video and invited him to its event.モデルと学習エンタープライズ AIファインチューニング+2
2:24:18EN
Cognitive Revolution "How AI Changes Everything"· 2024年10月30日ポッドキャストMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.ホスト Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.モデルと学習Local inference評価(Evals)+3
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