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57:41EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 2월 25일팟캐스트 · 2명A Hundred Million Cells, Four People, Three Days: Biology's ImageNet Moment진행자 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.모델과 학습AI for scienceData quality & curation+3
1:18:09EN
20VC with Harry Stebbings· 2025년 2월 24일팟캐스트 · 2명Steeve Morin on AI Inference, Chips and the Cost of Switching Hardware게스트 Steeve Morin · 진행자 Harry StebbingsThe ZML founder explains the difference between training and production inference and offers a competitive view of the AI hardware market.인프라와 배포InferenceAI cost & performance+2
1:25:52EN
20VC with Harry Stebbings· 2025년 2월 17일팟캐스트 · 2명Jonathan Ross on Groq, Inference Economics and the AI Infrastructure Race게스트 Jonathan Ross · 진행자 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.인프라와 배포모델과 학습AI cost & performance+2
슬라이드 5장15:33EN
Matthew Berman· 2025년 2월 13일Latent 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 compute모델과 학습AI research+2
22:32EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 2월 7일팟캐스트 · 2명The $5.5 Million Was Only the Last Mile진행자 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.모델과 학습AI StrategyAI cost & performance+3
슬라이드 5장12:07EN
Matthew Berman· 2025년 2월 2일TinyZero: 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
슬라이드 6장24:22EN
Matthew Berman· 2025년 1월 31일Dario 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.모델과 학습AI cost & performanceGeopolitics+2
1:50EN
Matthew Berman· 2025년 1월 30일Five AI Trends Discussed by Coveo’s Sébastien PaquetMatthew Berman · Forward FutureMatthew Berman summarizes a 2025 interview on production systems, smaller models, agents and trust.엔터프라이즈 AI에이전트모델과 학습+1
슬라이드 4장20:34EN
Matthew Berman· 2025년 1월 28일DeepSeek 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.모델과 학습AI cost & performanceOpen-weight models+2
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
1:40:22EN
Cognitive Revolution "How AI Changes Everything"· 2025년 1월 22일팟캐스트Twenty-atom crystals taught an AI the physics of a heartbeat진행자 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.모델과 학습AI for science머신러닝 기초+3
슬라이드 3장16:25EN
Matthew Berman· 2025년 1월 18일Why 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 research모델과 학습AI cost & performance+1
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
슬라이드 2장30:29EN
Matthew Berman· 2025년 1월 3일How 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 Learning모델과 학습AI research+1
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
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 7장27:54EN
Matthew Berman· 2024년 12월 5일AI 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"· 2024년 11월 21일팟캐스트A Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Base진행자 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.모델과 학습Post-trainingData quality & curation+3
슬라이드 7장14:50EN
Matthew Berman· 2024년 11월 19일AI 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
슬라이드 4장14:05EN
Matthew Berman· 2024년 11월 15일Test-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· 2024년 11월 11일팟캐스트 · 2명Dario Amodei Has Seen This Scaling Movie Enough Times게스트 Dario Amodei · 진행자 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.모델과 학습안전과 거버넌스Scaling Laws+3
0:35EN
Matthew Berman· 2024년 11월 6일An 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.모델과 학습AI research
슬라이드 3장11:56EN
Matthew Berman· 2024년 11월 3일IBM 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.모델과 학습엔터프라이즈 AI파인튜닝+2
2:24:18EN
Cognitive Revolution "How AI Changes Everything"· 2024년 10월 30일팟캐스트MMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.진행자 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.모델과 학습Local inference평가(Evals)+3
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