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349 件並び順
57:41ENA 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.
1:18:09ENSteeve 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.
1:25:52ENJonathan 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.
15:33ENLatent 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.
22:32ENThe $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.
12:07ENTinyZero: 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.
24:22ENDario 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.
1:50ENFive AI Trends Discussed by Coveo’s Sébastien PaquetMatthew Berman · Forward FutureMatthew Berman summarizes a 2025 interview on production systems, smaller models, agents and trust.
20:34ENDeepSeek 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.
1:48:20ENFrom 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.
1:40:22ENTwenty-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.
16:25ENWhy 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.
2:04:45ENo3 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.
30:29ENHow 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.
1:07:32ENo1 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.
48:18ENNoam 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.
27:54ENAI 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.
1:49:41ENA 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.
14:50ENAI 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.
14:05ENTest-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.
5:15:00ENDario 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.
0:35ENAn 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.
11:56ENIBM 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.
2:24:18ENMMLU 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.
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