絞り込み結果
49 件並び順
1:29:02ENFrom 50 Designs a Day to 1,500: How AI Changed What Jaguar Land Rover Can Exploreホスト Nathan LabenzNeural Concept’s Thomas von Tschammer on specialist physics models, engineering copilots, and why Formula 1 already runs thousands of designs overnight.
44:52ENAn 80GB Wikipedia Cache Versus a 100GB Model That Holds the InternetEngram’s founders argue that stuffing a company into the context window is the expensive kind of memory—and that baking it into the weights can cut token use by two orders of magnitude.
1:48:43ENThe reward jumped from 3 to 180. Every Hacker News title was a Google layoff.ホスト Nathan LabenzCoreWeave's Kyle Corbitt walks Nathan Labenz through GRPO, rubrics, RL environments, and why most reward hacks are easy to catch once you know to look.
2:25:10ENFine-Tuning Isn't Dead — But It Turned a Helpful Model Evilホスト Nathan LabenzNathan Labenz on why prompting replaced fine-tuning for most work, what emergent misalignment actually showed, and how he is thinking about jobs, UBI, and AGI.
1:32:38ENPolish Is 1% of Frontier Training Data, So Poland Built Its Own Modelホスト Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.
1:55:12EN28,000 Documents, a 3B Model, 22 People: The Technical Ledger of Catholic AIホスト Nathan LabenzLongbeard CEO Matthew Harvey Sanders tells The Cognitive Revolution how a mission-driven team is aligning models to the Catholic magisterium — and why fine-tuning was not enough.
50:00ENCrosby Bills by the Document, Not the Hour, and Turns Contracts Around in Under an HourThe founders of an AI-native law firm explain why they became lawyers with engineers at the next desk instead of shipping legal software, and how deal velocity became the product.
38:10ENPlus 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.
1:28:48ENDoctors code at 45%. The model beat them by 12 points — then 4,000 physicians had to try it once.ホスト Nathan LabenzAmbience Healthcare and Cleveland Clinic walk through the AI scribe that cut pajama time, climbed ICD-10 with reinforcement fine-tuning, and won 75% voluntary use.
47:07ENGoodfire’s Eric Ho thinks we will decode neural nets by 2028On Sequoia’s Training Data, the interpretability founder argues that sparse autoencoders, model editing, and a third-party lab are how AI stops being grown from data and starts being designed.
1:42:06ENEvery Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It Goesホスト Nathan LabenzLabelbox CEO Manu Sharma on the $15 billion Scale deal, RL “gyms,” $250,000 trainers, and why post-training now eats the budget that used to belong to pre-training.
47:00ENAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a Decadeホスト Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.
41:52ENMercor Hit $100 Million ARR Teaching Models to Hire. Humans Now Write the Exams.ホスト Sarah Guo, Elad GilBrendan Foody tells Sarah Guo and Elad Gil how Mercor automated recruiting for frontier labs — and why building evals may become the world's most common knowledge job.
3:53:49ENWith One-Twelfth the Data, an Open-Source Vision Model Beat GPT-4oホスト Nathan LabenzWill Hardman of Veratai walks Nathan Labenz through two years of vision-language models, from ViT and CLIP to InternVL, BLINK, and Transfusion.
55:50ENThere will not be one model. Fireworks is betting on hundreds of small expertsホスト Jacob EffronPyTorch veteran Lin Qiao argues that inference is a compound system hyperscalers cannot buy their way through, and that the products with real traction still keep a human in the loop.
1:24:48ENCanva's AI Features Have Been Used 10 Billion Times. The Playbook Is Not a Chat Boxホスト Nathan LabenzJohn Milinovich, Canva's generative-AI product lead, walks Nathan Labenz through automation versus augmentation, concept-level interfaces, evals without a design arena, and why application-layer companies still have a decade of room.
1:04:54ENHe bet company equity that Transformer would last — then drew an AI map for 12,000 companiesホスト Jacob EffronDatabricks chief AI scientist Jonathan Frankle tells enterprises when to prompt, fine-tune, or pre-train, and why he still refuses to forecast the next architecture.
38:28ENHalf of Production AI Is RAG. Fine-Tuning Almost Disappeared.ホスト Sarah Guo, Elad GilBraintrust CEO Ankur Goyal tells No Priors what Notion, Zapier, and Vercel actually ship — and which hyped techniques they quietly dropped.
1:09:09EN30 Percent on SWE-bench: Cosine Taught Genie How Engineers Actually Thinkホスト Nathan LabenzAlistair Pullen tells Latent Space how Cosine reconstructed human problem-solving, fine-tuned on billions of tokens, and took the top score on software engineering’s hardest public test.
1:51:00ENStart With Ten Gold Examples: A Three-Step Playbook for AI Automationホスト Nathan LabenzThe day after OpenAI opened GPT-4o fine-tuning, Nathan Labenz unpacks a three-year method for choosing work, extracting tacit judgment, and raising model performance.
39:18ENOpen and Closed Will Tie at the Same Size: PyTorch's Former Lead on Fast InferenceA six-month library swap at Meta became a five-year rebuild. Lin Qiao wants the rest of the industry to get that stack in five weeks.
36:20ENA Hundred Million Tokens in Context Would Cost $10 Million. Tengyu Ma Runs the RAG Mathホスト Sarah Guo, Elad GilThe Stanford professor and Voyage AI founder argues that retrieval, not a giant context window, is how enterprises should manage private knowledge.
1:10:07ENSame Intelligence, 1% of the Price: Logan Kilpatrick on Gemini Flashホスト Nathan LabenzA former OpenAI developer-relations lead, now the product manager for Google AI Studio, walks through what Gemini 1.5 Flash actually changes for builders.
1:42:17ENThe Cheapest Inference Provider Is the Most Dangerousホスト Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.
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