絞り込み結果
198 件並び順
32:48ENFourteen People, $200 Million of Compute, and the Reliability Bottleneck for Agentsホスト Sarah Guo, Elad GilImbue co-founders Kanjun Qiu and Josh Albrecht on why agents fail, why language models cannot learn general algorithms, and why they are spending a giant round on GPUs rather than headcount.
1:23:10ENLuxury Purses Under $500 Beat a Bank's Fraud Modelホスト Nathan LabenzArthur.ai CEO Adam Wenchel on boundary attacks, LLM firewalls, and why a model that eats raw text cannot be secured from the inside.
55:08ENOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2ホスト Nathan LabenzAt Google I/O in May 2023, PaLM 2 arrived already running in more than two dozen products — and its lead product manager walks through what that actually took.
39:45ENPalantir's 13th Employee: The Model Is a Stochastic Genie. Ontology Is the Stake That Tethers Itホスト Sarah Guo, Elad GilCTO Shyam Sankar on 550 microservices, air-gapped submarines, and why Palantir's ontology is what large language models were waiting for.
12:20ENTesting UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.
54:00ENDouwe Kiela on Data, Retrieval and Enterprise AIゲスト Douwe Kiela · ホスト Harry StebbingsThe Contextual AI CEO discusses language model trade-offs, evaluation, open models and the hurdles to enterprise use.
18:08ENSix Language Models Compared on an Informal 2023 Prompt SetMatthew Berman · Forward FutureMatthew Berman tests coding, writing and reasoning with H2O GPT, finding a tie under a small and subjective rubric.
10:23ENTesting Hosted Falcon 40B and Document Chat in h2oGPTMatthew Berman · Forward FutureMatthew Berman tries H2O.ai’s 2023 hosted Falcon 40B interface on coding, writing and reasoning prompts, then demonstrates document chat.
11:49ENTesting Wizard-Vicuna 30B: Responses, Reasoning, and CodeMatthew Berman · Forward FutureMatthew Berman examines a model marketed as uncensored, demonstrates a 2023 cloud setup, and runs an informal collection of tests.
1:33:51ENGPT-4 still writes SQL that will not run. She put a guardrail on the modelホスト Nathan LabenzShreya Rajpal built Guardrails AI so developers can encode what “correct” means, validate the pieces, and send failures back to the model instead of hoping the prompt holds.
1:25:35ENTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical Schoolホスト Nathan LabenzVivek Natarajan walks Nathan Labenz through Med-PaLM and Med-PaLM 2: the data-light techniques, the nine-axis clinician evals, and why a high USMLE-style score is the wrong trophy.
42:18ENTen Percent of Search Is Health — and the Doctor Is Googling Tooホスト Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.
1:28:43ENClinical Guidelines Are 11% Evidence. Curai’s Founder Still Wants GPT-4 in the Exam Roomホスト Nathan LabenzNeal Khosla of Curai Health argues that large language models already match median U.S. physicians on today’s tests — and that the real work is scaffolding, supervision, and a doctor’s signature.
58:24ENAmong 100 Random Doctors, Zak Kohane Would Be Surprised If One Beat GPT-4ホスト Nathan LabenzHarvard's biomedical informatics chair on a rare newborn case, why drug-style trials will not certify a general medical model, and why the AI should still work for the patient.
1:21:06ENEveryone Talks About RLHF. Almost Nobody Is Doing It.ホスト Nathan LabenzHumanloop CEO Raza Habib on why an LLM API is maybe five percent of a working product, and what actually fills the rest.
1:56:16ENThey Used Humans as Stand-In AIs. Now 250,000 People Run Elicit.ホスト Nathan LabenzOught's founders tell Nathan Labenz why open-ended reasoning has to be decomposed, supervised, and grounded—not scaled until it merely looks persuasive.
1:23:53ENGPT-4, 24 Hours Later: No Safety Locks, a Captcha Hire, and a U.S.–China Deadlockホスト Nathan LabenzA day after OpenAI shipped GPT-4, ChinaTalk sat a red teamer, a pessimist, and a policy researcher down to argue over capability, safety, and a race nobody can brake.
1:13:06ENLockheed Martin's CTO on Autonomy, the Moon, and the Culture of Not Getting It Wrongゲスト Keoki Jackson · ホスト Lex FridmanKeoki Jackson tells Lex Fridman why going back to the Moon is the easy part—and why proving a learning system will still do the right thing is not.
1:45:49ENTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the Companyホスト Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.
1:25:13ENCodex Is Not Just a Coding Tool: OpenAI's Plan for an Agent That Behaves Like a Teammateホスト Lenny RachitskyCodex product lead Alexander Embiricos explains the 20x growth, why every useful agent may need to write code, and why human review speed is the real bottleneck.
1:33:50ENEvals Are the New PRDs: Inside Anthropic's Research Product Teamホスト Lenny RachitskyDianne Penn, Anthropic's head of product for research and Labs, explains how the company found its identity, how her team turns user feedback into evals, and why human judgment still matters.
1:31:41ENThe Worst Model You'll Ever Use: Kevin Weil on Building Products at OpenAI's Paceゲスト Kevin Weil · ホスト Lenny RachitskyOpenAI's chief product officer explains how evals, light planning, fine-tuned model ensembles and a small PM team shape the way the company ships.
1:22:36ENStop Chasing AI News: Chip Huyen on What Actually Makes AI Products Betterゲスト Chip Huyen · ホスト Lenny RachitskyAuthor of AI Engineering walks through pre-training, post-training, RL, evals and RAG, then explains what separates companies that get value from AI from those that stall.
1:07:08ENIf the Model Is the Product, the Eval Is the PRD: Inside Mercor's Riseゲスト Brendan Foody · ホスト Lenny RachitskyMercor CEO Brendan Foody explains why AI labs will pay almost anything for expert-written evals, how his company grew from $1 million to a $400 million run rate in 16 months, and what that means for jobs and founders.
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