筛选结果
23 篇排序
1:28:08ENThe same H100 costs 3x at AWS, and a four-month-old lab is catching genes the top sequencers missed主持 Nathan LabenzA Cognitive Revolution weekly briefing on a possible U.S.–China AI hotline, why hyperscalers charge two to three times as much for identical chips, co-writing a novel with frontier models, and an AI loop that re-diagnosed a child a premier prenatal lab had cleared.
1:44:15ENNathan Labenz's p(doom) Is 10 to 90 Percent. He Is More Optimistic Anyway主持 Nathan LabenzFive years ago, expecting AGI by 2035 made you an extremist. On The Intelligence Horizon, the Cognitive Revolution host explains why the same forecast now sounds bearish, and why he still thinks a defense-in-depth strategy might keep society on the rails.
2:03:56EN260 Doctors and 49,000 Criteria: How OpenAI Built Attending-Level Health AI主持 Nathan LabenzKaran Singhal tells Nathan Labenz how ChatGPT Health was evaluated with physicians, walled off from model training, and aimed at raising both the floor and the ceiling of human health.
2:25:42EN → 简体中文专家们争到咖啡时间,也没有定论:AI 能不能造出 AI主持 Nathan Labenz在 Cognitive Revolution 的一场马拉松直播中,一位癌症 AI 研究者、一位前 OpenAI 董事会成员和一位国家安全分析师,各自撞上了这股热潮里一堵不同的墙。
1:04:51EN125 million cells took a decade. The next billion takes months主持 Nathan LabenzMark Zuckerberg and Priscilla Chan on pairing a frontier biology lab with a frontier AI lab so models train on data that was designed for them.
1:29:30EN2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New Antibiotics主持 Nathan LabenzMIT's Jim Collins tells Nathan Labenz how a bootlegged 2018 project, a binary growth-inhibition label, and Chemprop graph networks pulled novel antibiotics out of chemical space that labs could never screen by hand.
1:28:48ENDoctors code at 45%. The model beat them by 12 points — then 4,000 physicians had to try it once.主持 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.
1:26:21ENFor under $10, Google's AI independently recovered a lab result nobody had published主持 Nathan LabenzVivek Natarajan and Anil Palepu tell Nathan Labenz how AMIE beats primary-care doctors on treatment plans, and how Co-Scientist matched Imperial College's unpublished bacterial-resistance mechanism.
1:31:35ENThirty Years of Research, Collapsed into One Sentence主持 Nathan LabenzImmunologist Derya Unutmaz on writing a medical patent in half an hour, treating o1 Pro as a professor-level collaborator, and why he no longer trusts his own expertise until he asks the model.
1:33:01ENRare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already Missed主持 Nathan LabenzDr. Catherine Brownstein of Boston Children's Hospital explains why cheap genomes made her job harder, why data sharing still beats any model, and where ChatGPT Pro actually gives her hours back.
1:32:45ENHe doubled a company with AI — then asked the labs to pause hyperscaling主持 Nathan LabenzOn the Nick Halaris Show, Cognitive Revolution host Nathan Labenz argues for adopting today's models fast and slowing the race to systems more powerful than we are.
1:37:07ENA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.主持 Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.
1:01:55ENA $9 AI nurse, an 86 on the medical boards, and a 10% extinction line主持 Nathan LabenzNathan Labenz’s solo scouting report walks from medical state of the art through how models actually learn, then to live players, scaling trends, and a control plan that still does not exist.
1:59:15ENA Protein Backbone in a Minute: The Real Progress, Bottlenecks, and Risks of AI Biology主持 Nathan LabenzThe White House set a thousand-fold lower reporting bar for biological models than for language models—and this conversation shows why that gap already looks prescient.
3:04:52EN → 简体中文医生在九个维度中有八个更青睐 AI:一位 GPT-4 红队测试者的侦察报告主持 Nathan LabenzNathan Labenz 在 80,000 Hours 播客上讲述 2023 年末的系统已经能做什么、哪些门槛仍然重要,以及两极化的政策之争为何反而成了强硬监管的礼物。
39:02ENAI’s product advantage is consistency. Its real weakness is getting conned.主持 Nathan LabenzNathan Labenz updates the Cognitive Revolution scouting report: jailbreaks, resume automation, Med-PaLM’s 40% radiology win, and what might follow Transformers.
1:18:18ENFifteen Thousand Brain Numbers Were Enough to Reconstruct What Someone Saw主持 Nathan LabenzTanishq Mathew Abraham walks through MindEye, the fMRI-to-image system that maps a handful of voxel values onto CLIP and diffusion models.
1:25:35ENTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical School主持 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.
1:28:43ENClinical Guidelines Are 11% Evidence. Curai’s Founder Still Wants GPT-4 in the Exam Room主持 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主持 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.
1:02:57ENVirtual Biopsies in One Second: How a Seven-Year-Old CycleGAN Is Beating Newer Models in the Clinic主持 Nathan LabenzTanishq Mathew Abraham explains how deep learning turns label-free 3D tissue images into virtual H&E stains fast enough to guide tumor surgery.
2:04:21EN"AI Doctors Are Here": A Father's Account of Fighting His Son's Burkitt Leukemia With GPT-5 Pro and Gemini 3主持 Nathan LabenzNathan Labenz recounts the first three weeks of his 6-year-old son's cancer diagnosis and how frontier models served as second opinions, bedside co-pilots and relapse planners.
1:22:17ENOne Set of Weights, Fourteen Medical Tasks: Inside Google's Multimodal Med-PaLM M主持 Nathan LabenzVivek Natarajan and Tao Tu explain how Google built a single generalist biomedical model that reads images, genomics and text, and what it will take to put it in front of patients.









