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132 件並び順
1:14:48ENDevDay Probably Created Thousands of Startups, Says OpenAI's Logan Kilpatrickホスト Nathan LabenzThe day after OpenAI's first developer conference, Dev Relations lead Logan Kilpatrick tells Nathan Labenz why thin wrappers were never companies, why agents were deliberately not shipped fully autonomous, and what $2 million custom models are for.
1:32:40ENThe Historian Who Taught GPT-3.5 to Beat GPT-4ホスト Nathan LabenzMark Humphries on a kilometer of unread paper, fur-trade jargon that embeddings cannot see, and why cheap processing will force history to be rewritten.
9:21ENDALL·E 3 in Bing and ChatGPT: A 2023 WalkthroughMatthew Berman · Forward FutureMatthew Berman tries DALL·E 3 in two interfaces and compares logo lettering and image styles with Leonardo.ai, documenting both promising results and service delays.
18:23ENEarly ChatGPT Vision Examples: From Diagrams to Code and Visual MistakesMatthew Berman · Forward FutureMatthew Berman surveys 2023 image-input demonstrations, including education, design and programming, and examines examples that were not verified or got details wrong.
2:17:30ENGemini Became the Daily Driver. A GPT-5 Worthy of the Name Would Make Agents Work.ホスト Nathan LabenzZvi Mowshowitz returns to Cognitive Revolution to unpack Gemini 1.5, Sora, superalignment, sleeper agents, and the live-player map of frontier labs.
20:26ENAI News in September 2023: ChatGPT Vision, Meta AI and Mistral 7BMatthew Berman · Forward FutureMatthew Berman reviews a busy week of AI launches, robotics demos and investment announcements, along with emerging questions about privacy and security.
1:04:38ENHundreds of Miles Apart, Zuckerberg and Fridman Sat Close Enough to See the Wrinklesゲスト Mark Zuckerberg · ホスト Lex FridmanLex Fridman sat in a dark room in Austin wearing a Quest headset; Mark Zuckerberg sat in another conference room, also wearing one, and it did not feel like a video call.
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.
39:02ENAI’s product advantage is consistency. Its real weakness is getting conned.ホスト Nathan LabenzNathan Labenz updates the Cognitive Revolution scouting report: jailbreaks, resume automation, Med-PaLM’s 40% radiology win, and what might follow Transformers.
39:50ENWhy AI Video Is Still a Four-Second Corgiホスト Sarah Guo, Elad GilMeta research director Devi Parikh explains why text-to-video has stalled on short animated images, and what it will take to get past that plateau.
1:32:22ENA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer Awayホスト Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.
1:18:18ENFifteen Thousand Brain Numbers Were Enough to Reconstruct What Someone Sawホスト 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.
5:24ENGoogle I/O 2023: Bard, PaLM 2 and AI Across Google's ProductsMatthew Berman · Forward FutureMatthew Berman reviews Google's 2023 AI announcements and weighs its focus on improving established products.
7:53ENHuggingGPT: Using a Language Model to Coordinate AI ToolsMatthew Berman · Forward FutureMatthew Berman explains a research system that routes multimodal tasks to specialist models using a language model as controller.
1:29:31EN130,000 Teleop Demos: Google's GPT Playbook for Robotsホスト Nathan LabenzKeerthana Gopalakrishnan of Google Robotics tells Nathan Labenz how RT-1, SayCan, and PaLM-E turn language-model scaling into kitchen-ready mobile manipulators.
1:21:20ENFreeze the eyes and the brain. Train only the optic nerve.ホスト Nathan LabenzJunnan Li and Dongxu Li tell Nathan Labenz how BLIP cleaned noisy web captions, and how BLIP-2 connected a frozen vision model to a frozen language model in under ten days on one A100.
45:03ENEmad Mostaque’s bet: open small models, not one giant brainホスト Sarah Guo, Elad GilStability AI’s founder tells No Priors why foundation models should be public infrastructure, why small models can beat giant ones, and what that means for media, medicine, and AGI.
1:03:29ZH多模态论文串讲:BLIP、CoCa 与 BEiT-3从图文数据清理到统一预训练,梳理三种视觉语言模型的结构、目标函数与相互联系。
2:36:21ENA Smile Is Not Happiness: Rana el Kaliouby on Emotion AI and the Empathy Crisisゲスト Rana el Kaliouby · ホスト Lex FridmanThe Affectiva founder and Smart Eye deputy CEO talks with Lex Fridman about teaching machines to read faces, sensing drivers inside the cabin, and why she is proudest of the people her company grew.
2:30:30ENThe Fallacy of the Successful First Stepゲスト Ishan Misra · ホスト Lex FridmanBerkeley's Jitendra Malik explains why computer vision still looks easy, why fully autonomous driving is not near, and what children know that today's networks do not.
1:00:11ENRosalind Picard: Clippy Never Saw You Frown, and That's the Whole Problemゲスト Rosalind Picard · ホスト Lex FridmanThe MIT professor who named affective computing on emotion recognition, privacy, seizure-detecting wearables, and why a device you can take off is safer than a camera you cannot see.
1:23:18ENFrom GPT-2 to GPT-3 Moments in Robotics: Six DeepMind Papers on Bridging Robots and the Internetホスト Nathan LabenzKeerthana Gopalakrishnan and Ted Xiao walk through RT-2, RT-X, RT-Trajectory, AutoRT, Learning to Learn Faster and PIVOT, and say what is still missing for general-purpose robots.
53:02ENAdobe's Firefly Bet: A Billion Beta Images, Creative Control, and Trust at Scaleホスト Jacob EffronAdobe's Alexandre explains how Firefly moved from research to product, how Adobe balances creative control with generative speed, and why trust and feedback loops are central to its AI strategy.
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