필터 결과
100개정렬
1:36:39ENOpenAI’s $5 Million Bet: A 60-Person Team Wants GPT-4V to Give Cars Common Sense진행자 Nathan LabenzGhost Autonomy cofounder John Hayes tells Cognitive Revolution why a multimodal model on the driving path could reset self-driving after two decades of DARPA-era stacks.
1:14:48ENDevDay Probably Created Thousands of Startups, Says OpenAI's Logan Kilpatrick진행자 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-4진행자 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.
2:17:30ENGemini Became the Daily Driver. A GPT-5 Worthy of the Name Would Make Agents Work.진행자 Nathan LabenzZvi Mowshowitz returns to Cognitive Revolution to unpack Gemini 1.5, Sora, superalignment, sleeper agents, and the live-player map of frontier labs.
1:04:38ENHundreds of Miles Apart, Zuckerberg and Fridman Sat Close Enough to See the Wrinkles게스트 Mark Zuckerberg · 진행자 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 2진행자 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.진행자 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 Corgi진행자 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 Away진행자 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 Saw진행자 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 School진행자 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.
1:29:31EN130,000 Teleop Demos: Google's GPT Playbook for Robots진행자 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.진행자 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 brain진행자 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.
2:36:21ENA Smile Is Not Happiness: Rana el Kaliouby on Emotion AI and the Empathy Crisis게스트 Rana el Kaliouby · 진행자 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 Step게스트 Ishan Misra · 진행자 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 Problem게스트 Rosalind Picard · 진행자 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 Internet진행자 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 Scale진행자 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.
1:53:14ENWorth Thousands, Priced at $20: Zvi Mowshowitz on OpenAI's Flurry, Gemini Bets, Deepfakes and a Compute Cap진행자 Nathan LabenzZvi Mowshowitz and Nathan Labenz survey OpenAI's launch spree, AI pricing and bundling, data pollution, deepfakes, the reversal curse, calls to cap training compute, and Amazon's Anthropic deal.
1:03:24ENWhy Sora Bet on People, Not Just Pixels진행자 Jacob EffronThe Sora team explains how a video model became a social creation network, what cameos revealed about human creativity, and why the next frontier is world simulation.
1:31:52ENFrom Phonemes to CTC: How Deep Learning Rebuilt Speech Recognition진행자 Lex FridmanBaidu's Adam Coates walks through the traditional speech pipeline, the CTC-based end-to-end engine that replaced much of it, and what it takes to scale and deploy it.
1:45:55ENFrom Static Structures to Moving Molecules: Where AI for Protein Design Stands After AlphaFold 3진행자 Nathan LabenzComputational biochemist Amelie Schreiber walks Nathan Labenz through six months of progress in structure prediction, molecular dynamics, peptide and antibody models, and early agent-driven design workflows.
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