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1:32:40ENThe Historian Who Taught GPT-3.5 to Beat GPT-4Moderation: 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.
8:26ENInstalling LLaVA on a GPU Server: Matthew Berman’s 2023 WalkthroughMatthew Berman · Matthew BermanA historical walkthrough of a LLaVA setup on RunPod, from GPU configuration to image-questioning examples and an observed repetition error.
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.Moderation: 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.
55:08ENOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2Moderation: 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.Moderation: Nathan LabenzNathan Labenz updates the Cognitive Revolution scouting report: jailbreaks, resume automation, Med-PaLM’s 40% radiology win, and what might follow Transformers.
1:32:22ENA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer AwayModeration: 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 SawModeration: 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 SchoolModeration: 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 RobotsModeration: 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.Moderation: 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.
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。
1:23:18ENFrom GPT-2 to GPT-3 Moments in Robotics: Six DeepMind Papers on Bridging Robots and the InternetModeration: 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 ScaleModeration: 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 CapModeration: 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 PixelsModeration: 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 RecognitionModeration: 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 3Moderation: 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.
1:02:57ENVirtual Biopsies in One Second: How a Seven-Year-Old CycleGAN Is Beating Newer Models in the ClinicModeration: 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.
45:46ENGiving AI a Nervous System: Voice, Touch, and the Post-Keyboard InterfaceModeration: Jacob EffronLiveKit's Russ explains how voice AI, multimodal chat, cloud browsers, and on-device models are reshaping how humans work with machines.
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