絞り込み結果
38 件並び順
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.
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.
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.
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.
1:53:14ENWorth Thousands, Priced at $20: Zvi Mowshowitz on OpenAI's Flurry, Gemini Bets, Deepfakes and a Compute Capホスト 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.
1:45:55ENFrom Static Structures to Moving Molecules: Where AI for Protein Design Stands After AlphaFold 3ホスト 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 Clinicホスト 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.
2:02:13ENWiring Without a Vision: What OpenAI's and Google's Dueling Launches Really Signalホスト Nathan LabenzZvi Mowshowitz and Nathan Labenz unpack the week of GPT-4o and Google I/O, from rushed demos and AI companions to the big tech singularity and the exodus from OpenAI's safety team.
1:48:13ENRobotics Hits Its GPT-3.5 Moment: Inside Gemini Robotics, From Embodied Reasoning to Origamiホスト Nathan LabenzGoogle DeepMind researchers Keerthana Gopalakrishnan and Ted Xiao explain how Gemini Robotics works, what it can and cannot do, and where the field goes next.
1:17:31ENImage AI Is Still at the GPT-2 Stage: Suhail Doshi on Building a Unified Vision Modelホスト Nathan LabenzPlayground AI's founder explains why image generation has barely begun to deliver real utility, and why his team is betting 2024 on editing and a single model that can create, edit and understand pixels.
1:22:17ENOne Set of Weights, Fourteen Medical Tasks: Inside Google's Multimodal Med-PaLM Mホスト 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.









