#具身智能

New: I spoke with @sundarpichai about Google's commitment to the AI frontier, how close we are to RSI, balancing DeepMind's compute needs vs. Google Cloud profits, and much, much more. Full Big Technology Podcast episode here --> Chapters: 0:00 Introduction 1:14 Google’s Frontier AI Strategy 3:47 Do Frontier Models Really Matter? 6:35 The Future of AI Agents 7:39 Recursive Self-Improvement 12:58 World Models and the Path to AGI 14:56 Google’s Compute Dilemma and AI Infrastructure 19:36 Is AI Overbuilt? 23:35 The Future of the AI Interface 31:42 Google’s Answer to Muse and Dots 35:03 Apple, Space Data Centers, and AI Coding 41:42 Optimism and AI’s Future

270M+ miles. 841 fewer injury-causing crashes. Our latest safety data shows the Waymo Driver continues to make roads safer for everyone. Compared to human drivers across 5 territories, it reduced: 📉 Injury crashes by 82% 📉 Serious injury crashes by 95% Full data: https://waymo.com/safety/impact

Figure just released this video. a beautiful robot future is indeed coming. Its Helix 2.5 model (Figure’s end-to-end robotics brain) lifted zero-shot household-task success more than sixfold across 30 unseen homes. Figure also reports success rising from 9% to 56%, with success requiring the entire task to be completed and no partial credit awarded.

We just hit 100M ARR within 10 months of starting deployments. We are in factory lines. On construction sites. In kitchens. In data centers. Cleaning. Welding. Building. Cooking. Deploying.

World Labs co-founders Fei-Fei Li, Justin Johnson, Ben Mildenhall, and a16z's Martin Casado on Atlas, a world model for spatial intelligence: LLMs are built on next token prediction. Video models are built on next frame prediction. Atlas is built on new view prediction, and it's the first model to unify pixel generation and pixel reconstruction, two problems computer vision has kept in separate tracks for half a century. The practical result is a 50 to 100x reduction in what it takes to digitally capture a 3D representation of a space. Previously, you needed 100 to 300 photos of a single room. Atlas can work from just three. In this conversation, they get into the slow motion shot from The Matrix that took hundreds of cameras and now takes three iPhones, the overnight Slack message that made them bet the company in five seconds, why robotics is bottlenecked on data rather than chips, and the case that new view prediction is AI-complete. 00:00 Intro 01:50 The Matrix slow motion scene now takes three iPhones 02:48 Why new view prediction is the primitive 07:10 Unifying generation and reconstruction 11:15 Gaussian splats became the bottleneck 14:17 Dense capture used to mean 300 photos 17:30 Why reconstruction needs generation to fill the gaps 18:44 The LLM lesson image models missed 23:39 The video that made them go all in 28:04 3D design is 95% revisions 30:50 The problem in robotics is data, not chips 32:48 Why a robot policy can't be trained like an image model 34:44 When the simulator becomes the planner 36:45 Frozen time required footage full of movement 40:57 Why new view prediction is AI-complete 42:43 Nature gave animals eyes but not trees YouTube: https://www.youtube.com/watch?v=qn1QDDBnTA0 @drfeifei @jcjohnss @BenMildenhall @theworldlabs @martin_casado

Chinese robots hold 86% of total global humanoid shipments and Nvidia is extending its CUDA playbook into that concentrated robotics market. WSJ published a peice China's concentration makes Nvidia's CUDA-style strategy easier to scale because Nvidia only needs to become deeply embedded in a handful of robot makers to sit underneath most of the market. The lesson Nvidia learned from CUDA was that selling the chip is much more powerful when developers also build their software around your platform. Nvidia is trying to create the robotics version of that dependency: Robot maker → Nvidia GPU/Jetson → CUDA → Isaac/GR00T/Cosmos → simulation, training, robot control and deployment