Search: “physics simulation”

Introducing WeatherNext 3, our most advanced and accurate global weather AI model

Google DeepMind launches WeatherNext 3, an AI weather model delivering hourly 5-km forecasts from live satellite data, now integrated across Google products.

WeatherNext 3 ingests live geostationary satellite mosaics and station observations through a Functional Generative Network (FGN) mesh transformer, producing hourly forecasts at 5-km surface resolution versus WeatherNext 2's 25-km, 6-hour grid. Independent live evaluations by Brightband rate it the most accurate global weather model to date. It adds renewable-energy variables such as 100-meter turbine-height wind speeds and solar radiation, and is integrated across Search, Gemini, Maps, Google Maps Platform, and Cloud.

Google DeepMind·Sep 3, 15:02 UTC · 13d agoModel release

Google’s AI weather model now uses more raw satellite data

Google launched WeatherNext 3, an AI weather model using raw satellite data that beats ECMWF and now powers Search, Gemini, and Maps.

Google released WeatherNext 3, an AI weather forecasting model that incorporates physical surface information (land/ocean type and elevation) to improve surface temperature and dewpoint calculations, improving point location temperature accuracy by up to 30 percent. Its white paper reports roughly 5 percent better upper-atmosphere accuracy than WeatherNext 2, equating to about six additional hours of forecast lead time, outperforming the ECMWF AI model on these metrics. The model now supplies forecast information across Google Search, Gemini, and Maps, though the paper notes unexplained short-lead degraded results and grid-shaped artifacts in some predictions.

Ars Technica · AI·Sep 8, 18:00 UTC · 7d agoModel release

Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video

Skild AI launched its S1 robot foundation model, built on NVIDIA infrastructure, that learns long-horizon industrial tasks from a single video.

Skild AI's S1 model uses in-context learning from one video demonstration to execute unfamiliar multistep tasks lasting up to 10 minutes without weight updates or task-specific post-training. In tests on new tasks it achieved about 66% per-step success versus 9% for a comparable AI system, and one video demonstration was estimated to match roughly 380 hands-on training examples. The company reached a $100 million annual revenue run rate with more than 60 deployment partnerships, and with NVIDIA and Foxconn deploys the Skild Brain on dual-arm manipulators assembling NVIDIA Blackwell systems. Training and validation rely on NVIDIA Isaac Lab, Isaac Sim, Omniverse, Cosmos and the Newton physics engine.

NVIDIA Blog·Sep 10, 16:30 UTC · 6d agoModel release

Keysight Technologies PathWave ADS 2023 automates design-to-test workflows

Help Net Security·Sep 18, 00:00 UTC · Sep 18, 2022Model release

nvidia/Qwen3.8-Flash-Next-NVFP4 — new model trending #28 on Hugging Face

NVIDIA released an NVFP4 4-bit quantized build of Alibaba's Qwen3.8-Flash-Next, a 125B-parameter MoE vision-language model, via Model Optimizer.

The checkpoint quantizes Qwen3.8-Flash-Next — a hybrid-attention (Gated DeltaNet and Qwen Sparse Attention) Mixture-of-Experts model with 125B total and 6B activated parameters, plus 51B n-gram embeddings and 4B MTP — using NVIDIA Model Optimizer v0.46.0. NVFP4 benchmarks stay close to FP8: GPQA Diamond 91.5 vs 92.0, MMMU Pro 78.3 vs 77.1, Terminal-Bench 2.1 82.9 vs 83.3. It targets Blackwell B200/B300 GPUs, runs on vLLM, supports 262K context extendable to 1M tokens, and is licensed under the NVIDIA Open Model License with Qwen Community License 1.0.

Hugging Face trending models·Sep 2, 04:03 UTC · 14d agoModel release