News
What's happening in robotics and AI — curated by the wujec.ai editors.
DeepMind opens its cyclone forecaster: three-day tracks as good as yesterday's two-day ones
Google DeepMind has published WeatherNext Cyclones in Nature and released the model weights on GitHub, alongside the broader WeatherNext 2 system. The headline claim is a full day of lead time: three-day forecasts of a storm's track and intensity now reach the accuracy that previous models achieved only at two days. The model was trained on roughly 20 terabytes of atmospheric data and about 5,000 historical storms, and it forecasts by ensemble — DeepMind scaled the run from 50 scenarios to 1,000, which is what allows a forecaster to read the spread as a probability rather than a single line on a map. A 15-day forecast at 28 by 28 kilometre resolution runs in under a minute on a single TPU. For a catalogue of AI models this is a useful reference point on what open weights now cover. Weather prediction has been a showcase for machine learning for several years, but the operational systems behind national forecasts have stayed closed. Releasing the weights moves a model of this class into the hands of meteorological services that cannot afford to train one. Two details fill out the picture. The first is who checked the work: DeepMind says the model was developed with the US National Hurricane Center, the Cooperative Institute for Research in the Atmosphere and the UK Met Office, and points to the 2025 season — Hurricane Melissa’s rapid intensification and its landfall on Jamaica — as a case of operational use. The second is intensity, historically the harder half of the problem and the half that decides evacuation orders: the model reports around 11 knots of error, better than the specialised HWRF hurricane model, while track error at day three sits near 100 km, ahead of the ECMWF ensemble. That it does this on a 28 by 28 kilometre grid is the awkward part for the traditional approach. The cells are about a hundred times coarser than the physics-based models it is measured against, and a hurricane eye fits inside one of them. The model is not resolving the storm at all; it has learned what storms of a given shape and history tend to do next.
WeatherNext Cyclones →Hassabis steps back from running Google DeepMind — Kavukcuoglu takes the controls, reporting to Pichai
Sundar Pichai announced on 5 August 2026 that Demis Hassabis is stepping back from day-to-day leadership of Google DeepMind. Hassabis becomes Chair of Google DeepMind and takes a newly created role as Chief Scientist of Alphabet; he remains chief executive of Isomorphic Labs, the drug-discovery spin-off. Operational command passes to Koray Kavukcuoglu, who becomes Senior Vice President of Google DeepMind and reports directly to Pichai. His remit covers Gemini model development, frontier AI research and the Gemini app and developer teams. Kavukcuoglu is not an outside hire: he has been at DeepMind for thirteen years, started its deep learning team and led work behind WaveNet and DQN. The timing matters more than the titles. The reshuffle lands in the same week as the departure of Jeff Dean after 27 years at Google, together with Gemini co-lead Oriol Vinyals — a subject this catalogue covered separately. Within roughly a month Google has also shipped Gemini 3.6 Flash into general availability, put Gemini Omni Flash into public preview as its recommended video model, and released the Gemini Robotics ER 2 endpoints. A laboratory does not usually change its command structure in the middle of a release run unless the change is meant to separate two jobs that had grown too big for one person: setting the research direction, and shipping products on a quarterly clock. Hassabis has led DeepMind since he co-founded it in 2010, through AlphaGo, AlphaFold and the Nobel Prize in Chemistry that followed. Google's announcement gives no effective date for the transition. Reports of the market's reaction to the news come from press coverage rather than from the memo itself.
Jeff Dean leaves Google after 27 years to automate the scientific method
Jeff Dean, Google's chief scientist and the engineer behind much of the infrastructure the modern web runs on, is leaving the company after almost 27 years. The departure was announced on 5 August 2026 alongside a wider reshuffle of Google's AI leadership, in which Koray Kavukcuoglu was promoted to senior vice-president leading Google DeepMind. Dean is not leaving alone. He co-founds Discovery Loop with Sanjay Ghemawat, the Google Senior Fellow who built much of that infrastructure with him; Oriol Vinyals, a vice-president of research at Google DeepMind and technical lead on Gemini; and Quoc Le, a co-founder of Google Brain. It is difficult to name four departures that would cut deeper into one company's research bench. Discovery Loop is set up as an independent public benefit corporation and aims to use AI to automate scientific and engineering research. Its declared starting point is deliberately recursive: autonomous experiment loops whose first subject is the machine-learning algorithms the company itself depends on — a system that runs experiments to improve the thing running the experiments. Google's response is unusual for a departure of this size. Rather than treating it as a loss, the company is a founding investor, will act as the startup's cloud partner, is supplying compute for its first year and plans to collaborate on a shared research framework for ML systems and infrastructure.