How Google's DeepMind tool is'more quickly' forecasting hurricane behavior'Less expensive and time consuming' model helps with fast and accurate predictions, possibly saving lives and property When then Tropical Storm Melissa was churning south of Haiti, Philippe Papin, a National Hurricane Center (NHC) meteorologist, had confidence it was about to grow into a monster hurricane. As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and begin a turn towards the coast of Jamaica. No NHC forecaster had ever issued such a bold forecast for rapid strengthening. But Papin had an ace up his sleeve: artificial intelligence in the form of Google's new DeepMind hurricane model - released for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Ukrainian President Volodymyr Zelensky has vowed to overhaul state-owned energy companies, after a major corruption scandal engulfed the country's energy sector. Around $100 million (£76m) has been embezzled, anti-graft investigators said, causing outrage in a country where Russian attacks have resulted in crippling power outages. Alongside a full audit of their financial activities, the management of these companies is to be renewed, Zelensky wrote in a post on X on Saturday. Energoatom, the state nuclear company at the heart of the scandal, will have a new supervisory board within a week, he added. Several of those implicated in the scandal have close links to the Ukrainian president.

The Aedes aegypti mosquito that can carry dengue, yellow fever, and Zika was thought to be too reliant on a hot and wet climate to survive in the Mountain West. But now, a population is thriving in Western Colorado. Hannah Livesay, biologist at the Grand River Mosquito Control District, points out the characteristic white markings of an Aedes aegypti mosquito shown under a microscope at her lab in Grand Junction, Colo. It can carry life-threatening diseases. It's difficult to find and hard to kill.

This becomes more obvious when studying a particular disease, for instance. Also, gaining access to large-scale datasets is often difficult due to privacy concerns.

Open problem: "Can we extend [fully dynamic] results (algorithm or hardness) to non-monotone

Suppose we are considering a multivariate event sequence. What caused a specific event to happen? Which variables are causes of each other?

Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized.