Weather forecasts can miss rare events like the extreme rainfall caused by 2017's Hurricane Harvey Training AI models on historical weather patterns can turn them into accurate forecasters – but they may not be able to predict extreme events that don't occur in their training data. This could be a growing issue as climate change drives more unprecedented weather. "These models are good, but the question we have been asking is about events that are so rare and strong that these models…

Breakthroughs, discoveries, and DIY tips sent every weekday. Tesla's roughly 7,000-pound stainless steel Cybertruck may not have sold particularly well among the general public, but it does appear to have found a receptive audience in one particular cohort: law enforcement. Police departments across the US--and as far away as the Qatar--have been spotted driving the electric behemoth. Now, a jet-black, militarized Cybertruck is reportedly among the vehicles set to respond to potential incidents during 2026 World Cup matches taking place in Jalisco, Mexico. Officials from the central state of Jalisco said this week that several Cybertrucks will be among 300 new tactical, armored vehicles added to its fleet. The move, first reported by the Jalisco-based newspaper El Informador, is part of a broader effort to revamp the state's police force in preparation for the influx of tourists expected to visit the capitol, Guadalajara, for World Cup matches next year.

Texas Rep. Jasmine Crockett attacked President Donald Trump's West Point address on MSNBC and called it proof of his unfitness as commander in chief. Rep. Jasmine Crockett, D-Texas, appears to be leaning in on her rising political stardom this week, briefly sharing what appeared to be a fan-made song that referred to the Democratic firebrand as the "leader of the future." "Jasmine Crockett, she rises with the dawn. Fighting for justice, her light will never be gone," the song went. Infectious with passion, she'll never bow down.

Russia and Ukraine have launched a wave of drone attacks against each other overnight, even as Moscow claimed it was finalising a peace proposal to end the war. Ukrainian air force officials said on Tuesday that Russia deployed 60 drones across multiple regions through the night, injuring 10 people. Kyiv's air defences intercepted 43 of them – 35 were shot down while eight were diverted using electronic warfare systems. In Dnipropetrovsk, central Ukraine, Governor Serhiy Lysak reported damage to residential properties and an agricultural site after Russian drones led to fires during the night. In Kherson, a southern city frequently hit by Russian strikes, a drone attack on Tuesday morning wounded a 59-year-old man and six municipal workers, officials said.

Breakthroughs, discoveries, and DIY tips sent every weekday. Weather forecasts are notoriously unreliable. Most people can relate to booking a trip or making plans expecting a sunny day, only to have it disappointingly rained out. While seven-day weather forecasts are accurate about 80 percent of the time, that figure drops to around 50 percent when extended to 10 days or more. Recent staffing cuts at the National Weather Service have already led to reduced weather balloon data collection, which experts warn could further degrade forecast accuracy.

Direct democracy, where each voter casts one vote, fails when the average voter competence falls below 50%. This happens in noisy settings when voters have limited information. Representative democracy, where voters choose representatives to vote, can be an elixir in both these situations. We introduce a mathematical model for studying representative democracy, in particular understanding the parameters of a representative democracy that gives maximum decision making capability. Our main result states that under general and natural conditions, 1. for fixed voting cost, the optimal number of representatives is linear; 2. for polynomial cost, the optimal number of representatives is logarithmic.

Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a number of fields, from artificial intelligence and robotics, to medicine and finance. This paper concerns the problem of learning control policies for unknown linear dynamical systems so as to maximize a quadratic reward function. We present a method to optimize the expected value of the reward over the posterior distribution of the unknown system parameters, given data. The algorithm involves sequential convex programing, and enjoys reliable local convergence and robust stability guarantees. Numerical simulations and stabilization of a real-world inverted pendulum are used to demonstrate the approach, with strong performance and robustness properties observed in both.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/