Lockheed Martin CEO Jim Taiclet weighs in on the Trump administration's Golden Dome defense system announcement on'Special Report.' Lockheed Martin CEO Jim Taiclet said President Donald Trump's proposed "Golden Dome" missile shield for the United States is a "fantastic vision" for the country as defense contracting companies work to implement the commander-in-chief's bold idea by the end of his term. "We'll be able to use the Golden Dome concept to make sure the country is increasingly protected against hypersonic threats," Taiclet said in an exclusive interview Tuesday on "Special Report." Trump unveiled his ambitious missile defense plan at the White House last week, which he says will be operational by the time he leaves office. The announcement comes as the United States faces growing threats from adversaries around the world who are making significant inroads in artificial intelligence and drone technology.

Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements. Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets. One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry. However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself.

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…

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.

Imagine your phone rings and the voice on the other end sounds just like your boss, a close friend, or even a government official. They urgently ask for sensitive information, except it's not really them. It's a deepfake, powered by AI, and you're the target of a sophisticated scam. These kinds of attacks are happening right now, and they're getting more convincing every day. That's the warning sounded by the 2025 AI Security Report, unveiled at the RSA Conference (RSAC), one of the world's biggest gatherings for cybersecurity experts, companies, and law enforcement.

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.

Last spring, Daniel Kokotajlo, an A.I.-safety researcher working at OpenAI, quit his job in protest. He'd become convinced that the company wasn't prepared for the future of its own technology, and wanted to sound the alarm. After a mutual friend connected us, we spoke on the phone. I found Kokotajlo affable, informed, and anxious. Advances in "alignment," he told me--the suite of techniques used to insure that A.I. acts in accordance with human commands and values--were lagging behind gains in intelligence.

A UFO researcher has an ominous message for humanity as governments around the world begin releasing more information about alleged contact with extraterrestrials. Dr Julia Mossbridge is a cognitive neuroscientist and a researcher of unidentified aerial phenomena (UAP) - the new term for UFOs and alien sightings. After scientists in Colombia recovered a mysterious, sphere-shaped object that many now believe is a piece of UFO technology, Mossbridge said the world is moving into an era which may soon have to deal with the knowledge that aliens exist. 'We are entering a time when we are starting to recognize as humans we don't have the control that we thought we had over everything,' Dr Mossbridge told Fox News. However, Mossbridge, who studies how humans think and also attended the May 1 congressional hearing on UAPs, said the impending disclosure of alien life could throw the worldview of many people into chaos.

Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and wellchosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, are not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple "filter normalization" method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.

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.