We develop a module-based method that having a dictionary of well fitted GPs, one could build ensemble GP models without revisiting any data.

In Figure 1 (left) weseethat Gaussian posteriors (intheabsenceofdata) optimizedwith Eq. (1) convergetotheirpriorN(w|0,1)[7,68]. In CMV -MF-VI, shown in (b.), prunedunitsconvergetospiked Gaussians.

We develop a framework for generalized variational inference in infinitedimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI).

No, chatbots aren't magic--but this symbol might make you think they are. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Alex_Kirshner newsletter. You can manage your newsletter subscriptions at any time.

Although it is, at least according to the Library of Congress, the most-watched movie of all time, The Wizard of Oz was a costly failure at the box office, and only became a perennial favorite thanks to the regular TV airings that began in the 1950s. But in the decades since it's become a metonym for the wonder of the big screen, a movie even people who prefer their content streaming will make the effort to see in a movie theater. Beginning on Labor Day weekend, audiences will get to experience the movie on perhaps the largest screen ever created. But it won't be The Wizard of Oz as we've come to know it for the better part of a century. The version of the movie that will fill Las Vegas' Sphere starting Aug. 28 has been retooled to fit the venue's curved shell, its images enhanced and expanded to fill four football fields' worth of 16K LED screens--the foundation of an immersive presentation that also includes flames, gusts of wind, and inflatable flying monkeys piloted by drone. It is, to quote the title of a CBS news report, "The Wizard of Oz as you've never seen it before."

Large language models (LLMs), inspired by neuroscience, exhibit behaviors that often evoke a sense of personality and intelligence-yet the mechanisms behind these effects remain elusive. Here, we operationalize Conceptual Blending Theory (CBT) as an experimental framework, using prompt-based methods to reveal how LLMs blend and compress meaning. By systematically investigating Prompt-Induced Transitions (PIT) and Prompt-Induced Hallucinations (PIH), we uncover structural parallels and divergences between artificial and biological cognition. Our approach bridges linguistics, neuroscience, and empirical AI research, demonstrating that human-AI collaboration can serve as a living prototype for the future of cognitive science. This work proposes prompt engineering not just as a technical tool, but as a scientific method for probing the deep structure of meaning itself.

United States Customs and Border Protection plans to log every person leaving the country by vehicle by taking photos at border crossings of every passenger and matching their faces to their passports, visas, or travel documents, WIRED has learned. The escalated documentation of travelers could be used to track how many people are self-deporting, or leave the US voluntarily, which the Trump administration is fervently encouraging to people in the country illegally. CBP exclusively tells WIRED, in response to an inquiry to the agency, that it plans to mirror the current program it's developing--photographing every person entering the US and match their faces with their travel documents--to the outbound lanes going to Canada and Mexico. The agency currently does not have a system that monitors people leaving the country by vehicle. "Although we are still working on how we would handle outbound vehicle lanes, we will ultimately expand to this area," CBP spokesperson Jessica Turner tells WIRED.

A new AI weather prediction system, developed by a team of researchers from the University of Cambridge, can deliver accurate forecasts which use less computing power than current AI and physics-based forecasting systems. The system, Aardvark Weather, has been supported by the Alan Turing Institute, Microsoft Research and the European Centre for Medium Range Weather Forecasts. It provides a blueprint for a new approach to weather forecasting with the potential to improve current practices. The results are reported in the journal Nature. "Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries," said Professor Richard Turner from Cambridge's Department of Engineering, who led the research.

An AI weather program running for a single second on a desktop can match the accuracy of traditional forecasts that take hours or days on powerful supercomputers, claim its creators. Weather forecasting has, since the 1950s, relied on physics-based models that extrapolate from observations made using satellites, balloons and weather stations. But these calculations, known as numerical weather prediction (NWP), are extremely intensive and rely on vast, expensive and energy-hungry supercomputers. Microsoft has a new quantum computer – but does it actually work? In recent years, researchers have tried to streamline this process by applying AI.

A single researcher with a desktop computer will be able to deliver accurate weather forecasts using a new AI weather prediction approach that is tens of times faster and uses thousands of times less computing power than conventional systems. Weather forecasts are currently generated through a complex set of stages, each taking several hours to run on bespoke supercomputers, requiring large teams of experts to develop, maintain and deploy them. Aardvark Weather provides a blueprint to replace the entire process by training an AI on raw data from weather stations, satellites, weather balloons, ships and planes from around the world to enable it to make predictions. This offers the potential for vast improvements in forecast speed, accuracy and cost, according to research published on Thursday in Nature from the University of Cambridge, the Alan Turing Institute, Microsoft Research and the European Centre for Medium-Range Weather Forecasts (ECMWF). Richard Turner, a professor of machine learning at the University of Cambridge, said the approach could be used to quickly provide bespoke forecasts for specific industries or locations, for example predicting temperatures for African agriculture or wind speeds for a renewable energy company in Europe.