Such models, recently coined foundation models, have been transformational to the field of natural language processing.

There's arguably no face, voice or collection of exuberant, patterned shirts more recognisable than those belonging to Dr Karl Kruszelnicki. The bespectacled boffin has been answering curly listener questions about science, with characteristic excitement and passion, for more than 40 years. Despite a seemingly tireless work ethic, Kruszelnicki, now 77 years old, can't be everywhere all at once. Those questions now come in waves, across social media platforms at all hours of the day. "Sometimes I get 300 requests a day on Twitter to answer an involved question about climate change," Kruszelnicki says.

Ukraine's military has said it struck an oil refinery in Russia's Saratov region in an overnight drone attack, causing explosions and destruction, according to an army statement, as daily aerial exchanges intensify with diplomatic momentum to end the war in play. Saratov's governor said on Sunday that one person was killed and several residential apartments and an industrial facility were damaged, but did not mention the oil refinery being struck. "[Ukrainian] drones are targeting … deeper into Russian territory [than] in the past, where previous attacks have been focused on the line of contact in the south and the western parts of Russia," said Al Jazeera's Osama Bin Javaid, reporting from Moscow. It is still unclear whether Ukraine's claims that it hit a refinery are true, he added. Ukraine's military also said on Sunday that it had taken back a village in the Sumy region from the Russian army, which has made significant recent gains there.

In the wake of January's horrific fires, detractors of Los Angeles -- an urban reality often seen as a toxic mixture of unsustainable resource planning and structurally poor governance systems -- are having a field day. Los Angeles knows how to weather a crisis -- or two or three. Angelenos are tapping into that resilience, striving to build a city for everyone. Their criticism is not new: For most of the 20th century -- and certainly for the last five decades or so -- Los Angeles has been seen by many urbanists as less city and more cautionary tale -- a smoggy expanse of subdivisions and spaghetti junctions, where ambition came with a two-hour commute. Planners shuddered, while architects looked away, even as they accepted handsome commissions to build some of L.A.'s -- if not the world's -- most iconic buildings.

That was how Sam Altman, chief executive of OpenAI, described the latest upgrade to ChatGPT this week. The race Altman was referring to was artificial general intelligence (AGI), a theoretical state of AI where, by OpenAI's definition, a highly autonomous system is able to do a human's job. Describing the new GPT-5 model, which will power ChatGPT, as a "significant step on the path to AGI", he nonetheless added a hefty caveat. "[It is] missing something quite important, many things quite important," said Altman, such as the model's inability to "continuously learn" even after its launch. In other words, these systems are impressive but they have yet to crack the autonomy that would allow them to do a full-time job.

In mid-2023, if a user asked OpenAI's ChatGPT for a recipe for artichoke pasta or instructions on how to make a ritual offering to the ancient Canaanite deity Moloch, its response might have taken – very roughly – 2 watt-hours, or about as much electricity as an incandescent bulb consumes in 2 minutes. OpenAI released a model on Thursday that will underpin the popular chatbot – GPT-5. Ask that version of the AI for an artichoke recipe, and the same amount of pasta-related text could take several times – even 20 times – that amount of energy, experts say. As it rolled out GPT-5, the company highlighted the model's breakthrough capabilities: its ability to create websites, answer PhD-level science questions, and reason through difficult problems. But experts who have spent the past years working to benchmark the energy and resource usage of AI models say those new powers come at a cost: a response from GPT-5 may take a significantly larger amount of energy than a response from previous versions of ChatGPT.

Unfortunately, when we move away from the safe haven of two-agent strictly competitive games, a lot of these regularities disappear.

Acting NASA Administrator Sean Duffy explains how the agency's Artemis program aims to return Americans to the Moon on'Hannity.' Amid significant budget cuts, NASA is fast-tracking the development of nuclear reactors on the moon and next-generation space stations with one clear objective: beating U.S. adversaries in the new space race. Two new memos signed by interim NASA chief and Transportation Secretary Sean Duffy outline a bold strategy to secure strategic ground on the moon. The centerpiece of this effort is a lunar nuclear reactor, a renewable and stable power source to support long-term exploration. "The goal is to power everything," a senior NASA official told Fox News Digital. "Our systems, habitats, rovers, robotic equipment, even future mining operations -- everything we want to do on the moon depends on this."

Bayesian optimization (BO) is a widely used method for data-driven optimization that generally relies on zeroth-order data of objective function to construct probabilistic surrogate models. These surrogates guide the exploration-exploitation process toward finding global optimum. While Gaussian processes (GPs) are commonly employed as surrogates of the unknown objective function, recent studies have highlighted the potential of Bayesian neural networks (BNNs) as scalable and flexible alternatives. Moreover, incorporating gradient observations into GPs, when available, has been shown to improve BO performance. However, the use of gradients within BNN surrogates remains unexplored. By leveraging automatic differentiation, gradient information can be seamlessly integrated into BNN training, resulting in more informative surrogates for BO. We propose a gradient-informed loss function for BNN training, effectively augmenting function observations with local gradient information. The effectiveness of this approach is demonstrated on well-known benchmarks in terms of improved BNN predictions and faster BO convergence as the number of decision variables increases.