Putting data centres in space isn't going to happen any time soon Could AI's insatiable thirst for colossal data centres be fixed by launching them into space? Tech companies are eyeing low Earth orbit as a potential solution, but researchers say it's unlikely in the near future due to a mountain of difficult and unsolved engineering issues. The huge demand for, and investment in, generative AI products like ChatGPT has created an unprecedented need for computing power, which requires both vast amounts of space and gigawatts of power, equivalent to that used by millions of homes. As a result, data centres are increasingly fuelled by unsustainable sources, like natural gas, with tech companies arguing that renewable power can neither produce the amount of power needed nor the consistency required for reliable use. To solve this, tech CEOs like Elon Musk and Jeff Bezos have suggested launching data centres into orbit, where they could be powered by solar panels with constant access to a higher level of sunlight than on Earth.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Three people, including two crew members of a cargo vessel, were killed in overnight Ukrainian drone attacks on the Russian port of Rostov-on-Don and the town of Bataysk in the country's southern Rostov region, local governor Yury Slyusar said. Russian strikes near Ukraine's Black Sea port of Odesa killed a woman in her car and hit infrastructure.

Inspired by biology, spiking neural networks (SNNs) process information via discrete spikes over time, offering an energy-efficient alternative to the classical computing paradigm and classical artificial neural networks (ANNs). In this work, we analyze the representational power of SNNs by viewing them as sequence-to-sequence processors of spikes, i.e., systems that transform a stream of input spikes into a stream of output spikes. We establish the universal representation property for a natural class of spike train functions. Our results are fully quantitative, constructive, and near-optimal in the number of required weights and neurons. The analysis reveals that SNNs are particularly well-suited to represent functions with few inputs, low temporal complexity, or compositions of such functions. The latter is of particular interest, as it indicates that deep SNNs can efficiently capture composite functions via a modular design. As an application of our results, we discuss spike train classification. Overall, these results contribute to a rigorous foundation for understanding the capabilities and limitations of spike-based neuromorphic systems.

Though the two countries are now in a race to develop atomic technology, China's most advanced reactor was the result of collaboration with American scientists. This April, in a speech given at the Shanghai branch of the Chinese Academy of Sciences, the physicist Xu Hongjie announced a breakthrough. For over a decade, his team had been working on an experimental nuclear reactor that runs on a lava-hot solution of fissile material and molten salt, rather than on solid fuel. The reactor, which went online two years ago, was a feat in itself. It is still the only one of its kind in operation in the world, and has the potential to be both safer and more efficient than the water-cooled nuclear plants that dominate the industry. Now, Xu explained, his team had been able to refuel the reactor without shutting it down, demonstrating a level of mastery over their new system. As dazzling as that was, the timing of Xu's speech also freighted the topic with geopolitical import. Only a few months earlier, DeepSeek, the Chinese artificial-intelligence company, had set alarms ringing through the U.S. tech world when it became clear that the relatively small Chinese startup, operating under U.S. export controls, had created a large language model that rivalled anything devised by the behemoths of Silicon Valley.

Has Trump failed to sell the Iran war to the world? Are US-Israeli attacks against Iran legal? Why did Trump fire Kristi Noem as DHS secretary? United States President Donald Trump is getting into the fusion power business through a $6bn merger of his social media firm and Google-backed TAE Technologies, just days after industry representatives urged federal funding. The all-stock deal, announced on Thursday, is an ambitious bet on the power boom spurred by artificial intelligence (AI) data centres and adds to the Trump family's growing roster of diverse ventures from cryptocurrency to real estate holdings and mobile services.

Authors pictured in order of their interview publication date (left to right, top to bottom). Each year, a small group of PhD students are chosen to participate in the AAAI/SIGAI Doctoral Consortium . This initiative provides an opportunity for the students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. During 2025, we met with some of the students to find out more about their research and the doctoral consortium experience. Kunpeng Xu completed his PhD at the Université de Sherbrooke and is now a postdoctoral fellow at McGill University.

Welcome to our annual list of the worst, least successful, and simply dumbest technologies of the year. We like to think there's a lesson in every technological misadventure. But when technology becomes dependent on power, sometimes the takeaway is simpler: it would have been better to stay away. Here are some of the more notable ones . Each time you've heard a borderline outlandish idea of what AI will be capable of, it often turns out that Sam Altman was, if not the first to articulate it, at least the most persuasive and influential voice behind it. For more than a decade he has been known in Silicon Valley as a world-class fundraiser and persuader.

The AI boom has caused as much carbon dioxide to be released into the atmosphere in 2025 as emitted by the whole of New York City, it has been claimed. The global environmental impact of the rapidly spreading technology has been estimated in research published on Wednesday, which also found that AI-related water use now exceeds the entirety of global bottled-water demand. The figures have been compiled by the Dutch academic Alex de Vries-Gao, the founder of Digiconomist, a company that researches the unintended consequences of digital trends. He claimed they were the first attempt to measure the specific effect of artificial intelligence rather than datacentres in general as the use of chatbots such as OpenAIâ s ChatGPT and Googleâ s Gemini soared in 2025. The figures show the estimated greenhouse gas emissions from AI use are also now equivalent to more than 8% of global aviation emissions.

Can AI really help us discover new materials? Judging from headlines and social media posts in recent years, one might reasonably assume that AI is going to fix the power grid, cure the world's diseases, and finish my holiday shopping for me. This week, we published a new package called Hype Correction . The collection of stories takes a look at how the world is starting to reckon with the reality of what AI can do, and what's just fluff. One of my favorite stories in that package comes from my colleague David Rotman, who took a hard look at AI for materials research . AI could transform the process of discovering new materials--innovation that could be especially useful in the world of climate tech, which needs new batteries, semiconductors, magnets, and more.

As the world looks to incorporate more renewables into energy grids, centuries-old systems that can balance supply and demand are being reappraised and innovated upon.