Last month, the UK government announced the home for its new exascale supercomputer, designed to give the country an edge in the global artificial intelligence race. The £900 million ($1.1 billion) project would be built in Bristol, a city in the west of England famed for its industrial heritage, and the machine itself would be named after the legendary local engineer, Isambard Kingdom Brunel. The Brunel AI project should have been a big moment for another Bristolian export--Graphcore, one of the UK's only large-scale chipmakers specializing in designing hardware for AI. Valued at $2.5 billion after its last funding round in 2020, the company is trying to offer an alternative to the US giant Nvidia, which dominates the market. With AI fast becoming an issue of geopolitical as well as commercial importance, and countries--including the UK--spending hundreds of millions of dollars on building strategic reserves of chips and investing in massive supercomputers, companies like Graphcore should be poised to benefit.

Spain's regional elections are still nearly four months away, but Irene Larraz and her team at Newtral are already braced for impact. Each morning, half of Larraz's team at the Madrid-based media company sets a schedule of political speeches and debates, preparing to fact-check politicians' statements. The other half, which debunks disinformation, scans the web for viral falsehoods and works to infiltrate groups spreading lies. Once the May elections are out of the way, a national election has to be called before the end of the year, which will likely prompt a rush of online falsehoods. "It's going to be quite hard," Larraz says.

To companies grappling with complex data projects powered by artificial intelligence, a system that Nvidia calls an "AI supercomputer in a box" is a welcome development. Early customers of Nvidia's DGX-1, which combines machine-learning software with eight of the chip maker's highest-end graphics processing units (GPUs), say the system lets them train their analytical models faster, enables greater experimentation, and could facilitate breakthroughs in science, health care, and financial services. Data scientists have been leveraging GPUs to accelerate deep learning--an AI technique that mimics the way human brains process data--since 2012, but many say that current computing systems limit their work. Faster computers such as the DGX-1 promise to make deep-learning algorithms more powerful and let data scientists run deep-learning models that previously weren't possible. It costs $129,000, more than systems that companies could assemble themselves from individual components.