IIt was more than even the most ardent advocates expected. After all the demonstrations of superhuman ability, and the debates about whether the technology was humanity’s best invention yet or its surest route to self-destruction, artificial intelligence landed a Nobel Prize this week. And then it landed another one.
First came the physics prize. The American John Hopfield and the British-Canadian Geoffrey Hinton won for foundational work on artificial neural networksthe computing architecture that supports modern AI like ChatGPT. Then came the chemistry prize, half handed Demis Hassabis and John Jumper at Google DeepMind. Their AlphaFold program solved a decades-long scientific challenge by predict the structure of all life’s proteins.
That artificial intelligence won two Nobels in as many days is one thing. That both honored British researchers in a field previously ignored by the Nobels is another. Both Hinton and Hassabis was born in London, albeit almost three decades apart. The watershed moment raises an obvious question: where did it all go wrong? And more importantly, will it go wrong?
Experts in the field do not recognize any specific moment, any specific decision, that Britain’s family tree in artificial intelligence – a technology loosely defined as computer systems that perform tasks that typically require human intelligence. But there were important ingredients that came together and set the stage for what happened in Stockholm this week.
The foundations were formed over centuries. The UK was a serious player in statistics, logic, mathematics and engineering – think Thomas Bayes, George Boole, Charles Babbage, Ada Lovelace – long before Alan Turing asked: “Can machines think?” As computers became an established technology, expertise flourished at a handful of centers.
“The UK has been a leader in computer science and in AI for a long time,” said Dame Muffy Calder, vice-chancellor and head of the college of science and engineering at the University of Glasgow. “We’ve led for years and years and I put that down in part to the funding environment we’ve had in the past that has recognized so-called discovery-led research.”
Unlike research that focuses on cracking a well-defined problem, the research Calder refers to is more speculative. Both AI and quantum technologies have benefited from such work, Calder says, some after decades of support. “That’s the message. You have to keep funding ideas from the beginning,” she said. “It can’t all be innovation-focused or challenge-focused. The Turing Machine? There was no application for the Turing machine when Alan Turing came up with it.”
Maneesh Sahani, professor of theoretical neuroscience and machine learning, and director of the Gatsby Computational Neuroscience Unit at University College London, highlights how groups of smart people have popped up across the UK and created a critical mass of expertise.
“Britain as a whole punched above its weight for a long time and I think that’s still true,” he says. Referring to the machine learning process where instead of being directly taught, computers “learn” by analyzing patterns in data and then making informed decisions, he adds: “But it was really machine learning that the UK fell very far behind. And it wasn’t because of any central decision. It’s one of those things where good people came forward at a similar time.”
Among the early key groups to make an impact were Edinburgh, Cambridge and Aston Universitiesall of which remain strong today. But the momentum Sahani mentions created further groups. His unit at UCL is one of them and his history gives a sense of how these nodes attract and drive expertise. The Gatsby Unit was set up by Hinton, who after studying at Cambridge and Edinburgh spent most of his career in Toronto. Sahani returned to the UK for a post at the Gatsby, where Hassabis, who went on to found DeepMind, did his postdoctoral research.
“The Gatsby was a phenomenal draw,” says Sahani. Funding from the Gatsby Foundation, a charity founded by supermarket heir David Sainsbury, allows the scientists to focus on research without the same demands for teaching and grant chasing that occupy academics elsewhere. “It’s like a chain reaction,” says Sahani. “When you have the critical mass, when you have people doing exciting things and talking to each other, others want to show up and be a part of it.”
AI has experienced boom and bust cycles for decades, but the machine learning revolution, powered by multi-layer neural networks crunching massive data sets on processors built for gaming, has galvanized investors. The surge in funding, from companies and nations eager to be left behind, has changed the landscape, with tech firms, mainly in the US, now dominating AI research.
“It is difficult, increasingly difficult, to be competitive, and it is not only with universities in other countries, but with industry,” says Sahani. “The UK doesn’t quite have the outsized presence it had 10 or 15 years ago. And it’s not because we went backwards, it was because everyone else invested and caught up a lot.”
Universities cannot hope to compete with the vast computing resources available to Google and other big tech firms, their massive data sets to feed AI models, or the salaries they can offer.
Dame Wendy Hall, a professor of computer science at the University of Southampton and a member of the UN’s advisory body on AI, says the priority for the UK must be to protect its “academic legacy” in the technology.
“It is so important that we keep our foot on the pedal to fund AI research in our universities. This is where future generations of AI technology will come from and we need the high-level skills to support our growing AI industry,” she says. “Other countries are deeply jealous. It takes 20 years or more to grow a research star like Hassabis. They don’t just fall out of the trees.”
Sahani believes more centers like the Gatsby Unit, where researchers can simply focus on their research, and a willingness among funders to pick winners and back them, will help the UK in the race ahead. Calder says close relationships between universities and tech firms are essential for both to thrive, while the UK needs to make better use of its sovereign assets, such as NHS health data. “We have to look at the resources we have,” she says.
Are more Nobels on the horizon? This will come down to individuals as well as the work environments around them. “What stands out about Geoff is his creativity and insatiable curiosity. He goes after all kinds of different problems,” says Sahani. “With Demis, his dynamics were clear when he was here. He felt there were big things to build and he was going to go after them.”
