It was more than even the most ardent advocates expected. After all the demonstrations of superhuman prowess, and the debates over 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.
First came the physics prize. The American John Hopfield and the British-Canadian Geoffrey Hinton won for foundational work on artificial neural networks, the computational architecture that underpins modern AI such as ChatGPT. Then came the chemistry prize, with half handed to Demis Hassabis and John Jumper at Google DeepMind. Their AlphaFold program solved a decades-long scientific challenge by predicting the structure of all life’s proteins.
That artificial intelligence won two Nobels in as many days is one thing. That both honoured British researchers in a field previously ignored by the Nobels is another. Both Hinton and Hassabis were born in London, albeit nearly three decades apart. The watershed moment raises an obvious question: where did it all go right? And more importantly, will it go wrong?
Experts in the field do not credit any particular moment, any particular decision, that ensured Britain’s pedigree in artificial intelligence – a technology that can be loosely defined as computer systems performing 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 shaped 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 centres.
“The UK has for a long time been a leader in computing science and in AI,” says Dame Muffy Calder, vice-principal 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 that we’ve had in the past that recognised 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’ve got to keep funding ideas from the beginning,” she said. “It can’t be all 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 clusters of smart people cropped up across the UK and created a critical mass of expertise.
“Britain as a whole has for a long time punched above its weight and I think that’s still true,” he says. Referring to the machine learning process where instead of being instructed directly, computers “learn” by analysing patterns in data and then making informed decisions, he adds: “But it was really machine learning that the UK got behind very strongly. And that was not because of any central decision. It’s one of those things where good people emerged at a similar time.”
Among the early key groups to make an impact were Edinburgh, Cambridge and Aston Universities, all of which remain strong today. But the momentum Sahani mentions created further clusters. His unit at UCL is one of them and its history gives a sense of how these nodes attract and propel 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 set up DeepMind, did his postdoctoral research.
“The Gatsby was a phenomenal draw,” Sahani says. The funding from the Gatsby foundation, a charity set up 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,” Sahani says. “When you’ve got the critical mass, when you’ve got people who are doing exciting things and talking to each other, others want to show up and be part of that.”
AI experienced boom and bust cycles for decades, but the machine learning revolution, driven by multi-layered neural networks crunching massive datasets on processors built for gaming, has galvanised investors. The surge in funding, from companies and nations that cannot risk being left behind, has transformed the landscape, with tech firms, primarily in the US, now dominating AI research.
“It’s difficult, increasingly difficult, to be competitive, and that’s not only with universities in other countries but with industry,” says Sahani. “The UK doesn’t have quite the disproportionate presence that it had 10 or 15 years ago. And that’s not because we went backwards, it was because everybody else invested and did a lot of catching up.”
Universities cannot hope to compete with the vast computing resources available to Google and other big tech firms, their massive datasets 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 we keep our foot on the pedal of funding AI research in our universities. This is where future generations of AI technologies will come from and we need the high-level skills to support our growing AI industry” she says. “Other countries are deeply envious. 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 centres like the Gatsby unit, where researchers can focus purely on their research, and a willingness among funders to pick winners and back them, will help the UK in the race ahead. Calder says tight relationships between universities and tech firms are essential for both to flourish, while the UK should make better use of its sovereign assets, such as NHS health data. “We need to look at the resources we have,” she says.
Are more Nobels on the horizon? That will come down to individuals as well as the working environments around them. “What stands out about Geoff is his creativity and insatiable curiosity. He goes after all sorts of different problems,” says Sahani. “With Demis, what was evident when he was here was his dynamism. He had sense there were great things to be built and he was going to go after them.”
