YoIt was more than even the most ardent defenders expected. After all the displays of superhuman prowess and debates about whether the technology was humanity’s best invention yet or its surest route to self-destruction, artificial intelligence won a Nobel Prize this week. And then another one landed.
First was the physics prize. American John Hopfield and British-Canadian Geoffrey Hinton won for their seminal work on artificial neural networks, the computational architecture that underpins modern AI like ChatGPT. Then came the chemistry prize, awarded halfway to Demis Hassabis and John Jumper of Google DeepMind. Their AlphaFold program solved a decades-old scientific challenge by predicting the structure of all of life’s proteins.
That artificial intelligence has won two Nobel Prizes in as many days is one thing. That they both honored British researchers in a field previously ignored by the Nobel Prize winners is another. Both Hinton and Hassabis were born in London, although almost three decades apart. The decisive moment raises an obvious question: where did everything go right? And most importantly, will it go wrong?
Experts in the field give no credit to any particular moment, any particular decision, that secured Britain’s pedigree in artificial intelligence, a technology that can be loosely defined as computer systems that perform tasks that normally 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 major 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, the expertise flourished in a handful of centers.
“The UK has long been a leader in computer science and artificial intelligence,” says Dame Muffy Calder, deputy principal and head of the Faculty of Science and Engineering at the University of Glasgow. “We’ve been a leader for years and I attribute that in part to the funding environment we’ve had in the past that recognized so-called discovery-based research.”
Unlike research that focuses on solving a well-defined problem, the research Calder refers to is more speculative. Both AI and quantum technologies have benefited from that work, Calder says, some after decades of support. “That’s the message. We have to continue financing ideas from the beginning,” he stated. “Not everything can be focused on innovation or challenges. The Turing machine? “There was no application for the Turing machine when Alan Turing invented 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 emerged across the UK and created a critical mass of expertise.
“Britain as a whole has long stretched itself beyond its means and I think that remains true,” he says. Referring to the machine learning process in which, rather than receiving direct instructions, computers “learn” by analyzing patterns in data and then making informed decisions, he adds: “But it was really machine learning that the UK strongly supported. force. And that was not due to any central decision. “It’s one of those things where good people emerged at a similar time.”
Among the first key groups to have an impact were the universities of Edinburgh, Cambridge and Aston, all of which remain strong today. But the push Sahani mentions created more groups. Its unit at UCL is one of them and its story gives an idea of how these nodes attract and drive experience. The Gatsby Unit was created by Hinton, who after studying at Cambridge and Edinburgh spent most of his career in Toronto. Sahani returned to the UK to take up a position at Gatsby, where Hassabis, who later founded DeepMind, conducted his postdoctoral research.
“The Gatsby was a phenomenal draw,” says Sahani. Funding from the Gatsby Foundation, a charity set up by supermarket heir David Sainsbury, allows scientists to focus on research without the same teaching and grant-seeking demands that occupy academics elsewhere. “It’s like a chain reaction,” says Sahani. “When you have critical mass, when you have people doing interesting things and talking to each other, others want to show up and be a part of that.”
AI has experienced boom and bust cycles for decades, but the machine learning revolution, powered by multi-layer neural networks that process massive data sets on processors designed for gaming, has galvanized investors. Increasing funding, from companies and countries that cannot risk being left behind, has transformed the landscape, with technology companies, primarily in the United States, now dominating AI research.
“It is difficult, increasingly difficult, to be competitive, and that is not only the case with universities in other countries but also with the industry,” says Sahani. “The United Kingdom does not have the disproportionate presence that it had 10 or 15 years ago. And that’s not because we’ve gone backwards, but because everyone else invested and played catch-up a lot.”
Universities cannot hope to compete with the vast computing resources available to Google and other big tech companies, their huge data sets to feed AI models, or the salaries they can offer.
Dame Wendy Hall, a computer science professor at the University of Southampton and a member of the UN advisory body on AI, says the priority for the UK must be protecting its “academic legacy” in the technology.
“It is very important that 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 high-level skills to support our growing AI industry,” he says. “Other countries are deeply envious. It takes 20 years or more for a research star like Hassabis to grow. “They don’t just fall out of trees.”
Sahani believes that more centers like the Gatsby unit, where researchers can focus solely on their research, and a willingness among funders to pick winners and support them, will help the UK in the race ahead. Calder says close relationships between universities and technology companies are essential for both to thrive, 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,” he says.
Are there more Nobel Prizes on the horizon? This will depend on both the people and the work environments that surround them. “What stands out about Geoff is his creativity and insatiable curiosity. It deals with all kinds of different problems,” says Sahani. “With Demis, what was evident when he was here was his dynamism. I had the feeling that there were great things to build and that I was going to go after them.”
