Why this year’s Physics Nobel is crucial to understanding the future of AI

US scientist John Hopfield and his British-Canadian colleague Geoffrey Hinton, known as the ‘godfather of artificial intelligence’, have been awarded the 2024 Nobel Prize in Physics for their foundational work on enabling machine learning with artificial neural networks.

"This year's two Nobel Laureates in Physics have used tools from physics to develop methods that are the foundation of today's powerful machine learning," the academy said on their official X account.

“They have showed a completely new way for us to use computers to aid and to guide us to tackle many of the challenges our society faces.”

The Royal Swedish Academy of Sciences, in announcing the award, praised both scientists for “transformative contributions” that have enabled machine learning to process vast amounts of data and make decisions, much like the human brain.

Who are the two Nobel laureates?

As a professor emeritus at Princeton University, Hopfield, 91, is well-known for developing the Hopfield Network in the 1980s, which modelled associative memory using principles from physics.

His work was critical to understanding how neural networks can simulate memory and learning processes.

British-born Hinton, 76, a professor emeritus at the University of Toronto, quit Google in 2023 after noticing computers could become smarter than people far sooner than he and other experts had expected.

Computer scientist and cognitive psychologist Hinton invented a method that can autonomously find properties in data and perform tasks such as identifying specific elements in pictures.

"I am flabbergasted, I had no idea this would happen, I am very surprised," Hinton told journalists, when asked how he felt about being a Nobel laureate.

"This will be comparable with the industrial revolution. Machine Learning will exceed people in intellectual abilities," he added.

Although he highlighted its several application areas, including healthcare, AI assistants, and improved work productivity, he also emphasised the potential danger of Machine Learning leading to situations where control could be lost.

The impact of their work

Hopfield and Hinton’s contributions are moving beyond theoretical research into practical applications that now touch daily life.

Hopfield network played a pivotal role in demonstrating how neural networks could mimic the brain’s way of processing and storing information.

Hinton extended the Hopfield network with the Boltzmann machine, which learns to recognise characteristic elements in data using statistical physics. This machine is trained by feeding it examples that are likely to emerge during its operation.

It can classify images or generate new examples based on its training, playing a major role in the development of machine learning.​

This theoretical breakthrough laid the foundation for future work in AI, making it possible for machines to simulate learning and memory​.

Hinton’s work on backpropagation revolutionised the training of neural networks, allowing these systems to improve by learning from their errors.

This method is crucial to the functioning of today’s deep learning systems, which are the backbone of technologies like speech recognition, computer vision, and natural language processing.

Without backpropagation, neural networks would struggle to improve in accuracy and complexity, hindering their ability to perform the sophisticated tasks they are now capable of.​

From voice recognition systems to diagnostic medical tools, their advancements form the backbone of the AI revolution.

The Nobel Committee highlighted how their work enables AI to sort and analyse vast datasets more efficiently throughout different sectors​.

"Machine learning based on artificial neural networks is currently revolutionising science, engineering and daily life." the committee stated.

Global concerns

The committee also acknowledged the widespread global concerns about machine learning and artificial intelligence.

"While machine learning has enormous benefits, its rapid development has also raised concerns about our future.” said Ellen Moons, chair of the Nobel Committee for Physics.

“Collectively, humans carry the responsibility for using this new technology in a safe and ethical way for the greatest benefit of humankind,” she added.

Hinton has taken action on these concerns in the past, resigning from his position at Google to openly discuss the risks associated with the technology he contributed to developing.

He conveyed his ongoing concerns about “a number of possible bad consequences” stemming from his work in machine learning, “particularly the threat of these things getting out of control.”

Despite these worries, he noted that he would still make the same decisions again.

The Nobel Prize, widely regarded as the highest honour for physicists globally, was established through the will of Alfred Nobel, alongside awards for accomplishments in science, literature, medicine and peace.

The award comes with a prize sum of 1.1 million dollars, shared by two winners.

Physics is the second Nobel Prize awarded this week, following the medicine prize won by US scientists Victor Ambros and Gary Ruvkun for discovering microRNA and its role in gene regulation, which has provided insight into how cells specialise.