Two Nobel Prizes went to artificial intelligence (AI) pioneers this week, marking a historic moment for the field. Geoffrey Hinton and John Hopfield shared the Nobel in Physics, while Demis Hassabis and John Jumper took home the Chemistry prize. However, the recognition of AI in categories traditionally reserved for other fields has sparked a lively debate over the appropriateness of these accolades within traditional scientific categories.

Critics argue that the Nobel committee is bending the rules to fit AI into traditional scientific disciplines, raising questions about how to properly recognize achievements in emerging fields like artificial intelligence.

Hinton and Hopfield’s Groundbreaking Work

Hinton and Hopfield’s work on neural networks, which began in the late 1970s, has laid the groundwork for modern AI applications. Hinton, hailed the “godfather of AI,” paved the way for modern machine learning and is no stranger to recognition. But some contented that his contributions, while groundbreaking, do not belong in the realm of physics.

“What he did was phenomenal, but was it physics? I don’t think so,” remarked Noah Giansiracusa, a math professor at Bentley University. “Even if there’s inspiration from physics, they’re not developing a new theory in physics or solving a longstanding problem in physics.”

Professor Dame Wendy Hall, an AI advisor to the United Nations, expressed skepticism about the awards. “The Nobel prize committee doesn’t want to miss out on this AI stuff, so it’s very creative of them to push Geoffrey through the physics route,” she said. “I would argue both are dubious, but nonetheless worthy of a Nobel prize in terms of the science they’ve done.”

Hinton himself, who left Google last year to speak freely about AI risks, acknowledged the dual nature of his achievements. “It’ll mean huge improvements in productivity. But we also have to worry about a number of possible bad consequences, particularly the threat of these things getting out of control,” he said.

The Big Tech’s Involved

In stark contrast, the Chemistry prize awarded to Hassabis and Jumper has been widely praised. Their AlphaFold model solved the decades-old problem of predicting protein structures from amino acid sequences. Since its release in 2020, AlphaFold has been used by millions of researchers across 190 countries to advance knowledge in fields as diverse as antibiotic resistance and drug discovery.

But these two awards also shine a spotlight on Big Tech’s growing dominance in AI research. Hassabis and Jumper, both affiliated with Google DeepMind, represent the increasing influence of private tech companies in fields traditionally led by academia. Critics argue that the financial backing and resources available to Big Tech far outpace those available to academic institutions.

“So much of Big Tech is not oriented towards the next deep-learning breakthrough, but making money by pushing chatbots or putting ads all over the internet,” Giansiracusa said. “There are pockets of innovation, but much of it is very unscientific.”

Hinton, however, after leaving Google in 2023, assured that the company acted responsibly.

Challenges Ahead in AI Recognition

The Nobel committee’s decision to honor AI researchers may reflect an attempt to acknowledge the field’s growing importance, but it surfaces challenges with categorizing these kinds of achievements as well as the gap between traditional academia and well-funded tech giants.