This Scientist Thinks an A.I. Could Win a Nobel Prize by 2050

For more than a century, early October has marked the arrival of Nobel Prize announcements recognizing achievements across sciences, literature and peace. Recipients vary by nationality, age and gender but share one thing in common: they’re human. That could change in the coming decades if the team behind the Nobel Turing Challenge succeeds.

Launched in 2016 by Japanese scientist Hiroaki Kitano, the challenge aims to spur the creation of an autonomous A.I. system capable of making a Nobel Prize-worthy discovery by 2050. Kitano was inspired to start the endeavor after concluding that progress in complex fields like systems biology might eventually require an A.I. scientist or A.I.-human hybrid. “After 30 years of research, I realized that biological systems may be too complex and vast and overwhelm human cognitive capabilities,” Kitano told Observer.

Kitano has long worked at the intersection of science and machine learning. In the 1980s and early 1990s, he researched A.I. systems at Carnegie Mellon University. More recently, he served as the chief technology officer of Sony Group Corporation from 2022 to 2024 and now holds the title of chief technology fellow. He’s also CEO of Sony Computer Science Laboratories, a unit focused on cutting-edge research.

The broader science community initially greeted the Nobel Turing Challenge with a mix of excitement and skepticism. This didn’t faze Kitano, who faced similar reactions in 1993 when he co-founded RoboCup, an international robotics competition challenging developers to build a robotic football team capable of defeating the best human players by 2050.

“Any grand challenge will face such mixed reactions,” he said. “Otherwise, it is not challenging enough.”

Today, Kitano’s goal seems less far-fetched. A.I. already plays a growing role in the work of recent Nobel Prize winners—albeit with human oversight. Last year, the Nobel Prize in Physics went to A.I. researchers Geoffrey Hinton and John Hopfield for their contributions to neural network training. Two of last year’s Chemistry laureates, Google DeepMind’s Demis Hassabis and John Jumper, were recognized for developing AlphaFold, an A.I. model that predicts protein structures.

The Nobel Turing Challenge has two main objectives. First, an A.I. system must autonomously handle every stage of scientific research: defining questions, generating hypotheses, planning and executing experiments, and forming new questions based on the results. Second, in a nod to the Turing test, the challenge aims to see whether such an A.I. scientist could perform so convincingly that peers—and even the Nobel Prize selection committee—would not realize it’s a machine.

Kitano believes A.I. is most likely to earn a Nobel Prize in physiology or medicine, chemistry, or physics, but he admits there’s still a long way to go despite rapid progress in recent years. Creating a system capable of generating large-scale hypotheses and running fully automated robotic experiments remains a formidable challenge. “We are in the early stage of the game,” he said.

Still, the challenge’s stated goal—to have an A.I. win a Nobel Prize—isn’t technically possible. The awards, established in 1895 through the will of inventor Alfred Nobel, can only be granted to a living person, organization or institution. Even so, Kitano hopes his initiative might eventually influence how the Nobel committees make decisions.

“I think if [the] Nobel committee created an internal rule to check if the candidate is human or A.I. before the award decision, that would be our win.”