The Royal Swedish Academy of Sciences today awarded the Nobel Prize in physics to artificial intelligence pioneers John Hopfield and Geoffrey Hinton.

Hopfield (pictured, left) is a professor emeritus of molecular biology at Princeton University. Hinton (right) is a professor emeritus of computer science at the University of Toronto. They received the Nobel Prize for their “foundational discoveries and inventions that enable machine learning with artificial neural networks.”

The committee that issues the prize selected Hopfield for his development of an early AI model called the Hopfield network. The algorithm, which can fix distorted images, is based on concepts borrowed from the field of condensed matter physics. This is a branch of physics that focuses on the study of matter, particularly solids and liquids.

Hopfield introduced the Hopfield network in a 1982 paper. Three years later, Hinton used the discovery to develop the Boltzmann machine, a groundbreaking deep learning model. The algorithm is based on not only the Hopfield network but also methods from the field of statistical physics, which uses statistical techniques to study particles.

“The laureates’ work has already been of the greatest benefit,” said Ellen Moons, the chair of the Nobel Committee for Physics. “In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.”

Following their Nobel Prize-winning discoveries in the 1980s, Hopfield and Hinton both went on to make significant discoveries in a number of other areas. 

In the AI ecosystem, Hinton is perhaps best known for his work on back propagation. This is a method of training neural networks that is widely used in AI projects to this day. For his work on back propagation and Boltzmann machines, Geoffrey Hinton won the 2018 Turing Award, the highest distinction in computer science.

Hinton joined Google LLC in 2013 to support the search giant’s machine learning research. He left last year, citing concerns about AI’s potential risks. Besides holding a professorship at the University of Toronto, Hinton is also the chief scientific advisor of nonprofit AI lab Vector Institute.

Hopfield’s scientific contributions span several different fields. He earned his physics doctorate in 1958 for a discovery related to quasiparticles, groups of particles that behave like a single particle. Hopfield’s subsequent work helped advance not only physics and AI research but also other fields such as biochemistry. 

“The pioneering methods and concepts developed by Hopfield and Hinton have been instrumental in shaping the field of ANNs [artificial neural networks],” the Nobel Committee for Physics said in a statement. “Simply put, thanks to their work humanity now has a new item in its toolbox, which we can choose to use for good purposes.”

Image: Royal Swedish Academy of Sciences