Nobel Prize in Physics Goes to AI Pioneers

 

"The Nobel Prize in physics has been awarded to John Hopfield of Princeton University and Geoffrey Hinton of the University of Toronto for discoveries and inventions that formed the building blocks of machine learning underpinning many of today's most powerful artificial intelligence models.

The scientists helped train artificial neural networks that can recognize patterns in large data sets using tools from physics, making machine learning tasks like facial recognition and language translation possible.

Hinton, a British-Canadian computer scientist known as one of the "godfathers of AI," worked for Google for more than a decade but quit last year to speak more freely about the risks of AI development. He, and others, have expressed alarm over the ways AI could harm humanity.

In a Tuesday morning call with the Nobel Committee, Hinton said he is "worried that the overall consequence of this might be systems more intelligent than us that eventually take control," but that he would "do the same again" when it comes to his work.

Hopfield echoed his co-laureate's concerns in a video call Tuesday afternoon to a Princeton auditorium full of faculty and other guests.

"The worry I have is not AI quite directly, but AI combined with information flow around the globe," he said, adding that a simple algorithm in a neural network can control a very big system of information, but he is "unnerved" by the idea that such networks' work may not be well understood.

Since the 1980s, Hopfield, 91 years old, and Hinton, 76, have conducted important work that uses fundamental concepts from physics to design artificial neural networks.

"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 face," the Nobel Committee said on X Tuesday as it announced the prize in Stockholm.

Artificial neural networks, as the name suggests, are programs that help machines learn -- inspired by the abilities of the human brain and its network of neurons.

"We can recognize images and speech and associate them with memories and past experiences. Millions of neurons wired together give us unique cognitive abilities," said Ellen Moons, chair of the Nobel committee for physics.

Machine learning aims to mimic those abilities by feeding computers incredible amounts of data so they can master tasks, like offering predictive text or choosing your next Netflix show.

Hopfield pioneered this work in 1982 when he created the "Hopfield network," a neural network that could save and recall patterns with only partial information, inspired by what he called "associative memory."

AI offers the "ability to link things together, link experiences together," Hopfield said on a 2020 podcast, pointing to how mentioning just a few facts about a person -- such as what they look like, what their voice sounds like, where they went to college and where you met them -- enables someone to recall who they are without their name ever being mentioned.

"A lot of progress in AI that we're witnessing today can certainly be directly or indirectly attributed to these ideas," said Dmitry Krotov, a research staff member at IBM Research in Cambridge, Mass. "His influence has been absolutely monumental."

Hinton expanded Hopfield's work to create a network that could recognize familiar patterns in never-before-seen information. He developed a technique that helps optimize a neural network by iteratively correcting errors until they disappear.

The scientists' approaches to training neural networks helped pave the way for systems like ChatGPT.

"It would be hard to imagine that GPT and everything would be here without them," said Jerome Delhommelle, chair of the American Physical Society's topical group on data science and a chemist at the University of Massachusetts, Lowell.

When asked about his favorite AI tool, Hinton answered that he uses GPT-4, the most recent model from OpenAI, whenever he wants to know the answer to anything. "I don't totally trust it, because it can hallucinate," he said, "but on almost everything, it's a not-very-good expert, and that's very useful."" [1]

1. U.S. News: Nobel Prize in Physics Goes to AI Pioneers. Woodward, Aylin.  Wall Street Journal, Eastern edition; New York, N.Y.. 09 Oct 2024: A.3.