Editor’s Note: Although this story about the 2024 Nobel Prize in physics winners does not relate to commercial AV specifically, it touches on AI and machine learning. Both of these areas, of course, continue to rise in importance in our circles. We hope you enjoy this dispatch from columnist Alan C. Brawn, whose passion for AI is well known.
For all of us who are “students” of artificial intelligence, the 2024 Nobel Prize in physics has been awarded to John Hopfield and Geoffrey Hinton for their fundamental discoveries in machine learning, which paved the way for how artificial intelligence is used today. For those who don’t know the names, Hopfield is a professor at Princeton University and Hinton is a computer scientist at the University of Toronto.
In an interview with CNN, Mark Pearce, a member of the Nobel committee of physics, praised the recipients for “laying the foundations for the machine learning that powers many of today’s AI-based products and applications. Their work was fundamental in laying the cornerstones for what we experience today as artificial intelligence.”
2024 Nobel Prize in Physics: Interview with Honoree
In a phone interview after the announcement, Hinton said, “AI will have a huge influence on our societies. It will be comparable with the industrial revolution. But instead of exceeding people in physical strength, it’s going to exceed people in intellectual ability. We have no experience of what it’s like to have things smarter than us.” He predicted the technology would revolutionize things such as healthcare, leading to a “huge improvement in productivity.” But he also cautioned, “we also have to worry about a number of possible bad consequences, particularly the threat of these things getting out of control. I am worried that the overall consequence of this might be systems more intelligent than us that eventually take control.”
In a CNN interview Michael Moloney, chief executive of the American Institute of Physics, said the Nobel Prize winners’ work had “transformed science, allowing machine learning systems to process huge amounts of data and allow scientists to spot patterns they would not otherwise be able to see.” Moloney went on to speak about neural networks at the core of AI. He said “People were very excited about these neural networks about 40 years ago… but we didn’t have the technology then to really implement and take advantage of the discoveries. It’s taken time and, now that we do, it’s really accelerated at an enormous rate.”
Discoveries in Machine Learning
The brain works on neural networks, and AI and machine learning mimic how the brain works using artificial neural networks. This technology was developed by Hopfield and Hinton and is based on the structure of the brain. For those of us who are not scientists, here is how it works.
While a brain has neurons, an artificial neural network has nodes with different values. Additionally, while the brain’s neurons communicate with each other through synapses, artificial nodes influence each other through connections. You can train an artificial neural network by developing stronger connections between the nodes, just like you can train the brain.
Mark Pearce recounted how the inventions came to be. “Just as we can wrack our brains for a particular word or fact we rarely use and only dimly remember, artificial neural networks can also search back through the patterns it has saved — thanks to the invention of the Hopfield network in 1982.”. He noted that “Hopfield was curious about whether it was possible to have a physical system which was inspired by the brain, a network of small computational neurons, which were connected together. He was curious whether it was possible to establish learning such a very simple system. And it was actually possible.”
Impact of Hopfield’s Research
After Hopfield published his research, Hinton expanded it using ideas from statistical physics and developed the earliest form of machine learning, called the “Boltzmann machine.”
Pearce added, “In particular, Hinton demonstrated that it was possible to use the networks to find patterns in data. Since the 1980s, the networks have swelled in size. Whereas Hopfield used a network with just 30 nodes – with fewer than 500 parameters linking them — today’s networks, such as those used to power ChatGPT, can contain more than one trillion parameters.”
Pearce explained that “Unlike traditional software, which is akin to following a recipe to bake a cake, an artificial neural network is able to learn by example – drawing on prior knowledge to create new recipes.”
2024 Nobel Prize in Physics: Caveat from One of the “Godfathers”
In an interview, Hinton spoke to the upsides but in spite of being an AI pioneer, he urged caution around the technology. It should be noted that in May 2023, he left his role at Google and decided to in his words “blow the whistle” after worrying about how smart it was becoming.
Hinton told CNN, “I’m just a scientist who suddenly realized that these things are getting smarter than us. I want to sort of blow the whistle and say we should worry seriously about how we stop these things getting control over us.” He warned that AI “knows how to program so it’ll figure out ways of getting around restrictions we put on it. It’ll figure out ways of manipulating people to do what it wants.”
Understanding Benefits and Pitfalls of AI
At the Nobel announcement ceremony, Hinton was asked whether he regrets his work to help create the technology he fears could cause great harm, despite its many potential benefits.
“There’s two kinds of regrets. There’s regrets where you feel guilty because you did something you knew you shouldn’t have done, and then there’s a regret where you did something that you would do again in the same circumstances, but it may in the end not turn out well,” Hinton said. “That second kind of regret I have. In the same circumstances I would do the same again, but I am worried that the overall consequence of this might be systems more intelligent than us that eventually take control.”
What this should tell us is that the genie is out of the bottle. AI is the next giant leap for mankind on a par with the industrial revolution, but it must be understood relative to what it is capable of and be prepared. Forewarned is forearmed. We must find ways to separate fact from fiction. Truth from lies. Real from made up. To paraphrase Hinton’s concerns, we must learn to control AI before it is able to controls us. People, companies, governments — take heed!
Alan C. Brawn, CTS, DSCE, DSDE, DSNE, DCME, DSSP, ISF-C, is principal at Brawn Consulting.