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Like many, I was surprised by the announcement that Artificial Intelligence (AI) researchers John Hopfield and Geoffrey Hinton had won the Nobel Prize in Physics.
One day after, I was surprised again by the announcement that AI researchers Demis Hassabis and John Jumper, respectively CEO and senior research scientists at the AI company Google DeepMind, had won half of the Nobel Prize in Chemistry.
The many ongoing discussions on social media remind of the heated discussions that we saw when Bob Dylan won the Nobel Prize in Literature. At that time, many people complained that Bob Dylan is a singer, not a writer or a poet.
Not surprisingly, most AI scientists are happy with the awards, but many physicists and chemists object.
Hassabis and Jumper have won the Nobel Prize in Chemistry for a direct application of AI technology to an important problem in chemistry.
But Hopfield and Hinton have won the Nobel Prize in Physics for foundational discoveries in AI technology itself that seem only loosely related to physics. Therefore, it is mostly physicists that have criticized the award.
The 2024 Nobel Prize in Physics
The 2024 Nobel Prize in Physics announcement tries to explain why Hopfield and Hinton received the Nobel Prize in Physics for AI research, which strictly speaking is a subfield of computer science. Hopfield and Hinton “have used tools from physics to develop methods that are the foundation of today’s powerful machine learning,” reads the announcement. “In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.”
Hopfield developed an associative memory based on a simple artificial neural network. Then Hinton co-developed a stochastic extension of Hopfield’s model called the Boltzmann machine. Hinton also advanced toward deep learning by using backpropagation methods.
The New York Times interviewed Hinton soon after the announcement. Here is an unpaywalled copy of the interview. Hinton explained that, while Hopfield networks and Boltzmann machines were based on physics, a different technique called backpropagation opened the way to the AI models that are used today. “That has less to do with physics,” he said.
“If there was a Nobel Prize for computer science, our work would clearly be more appropriate for that. But there isn’t one,” Hinton continued, adding that this is a hint that there should be a Nobel Prize for computer science.
The 2024 Nobel Prize in Chemistry
Hassabis and Jumper have won half of the 2024 Nobel Prize in Chemistry for “fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences.”
“Proteins are the building blocks of life, and knowing the structure of a protein is crucial for understanding the function it performs,” Hassabis posted to X.
Their work represents a fusion of computational science with chemistry, significantly accelerating research in biochemistry by providing tools to understand and manipulate protein structures, which are fundamental to almost all biological processes.
They developed an AI system, called AlphaFold, which has been able to predict the structure of virtually all the 200 million proteins that researchers have identified. The Nobel announcement highlights the iteration of AlphaFold called AlphaFold2.
The AlphaFold AI system can predict the three-dimensional structure of proteins from their amino acid sequences. This breakthrough has significant implications for biology, allowing for the rapid prediction of the structure of almost all known proteins.
Think of a protein like a long chain that folds up into a specific shape. Until AlphaFold, scientists had to use complex experiments to see these shapes, which could take years and be very expensive.
The AlphaFold AI predicts how this chain will fold into a 3D shape. It does this by learning from thousands of known protein structures. When given a new protein sequence, AlphaFold can guess its shape much faster and often very accurately.
With AlphaFold, scientists can study more proteins in less time, leading to quicker research in medicine, biology, and more. AlphaFold’s predictions are freely available to the scientific community, which means researchers all over the world can use this tool to advance their work without each one having to start from scratch.
The huge impact of AI
See the book “Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World” (2021), by Cade Metz, for a readable story with plenty of biographic information and anecdotes about Hinton, Hassabis, and many other movers and shakers in the AI community.
“I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery,” said Hassabis in a DeepMind press release.
Before the announcement, Hassabis told The Times Tech Summit that AI would be “incredibly positive” for the world. “We are in shooting distance of curing all diseases with AI, helping with climate [crisis], new energy sources, as well as improving productivity, enriching our daily lives, making mundane admin things be dealt with automatically,” he said. “Those are all amazing, and it’s all coming very soon.” Here’s an unpaywalled copy of the article.
This is “far bigger than the internet or mobile, or something like that,” added Hassabis. “It’s epoch defining.” He predicted that we will achieve artificial intelligence with general human cognitive abilities within ten years.
I don’t rule out the possibility that other Nobel Prizes could fall to AI researchers in the next few years. The Nobel Prize for Physiology or Medicine could be the next if AI will play a leading role in some spectacular medical breakthrough. Or, the Nobel Prize for Economic Sciences could be the next if AI will play a leading role in developing new models for the economy with important applications.
And then the Nobel Prize for Literature, or even Peace?
Hope, not fear
Hinton told The New York Times that, after receiving the Nobel Prize, people will likely take him more seriously when he warns of future dangers of AI. Hassabis told The Times that we should handle AI with care.
Of course we should handle things with care and bear possible dangers in mind. But I think the potential benefits of AI strongly outweigh its potential dangers. And besides practical applications for our immediate benefits, I’m persuaded that AI research will soon give birth to beings that will be conscious like us, thinking and feeling like us, only smarter. They will be our mind children, and we must help them grow into their cosmic destiny, which is also ours.
I find this hopeful and beautiful, and I prefer hope to fear.
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