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AI COMES TO THE NOBELS: DOUBLE WIN SPARKS DEBATE ABOUT SCIENTIFIC
FIELDS  
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Davide Castelvecchi, Ewen Callaway and Diana Kwon
October 10, 2024
Nature [[link removed]]

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_ While many researchers celebrated this year’s chemistry and
physics prizes, others were disappointed by the focus on computational
methods. _

AlphaFold accession number: AF-C1A9D3-F1, Karen Arnott/EMBL-EBI,
Creative Team/EMBL

 

Nobel committees recognized the transformative power of artificial
intelligence (AI) in two of this year’s prizes —
honouring pioneers of neural networks in the physics prize
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the developers of computational tools to study and design proteins
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prize. But not all researchers are happy.

Moments after the Royal Swedish Academy of Sciences unveiled the
winners of this year’s physics Nobel, social media lit up, with
several physicists arguing that the science underlying machine
learning, celebrated in the awards to Geoffrey Hinton and John
Hopfield, was not actually physics.

“I’m speechless. I like machine learning and artificial neural
networks as much as the next person, but hard to see that this is a
physics discovery,” Jonathan Pritchard, an astrophysicist at
Imperial College London wrote on X
[[link removed]]. “Guess the
Nobel got hit by AI hype.”

The research by Hinton, at the University of Toronto in Canada, and
Hopfield at Princeton University in New Jersey, “falls into the
field of computer science,” says Sabine Hossenfelder, a physicist at
the Munich Center for Mathematical Philosophy in Germany. “The
annual Nobel Prize is a rare opportunity for physics — and
physicists with it — to step into the spotlight. It's the day when
friends and family remember they know a physicist and maybe go and ask
him or her what this recent Nobel is all about. But not this year.”

Bringing fields together

Not everyone was troubled, however: many physicists welcomed the news.
“Hopfield and Hinton's research was interdisciplinary, bringing
together physics, math, computer science and neuroscience,” says
Matt Strassler, a theoretical physicist at Harvard University in
Cambridge, Massachusetts. “In that sense, it belongs to all of these
fields.”

Anil Ananthaswamy, a science writer based in Berkeley, California, and
author of the book _Why Machines Learn_, points out that although the
research cited by the Nobel committee might not be theoretical physics
in the purest sense, it is rooted in techniques and concepts from
physics, such as energy. The ‘Boltzmann networks’ invented by
Hinton and the Hopfield networks “are both energy-based models”,
he says.

The connection with physics became more tenuous in subsequent
developments in machine learning, Ananthaswamy adds, particularly in
the ‘feed-forward’ techniques that made neural networks easier to
train. But physics ideas are making a comeback, and are helping
researchers understand why the increasingly complex deep-learning
systems do what they do. “We need the way of thinking we have in
physics to study machine learning,” says Lenka Zdeborová, who
studies the statistical physics of computation at the Swiss Federal
Institute of Technology in Lausanne.

“I think that the Nobel prize in physics should continue to spread
into more regions of physics knowledge,” says Giorgio Parisi, a
physicist at the Sapienza University of Rome who shared the 2021
Nobel [[link removed]]. “Physics
is becoming wider and wider, and it contains many areas of knowledge
that did not exist in the past, or were not part of physics.”

Not just AI

Computer science seemed to be completing its Nobel take-over the day
after the physics prize announcement, when Demis Hassabis and John
Jumper, co-creators of the protein-folding prediction AI tool
AlphaFold [[link removed]] at
Google DeepMind in London, won half of the chemistry Nobel. (The other
half was awarded to David Baker at the University of Washington in
Seattle for protein-design work that did not employ machine learning).

The prize was a recognition of the disruptive force of AI, but also of
the steady accumulation of knowledge in structural and computational
biology, says David Jones, a bioinformatician at University College
London, who collaborated with DeepMind on the first version of
AlphaFold. “I don’t think AlphaFold involves any radical change in
the underlying science that wasn’t already in place,” he says.
“It’s just how it was put together and conceived in such a
seamless way that allowed AlphaFold to reach those heights.”

For example, one key input AlphaFold uses is the sequences of related
proteins from different organisms, which can identify amino acid pairs
that have tended to co-evolve and therefore might be in close physical
proximity in a protein’s 3D structure. Researchers were already
using this insight to predict protein structures at the time AlphaFold
was developed, and some even began embedding the idea in deep learning
neural networks.

“It wasn't just that we went to work and we pressed the AI button,
and then we all went home,” Jumper said at a press briefing at
DeepMind on 9 October. “It was really an iterative process where we
developed, we did research, we tried to find the right kind of
combinations between what the community understood about proteins and
how do we build those intuitions into our architecture.”

AlphaFold also would not have been possible were it not for the
Protein Data Bank, a freely available repository of more than 200,000
protein structures — including some that have contributed to
previous Nobels — determined using X-ray crystallography,
cryo-electron microscopy and other experimental methods. “Each data
point is years of effort from someone,” Jumper said.

Since they were first awarded in 1901, the Nobels have often been
about the impact of research on society, and have rewarded practical
inventions, not only pure science. In this respect, the 2024 prizes
are not outliers, says Ananthaswamy. “Sometimes they are given for
very good engineering projects. That includes the prizes for lasers
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_doi: [link removed]

_More articles by _

* Davide Castelvecchi
[[link removed]], 
* Ewen Callaway
[[link removed]] & 
* Diana Kwon
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Nature [[link removed]] is a weekly
international journal publishing the finest peer-reviewed research in
all fields of science and technology on the basis of its originality,
importance, interdisciplinary interest, timeliness, accessibility,
elegance and surprising conclusions. Nature also provides rapid,
authoritative, insightful and arresting news and interpretation of
topical and coming trends affecting science, scientists and the wider
public. Nature's mission statement: First, to serve scientists through
prompt publication of significant advances in any branch of science,
and to provide a forum for the reporting and discussion of news and
issues concerning science. Second, to ensure that the results of
science are rapidly disseminated to the public throughout the world,
in a fashion that conveys their significance for knowledge, culture
and daily life.

* artificial intelligence
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* Nobel Prize
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* protein folding
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