CERN's Mark Thomson: AI To Revolutionize Fundamental Physics (theguardian.com) 24
An anonymous reader quotes a report from The Guardian: Advanced artificial intelligence is to revolutionize fundamental physics and could open a window on to the fate of the universe, according to Cern's next director general. Prof Mark Thomson, the British physicist who will assume leadership of Cern on 1 January 2026, says machine learning is paving the way for advances in particle physics that promise to be comparable to the AI-powered prediction of protein structures that earned Google DeepMind scientists a Nobel prize in October. At the Large Hadron Collider (LHC), he said, similar strategies are being used to detect incredibly rare events that hold the key to how particles came to acquire mass in the first moments after the big bang and whether our universe could be teetering on the brink of a catastrophic collapse.
"These are not incremental improvements," Thomson said. "These are very, very, very big improvements people are making by adopting really advanced techniques." "It's going to be quite transformative for our field," he added. "It's complex data, just like protein folding -- that's an incredibly complex problem -- so if you use an incredibly complex technique, like AI, you're going to win."
The intervention comes as Cern's council is making the case for the Future Circular Collider, which at 90km circumference would dwarf the LHC. Some are skeptical given the lack of blockbuster results at the LHC since the landmark discovery of the Higgs boson in 2012 and Germany has described the $17 billion proposal as unaffordable. But Thomson said AI has provided fresh impetus to the hunt for new physics at the subatomic scale -- and that major discoveries could occur after 2030 when a major upgrade will boost the LHC's beam intensity by a factor of ten. This will allow unprecedented observations of the Higgs boson, nicknamed the God particle, that grants mass to other particles and binds the universe together. Thomson is now confident that the LHC can measure Higgs boson self-coupling, a key factor in understanding how particles gained mass after the Big Bang and whether the Higgs field is in a stable state or could undergo a future transition. According to Thomson: "It's a very deep fundamental property of the universe, one we don't fully understand. If we saw the Higgs self-coupling being different from our current theory, that that would be another massive, massive discovery. And you don't know until you've made the measurement."
The report also notes how AI is being used in "every aspect of the LHC operation." Dr Katharine Leney, who works on the LHC's Atlas experiment, said: "When the LHC is colliding protons, it's making around 40m collisions a second and we have to make a decision within a microsecond ... which events are something interesting that we want to keep and which to throw away. We're already now doing better with the data that we've collected than we thought we'd be able to do with 20 times more data ten years ago. So we've advanced by 20 years at least. A huge part of this has been down to AI."
Generative AI is also being used to look for and even produce dark matter via the LHC. "You can start to ask more complex, open-ended questions," said Thomson. "Rather than searching for a particular signature, you ask the question: 'Is there something unexpected in this data?'"
"These are not incremental improvements," Thomson said. "These are very, very, very big improvements people are making by adopting really advanced techniques." "It's going to be quite transformative for our field," he added. "It's complex data, just like protein folding -- that's an incredibly complex problem -- so if you use an incredibly complex technique, like AI, you're going to win."
The intervention comes as Cern's council is making the case for the Future Circular Collider, which at 90km circumference would dwarf the LHC. Some are skeptical given the lack of blockbuster results at the LHC since the landmark discovery of the Higgs boson in 2012 and Germany has described the $17 billion proposal as unaffordable. But Thomson said AI has provided fresh impetus to the hunt for new physics at the subatomic scale -- and that major discoveries could occur after 2030 when a major upgrade will boost the LHC's beam intensity by a factor of ten. This will allow unprecedented observations of the Higgs boson, nicknamed the God particle, that grants mass to other particles and binds the universe together. Thomson is now confident that the LHC can measure Higgs boson self-coupling, a key factor in understanding how particles gained mass after the Big Bang and whether the Higgs field is in a stable state or could undergo a future transition. According to Thomson: "It's a very deep fundamental property of the universe, one we don't fully understand. If we saw the Higgs self-coupling being different from our current theory, that that would be another massive, massive discovery. And you don't know until you've made the measurement."
The report also notes how AI is being used in "every aspect of the LHC operation." Dr Katharine Leney, who works on the LHC's Atlas experiment, said: "When the LHC is colliding protons, it's making around 40m collisions a second and we have to make a decision within a microsecond ... which events are something interesting that we want to keep and which to throw away. We're already now doing better with the data that we've collected than we thought we'd be able to do with 20 times more data ten years ago. So we've advanced by 20 years at least. A huge part of this has been down to AI."
Generative AI is also being used to look for and even produce dark matter via the LHC. "You can start to ask more complex, open-ended questions," said Thomson. "Rather than searching for a particular signature, you ask the question: 'Is there something unexpected in this data?'"
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How do you know this?
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I don't know what trump and musk will do, but spending billions on digging more tunnels [home.cern] does indeed seem a bit [science.org] excessive. [spiedigitallibrary.org]
Conned (Score:2)
Everybody at CERN today is vastly more intelligent than the happy chat bot who wants to be your friend. And yet they think this toy is going to figure things out? If the math is too hard then use Mathematica, don't rely upon an AI that doesn't know what true or false means.
This new Gen AI is not designed to do "measuring", much less measuring of Higgs boson self coupling... "We asked it 5 times, and were given the answers of 1, -1, 41, Epstein's constant, and apparently "up yours nerd."
Are you serious? (Score:4, Informative)
The AI he's talking about isn't LLMs. Take a look at AlphaFold [wikipedia.org] and then shut up because you're making your ignorance on this topic very clear to everyone.
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AlphaFold isn't really AI, even if Google says it is. It's protein folding. This tech has been around awhile. Yes, it's great it can search lots of permutations and has access to a huge database, but that's just scaling up algorithms we've already known about.
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AlphaFold excels precisely because it isn't just scaling up algorithms we've already known about.
It is a neural network that was trained with the output from those algorithms.
Neural Networks are universal function approximators. This is also called Machine Learning, or Artificial Intelligence.
The function that is learned in the NN exceeds the performance of all known algorithms.
Only with a lot of data and a clear goal (Score:3)
Advances made by AI like Alphafold have two things: a lot of data and a clear goal.
Now, stuff like the LHC generates a shitload of data in one go but for the AI, you'll need many instances, so that means repeating experiments possibly hundreds or thousands of times. If that's viable then it's OK.
However, the most important restriction is you need to create a clear and definable goal for the AI. AI doesn't think, so if you don't know what your data means then it could be a problem that AI isn't suited for.
Despite all this, this is one of the few fields where I think Ai could be highly useful in making advances.
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As someone who as a physics undergraduate worked on large collider experiments I very much believe that this could be a place where AI could be incredibly useful. Even as an undergraduate I was put to work trying to sift through "rejected" data from particle accelerators. I wrote shit code, at the time learned the basics of scientific Linux, and ultimate probably accomplished nothing. The amount of reject data is vast.
With an AI that is basically a pattern matching on steroids I can believe science can be m
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There was a whole lot written about this alphafold, but so far there have been no results to write home about.
The "applications" section in wikipedia is thinner than the resume of a master's candidate and a master's candidate costs a lot less.
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The results are that it predicts protein folding better than any other known system, by a large margin.
The database that has been produced by it is almost certainly in use in just about every biochemistry lab on the planet.
Innate Primal Urge (Score:2)
Even cavemen had the urge to bang rocks together.
These physicists are no different, they just have bigger boom sticks.
--
You know, I just do whatever feels right to me! - Bruno Mars
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One other difference is that before banging rocks together, we're usually expected to submit a formal paper discussing what we want to bang, how we want to bang it, what pieces we expect to see and why.
Oh, and the stones are much, much smaller although in terms of mass equivalence we're slowly getting to the size of a pebble.
I expect AI to help but.. (Score:3)
tldr; just from the summary this guy is spouting so many snake oily things I don't trust him. We can produce dark matter, We can do 20 times better in 10 years so we advanced 20 years, If you have a big complex problem and throw a complex technique at it you're gonna win, etc. It sounds like little sound bites greatly distorting some kernel of truth that can't get past the oil he has to coat it with to get the people with the money to listen, unfortunately. I expect some kind of AI, or machine learning, already is helping a lot and likely will help a lot more. Maybe as transformative as he suggests, or at least reducing grunt work. But it comes out of the was smelling like "we're also on the cutting edge so give us your money". It's wierd.
Finally, (Score:1)
...we solved Fermi's Para ^~7 #& ` NO CARRIER
This is the proper use of AI (Score:2)
...not the current crop of crap generators
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What do you mean, "nobody knows how it works?" Many people know how it works, that's why you have a device that is based on the principles of quantum mechanics at your fingertips that you used to type your drivel.
Don't project your ignorance on everyone else, please.
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Sure, we can be technical and say, "we know precisely how it works- it calculations trillions of sigmoid functions for matrices that simulate trillions of things we call perceptrons that analogous to neurons."
But ultimately- the function that is encoded within those simulated neural networks is indeed, for all practical purposes, unknowable.
The extent that we "understand what they do", is that they "are governed by the Universal Approximation Theo
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