In the time you’ve been reading this, the Large Hadron Collider (LHC) has crushed billions of particles. Perhaps we’ll discover more evidence for the exact same thing we discovered yesterday: the Standard Model of particle physics.
For the engineers who built this 27-kilometre-long ring, this consistency is a victory. But for theoretical physicists, it was quite frustrating. As Matthew Hutson reports in AI Hunts for the Next Big Thing in Physics, the field is currently undergoing a quiet crisis. In an email discussing his report, Hutson explained that the standard model, which describes known elementary particles and forces, is not a complete picture. “So theorists proposed new ideas, experimentalists built huge facilities to test them, but despite the vast amount of data, no major progress was made,” Hutson says. “There are important elements of reality that we are completely overlooking.”
That’s why researchers are applying artificial intelligence to particle physics. Hutson explains that he’s not just asking AI to sift through accelerator data to confirm existing theories. They are asking AI to show them the way to theories that were previously unimaginable. “Rather than trying to uphold human-generated theories, unsupervised AI can reveal the unusual and extend our reach into uncharted territory,” he says. By asking AI to report anomalies in the data, researchers hope to find a path to “new physics” that extends the standard model.
At first glance, this article may sound like another “for AI” article. ×” as a story. IEEE spectrumAs the AI editor of , I constantly receive pitches for such articles: AI for drug discovery, AI for agriculture, AI for wildlife tracking. In many cases, what that actually means is faster data processing and automation around the edge. It’s certainly useful, but it’s incremental.
What struck me about Mr. Hutson’s report is that this effort feels different than anything that has come before. Rather than analyzing experimental data after the fact, AI essentially becomes part of the equipment, scanning for subtle patterns and determining what is interesting in real time. At the LHC, detectors record 40 million collisions every second. There was simply no way to store all this data, so engineers had to constantly build filters to determine which events to save for analysis and which to discard. Almost everything is thrown away.
These instantaneous decisions are now increasingly passed to machine learning systems running on field programmable gate arrays (FPGAs) connected to the detectors. The code must run on the chip’s limited logic and memory, and compressing neural networks onto that hardware is not easy. Hutson describes a theorist pleading with an engineer, “Which of my algorithms will fit on your bloody FPGA?”
This moment is part of a much older pattern. As Hutson writes in his article, throughout the history of science, new instruments have opened doors to the unexpected. Galileo’s telescope revealed moons orbiting Jupiter. Early microscopes revealed a whole world of swimming “animals.” These great tools do more than just answer existing questions. They allowed new questions to be asked.
In other words, if there is a crisis in particle physics, it may not just be the loss of particles. It’s about how to see beyond the limits of human imagination. Hutson’s story suggests that while AI may not completely solve the mysteries of the universe, it could change the way we look for answers.
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