Artificial intelligence (AI), an emerging powerhouse in modern society, has transformed fields from medicine and economics to defense and transportation. In science, it's become an indispensable tool for researchers worldwide.
AI's vast computational and predictive capabilities make it a cornerstone in labs and institutions. From detecting exoplanets to diagnosing complex diseases, its applications continue to impress experts.
The quest for new particles drives particle accelerators and fuels both theoretical and experimental physics. Since the late 1980s, physicists have leveraged early AI models, including machine-learning algorithms that form the foundation of artificial neural networks.
Particle accelerators generate millions of collisions, producing massive datasets that demand meticulous analysis. Physicists sift through this data to spot subtle patterns and recurrences in collision byproducts, revealing potential new energy signatures.
This rigorous process enabled LHC scientists at CERN to confirm the Higgs boson by matching observed energy signatures to theoretical predictions.
Particle signatures often hide amid background noise from decay products. At the LHC, for instance, a Higgs boson emerges in just one of every billion proton-proton collisions and decays into particles like photons and muons in a billionth of a picosecond. Reconstructing these events requires pinpointing every involved particle.
Neural networks excel here. In particle detectors, incoming particles like photons, electrons, or hadrons trigger cascades of sub-particles in electronic calorimeters. These cascades vary subtly by origin, and machine-learning algorithms discriminate them precisely—determining, for example, if a photon pair stems from Higgs decay.
While invaluable, AI complements rather than replaces physicists, who apply deep theoretical knowledge to spot anomalies. Yet, as Paolo Calafiura, an analyst at Berkeley National Laboratory in California, notes, AI's role will grow. With plans to increase LHC collisions by 10-fold in 2024, it will become essential.
Social media's explosion has unleashed billions of daily interactions, giving scientists accessible data troves. Psychologist Martin Seligman highlights how AI unlocks insights from this deluge.
At the Pennsylvania Center for Positive Psychology, Seligman and a team of over 20 psychologists, doctors, and data scientists in the World Well-Being Project deploy neural networks and natural language processing to assess population health.
Traditional surveys are resource-intensive, but social data offers scale, speed, and affordability. AI tames this chaos, uncovering individual and collective behavioral patterns.
In a recent study, Seligman's team analyzed 29,000 depression-related Facebook posts. Training on 28,000, their neural network linked word usage to depression severity—and accurately predicted it for the rest based solely on language.
In another study, the algorithm correctly predicted the rate of… (continued on next page)
