Advanced artificial intelligence (AI) is poised to transform fundamental physics, offering insights into the universe's fate, as stated by Prof. Mark Thomson, who will lead CERN starting January 1, 2026. According to Thomson, machine learning techniques are facilitating groundbreaking advancements in particle physics, akin to the AI-driven protein structure predictions that earned a Nobel Prize for Google DeepMind last October.
At the Large Hadron Collider (LHC), researchers are employing these strategies to identify rare events crucial to understanding how particles gained mass in the universe's infancy and assessing the potential for catastrophic collapse. Thomson emphasized that these are not minor enhancements but substantial advancements driven by sophisticated AI methodologies.
As CERN advocates for the Future Circular Collider, a proposed 90km facility that would surpass the LHC, skepticism persists regarding the $17 billion project amid a lack of significant findings since the Higgs boson discovery in 2012. However, Thomson noted that AI is reinvigorating the quest for new physics at the subatomic level, with significant discoveries anticipated post-2030, following a major upgrade that will enhance the LHC's beam intensity tenfold.
This upgrade aims to facilitate unprecedented observations of the Higgs boson, a particle integral to mass generation in the universe. Thomson outlined plans to measure Higgs self-coupling, a fundamental property that could illuminate how particles acquired mass just after the Big Bang. Such measurements could provide insights into whether the Higgs field has stabilized or if a drastic transition is on the horizon, potentially leading to significant changes in our universe.
AI is now integral to the LHC's operations, optimizing data collection and interpretation. Dr. Katharine Leney, involved in the LHC's Atlas experiment, highlighted that AI's capabilities have already allowed for more effective data analysis than previously anticipated, advancing the field by decades.
Moreover, the search for dark matter—a mysterious substance believed to constitute a large part of the universe—continues to challenge scientists. Thomson suggested that generative AI could enhance this search by enabling researchers to pose more complex inquiries about the data, moving beyond simple signatures to uncover unexpected findings.