Abstract: This year’s Nobel Prize in Physics celebrates the transformative contributions of John Hopfield and Geoffrey Hinton, “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” The award illustrates the deep connections between physics and AI. While physics seeks to discover universal laws—ultimately aspiring toward a "theory of everything" that explains phenomena from subatomic particles to galaxies—AI focuses on representing and identifying specific patterns to enhance prediction and decision-making. Hopfield and Hinton's work exemplifies how physics-inspired thinking has shaped neural networks, from the associative memory models rooted in the Ising model to the probabilistic learning framework of restricted Boltzmann machines. Although AI has since evolved toward data-driven approaches, the foundational influence of physics remains evident. Moreover, AI is now driving breakthroughs in chemistry, biology, and physics itself, notably in protein folding and complex differential equations. This ongoing synergy between AI and the physical sciences offers a glimpse into the future, where new opportunities at their intersection will further advance both our understanding of physical phenomena and our ability to solve practical problems through intelligent systems.

Bio: Dong Xu is Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his Ph.D. in Physics from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016. Over the past 30 years, he has conducted research in many areas of computational biology and bioinformatics, including single-cell data analysis, protein structure prediction and modeling, protein post-translational modifications, protein localization prediction, computational systems biology, biological information systems, and bioinformatics applications in human, microbes, and plants. His research since 2012 has focused on the interface between bioinformatics and deep learning. He has published more than 500 papers with more than 27,000 citations and an H-index of 84 according to Google Scholar. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015 and American Institute for Medical and Biological Engineering (AIMBE) Fellow in 2020.