Abstract:
The limitations inherent in human scientific discovery research necessitate the adoption of research paradigms based on artificial intelligence (AI). Although the rapid development of the AI field holds promising prospects for this new paradigm, enabling AI to mimic human-like scientific discovery remains a significant challenge to be addressed. The theoretical aspect of scientific discovery involves a cycle from data to models and from models back to data. This talk will introduce the challenges and research progress in both directions. Specifically, the talk will cover AI-Newton related to the former and LOCA related to the latter.
Biography:
Yan-Qing Ma is a Boya Distinguished Professor in the School of Physics at Peking University. He earned his bachelor's degree from Wuhan University in 2006 and his Ph.D. from Peking University in 2011. Following his doctorate, he conducted postdoctoral research in the United States at Brookhaven National Laboratory and the University of Maryland. He joined the faculty at Peking University in 2015. His research focuses on quantum field theory, precision tests of the Standard Model, and the application of artificial intelligence in physics. He has authored or co-authored over 70 papers in leading academic journals.
Alternative online link: https://meeting.tencent.com/dm/idzeKK9del2V
ID: 800377750