Event

Mar 15, 2023
Military-Civil Fusion and China’s AI Research Strategies

The People’s Republic of China has prioritized the development of artificial intelligence (AI) throughout all facets of society, ranging from education and training to military modernization and broader national security applications. A primary motivation for the prioritization of AI has been the country’s strategy of Military-Civil Fusion (MCF), which seeks to ensure that new advancements and innovations simultaneously advance the country’s economic and military development. This talk will discuss the intersection of China’s AI policies and MCF strategy. Via case studies, it will also illuminate how these efforts have and may continue to work in tandem.

Address
Max Planck Institute for the History of Science, Boltzmannstraße 22, 14195 Berlin, Germany
Room
Zoom/Online Meeting Platform
Contact and Registration

Please register at the following link: 
https://zoom.us/j/95148903040?pwd=UmNZeUJWMzYxSUw0ZkJMckJFK3V1dz09

This event is part of the LMRG & BCCN Lecture Series "China—The New Science Superpower?" For further information about the series, specific sessions, or questions concerning registration, please contact office-ahlers@mpiwg-berlin.mpg.de.

About This Series

China’s push to become a leading science power is unprecedented in its speed, scope and, arguably, success. Reactions to China’s rise in global science are dichotomous: some anticipate that science made in China may come to dominate global academia while others deem it impossible to achieve scientific leadership under an authoritarian regime. A focus on rankings and statistics alone is apparently not enough to grasp the origins, characteristics, and the possible futures of China as a science superpower.

This monthly lecture series will bring together fresh empirical insights and intriguing theoretical reflections about the development of the science system in the People’s Republic of China and its global integration. Representing a variety of social science perspectives, our guest speakers will explore the evolution of Chinese science policy, interactions of societal norms and values and academia in the PRC, factors that enable or constrain scientific innovation, the global reception of scientific output and investment from China, the securitization of international collaboration, and much more.

EmilyWeinstein_Mar15
2023-03-15T14:00:00SAVE IN I-CAL 2023-03-15 14:00:00 2023-03-15 15:30:00 Military-Civil Fusion and China’s AI Research Strategies Watch this lecture on YouTube The People’s Republic of China has prioritized the development of artificial intelligence (AI) throughout all facets of society, ranging from education and training to military modernization and broader national security applications. A primary motivation for the prioritization of AI has been the country’s strategy of Military-Civil Fusion (MCF), which seeks to ensure that new advancements and innovations simultaneously advance the country’s economic and military development. This talk will discuss the intersection of China’s AI policies and MCF strategy. Via case studies, it will also illuminate how these efforts have and may continue to work in tandem. Emily S. Weinstein Emily S. Weinstein is a Research Fellow at Georgetown’s Center for Security and Emerging Technology (CSET), focused on US national competitiveness in AI/ML technology and US-China technology competition. She is also a Nonresident Fellow at the Atlantic Council’s Global China Hub and the National Bureau of Asian Research. In her previous role at CSET, Emily conducted research on China’s S&T ecosystem, talent flows, and technology transfer issues. She has written on topics related to research security and China’s S&T developments in Foreign Policy, Lawfare, DefenseOne, and other outlets. Emily holds a B.A. in Asian Studies from the University of Michigan and an M.A. in Security Studies from Georgetown University. Max Planck Institute for the History of Science, Boltzmannstraße 22, 14195 Berlin, Germany Zoom/Online Meeting Platform Europe/Berlin public