An Open-Source Method for Assessing National Scientific and Technological Standing

With Applications to Artificial Intelligence and Machine Learning

by Jon Schmid

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The author of this report develops a quick-turn and open-source methodology for assessing national standing in science and technology (S&T) for a given field. The approach entails the calculation of four metrics: high-impact publications, collaborative network density, quality-adjusted patents, and S&T organizational capacity for an analyst-defined S&T area. Following its presentation, the methodology is applied to the field of artificial intelligence and machine learning for nine countries: Germany, France, the United Kingdom, South Korea, Japan, India, Russia, China, and the United States. Using this approach, the author finds that the United States ranks first in three metrics (high-impact publications, collaborative network density, and S&T organizational capacity) and second to China in quality-adjusted patents. The author concludes the report by using the data collected to implement the methodology to explore three additional topics: international patterns of collaboration, the role and research foci of particular organizations, and an application area: the intersection of artificial intelligence and cybersecurity technology.

This research was sponsored by the Department of Defense and conducted within the Cyber and Intelligence Policy Center of the RAND National Security Research Division (NSRD).

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