An Open-Source Method for Assessing National Scientific and Technological Standing
With Applications to Artificial Intelligence and Machine Learning
Published Oct 28, 2021
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.