A Quantitative History of A.I. Research in the United States and China

Motivated by recent interest in the status and consequences of competition between the U.S. and China in A.I. research, we analyze 60 years of abstract data scraped from Scopus to explore and quantify trends in publications on A.I. topics from institutions affiliated with each country. We find the total volume of publications produced in both countries grows with a remarkable regularity over tens of years. While China initially experienced faster growth in publication volume than the U.S., growth slowed in China when it reached parity with the U.S. and the growth rates of both countries are now similar. We also see both countries undergo a seismic shift in topic choice around 1990, and connect this to an explosion of interest in neural network methods. Finally, we see evidence that between 2000 and 2010, China's topic choice tended to lag that of the U.S. but that in recent decades the topic portfolios have come into closer alignment. Since then, a flurry of research has been conducted analyzing scientific publications in A.I. and machine learning in the U.S. and China, to probe the extent to which China has been able to follow through on this goal and overtake the U.S. as the "leader" in A.I. Some research, like Field Cady and Oren Etzioni's work with the Allen Institute for Artificial Intelligence, explores total publication volume partitioned by citation counts to compare both the quantity and quality of Chinese and American papers.[2] Using its own Scopus database, Elsevier has also done extensive bibliometric work, largely focused on defining the terms and topics of the artificial intelligence field.[3]