A Quantitative History of A.I. Research in the United States and China
This provides a quantitative historical perspective on AI research competition between the U.S. and China, which is incremental as it applies existing methods to new data.
The paper analyzes 60 years of AI publication data from the U.S. and China, finding that both countries show regular growth in volume, with China initially growing faster but now having similar rates to the U.S., and a shift in topics around 1990 linked to neural networks, with China's topics lagging the U.S. until recent decades.
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.