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

• URL: http://arxiv.org/abs/2003.02763v2
• Date: Thu, 11 Jun 2020 14:35:39 GMT
• Title: A Quantitative History of A.I. Research in the United States and China
• Authors: Daniel Ish, Andrew Lohn, Christian Curriden
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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