James Dunham is a Data Scientist at the Center for Security and Emerging Technology (CSET). He collaborates with subject-matter experts to solve measurement and inference problems, and specializes in natural language processing methods for scientific text. His substantive focus is on the AI research landscape and workforce. James was previously at the Massachusetts Institute of Technology, where his doctoral work addressed measurement problems in political science. He has also worked on open civic data at the MIT Election Lab and developed survey methods at MIT’s Political Experiments Research Lab. He is an Adjunct Assistant Professor of Public Service at New York University. James holds a B.A. from the University of Wisconsin, an M.P.A. from New York University, and a Ph.D. from MIT.

Over the last decade, Moscow has boosted funding of universities and implemented reforms in order to make Russia a global leader in AI. As part of that effort, Russian researchers have expanded their English-language publication output, a key—if imperfect—measure of the country’s innovation and impact. Between 2010 and 2018, the number of English-language publications by Russian scientists in AI-related fields increased six-fold.

To better understand immigration paths of the AI workforce, CSET surveyed recent PhD graduates from top-ranking AI programs at U.S. universities. This data brief offers takeaways — namely, that AI PhDs find the United States an appealing destination for study and work, and those working in the country plan to stay.

Are great powers engaged in an artificial intelligence arms race? This issue brief explores the rhetorical framing of AI by analyzing more than 4,000 English-language articles over a seven-year period. Among its findings: a growing number of articles frame AI development as a competition, but articles using the competition frame represent a declining proportion of articles about AI.

The United States faces increased international competition for top talent in artificial intelligence, a critical component of the American AI advantage. CSET surveyed recent AI PhDs from U.S. universities, offering insights into the academic and career preferences of the AI workforce.

The task of artificial intelligence policymaking is complex and challenging, made all the more difficult by such a rapidly evolving technology. In order to address the security and economic implications of AI, policymakers must be able to viably define, categorize and assess AI research and technology. In this issue brief, CSET puts forward a functional definition of AI, based on three core principles, that significantly outperforms methods developed over the last decade.

Policymakers continue to debate the ability of the United States to attract and retain top international talent. This Issue Brief assesses how many international Ph.D. graduates across various STEM fields and nationalities intend to stay in the United States after completing their degrees.

Talent is core to U.S. competitiveness in artificial intelligence, and international graduate students are a large source of AI talent for the United States. Retaining them in this country as they transition into the workforce is key. Graduate student retention has historically been a core U.S. strength, but that strength is endangered by recent events.