James Dunham is an NLP Engineer at the Center for Security and Emerging Technology (CSET). He collaborates with subject-matter experts to solve prediction and inference problems, and specializes in natural language processing methods for scientific text. James was previously at the Massachusetts Institute of Technology, where his doctoral work addressed measurement challenges in political science. He has also worked on open civic data at the MIT Election Lab; developed survey methods at MIT’s Political Experiments Research Lab; and been 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.
Identifying AI ResearchJuly 2023
The choice of method for surfacing AI-relevant publications impacts the ultimate research findings. This report provides a quantitative analysis of various methods available to researchers for identifying AI-relevant research within CSET’s merged corpus, and showcases the research implications of each method.
The Long-Term Stay Rates of International STEM PhD GraduatesApril 2022
This issue brief uses data from the National Science Foundation’s Survey of Doctorate Recipients to explore how many of the international students who earn STEM PhDs from U.S. universities stay in the country after graduation. The authors trace the journeys that these graduates take through the immigration system and find that most remain in the United States long after earning their degrees.
Trends in AI Research for the Visual Surveillance of PopulationsJanuary 2022
Progress in artificial intelligence has led to growing concern about the capabilities of AI-powered surveillance systems. This data brief uses bibliometric analysis to chart recent trends in visual surveillance research — what share of overall computer vision research it comprises, which countries are leading the way, and how things have varied over time.
Headline or Trend Line?August 2021
Chinese and Russian government officials are keen to publicize their countries’ strategic partnership in emerging technologies, particularly artificial intelligence. This report evaluates the scope of cooperation between China and Russia as well as relative trends over time in two key metrics of AI development: research publications and investment. The findings expose gaps between aspirations and reality, bringing greater accuracy and nuance to current assessments of Sino-Russian tech cooperation.
Contending FramesMay 2021
The narrative of an artificial intelligence “arms race” among the great powers has become shorthand to describe evolving dynamics in the field. Narratives about AI matter because they reflect and shape public perceptions of the technology. In this issue brief, the second in a series examining rhetorical frames in AI, the authors compare four narrative frames that are prominent in public discourse: AI Competition, Killer Robots, Economic Gold Rush and World Without Work.
Russian AI Research 2010-2018October 2020
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.
Immigration Pathways and Plans of AI TalentSeptember 2020
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.
Mainframes: A Provisional Analysis of Rhetorical Frames in AIAugust 2020
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.
Career Preferences of AI TalentJune 2020
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.
AI Definitions Affect PolicymakingJune 2020
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.
Trends in U.S. Intention-to-Stay Rates of International Ph.D. Graduates Across Nationality and STEM FieldsApril 2020
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.
Keeping Top AI Talent in the United StatesDecember 2019
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.