Data Science

Rebecca Gelles

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Rebecca Gelles is a Data Scientist at Georgetown’s Center for Security and Emerging Technology. Previously, she spent almost seven years at the National Security Agency, where she graduated from the Director’s Summer Program (DSP) and the Cryptanalytic Computer Network Operations Development Program (C2DP) and worked on topics ranging from cryptography to data science to natural language processing to high performance computing. Rebecca holds a B.A. in Computer Science and Linguistics from Carleton College and an M.S. in Computer Science from University of Maryland College Park, where her research focused on how the media influences users’ computer security postures and on new techniques for defending IoT devices from cyber attacks.

CSET’s Private-sector AI-Related Activity Tracker (PARAT) collects data related to companies’ AI research and development to inform analysis of the global AI sector. The global AI market is already expanding rapidly and is likely to continue growing in the coming years. Identifying “AI companies” helps illustrate the size and health of the AI industry in which they participate as well as the most sought-after skills and experience in the AI workforce.

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.

Leading U.S. companies are investing in the broad research field of artificial intelligence (AI), but where, specifically, are they making these investments? This data brief provides an analysis of the research papers published by Amazon, Apple, Facebook, Google, IBM, and Microsoft over the past decade to better understand what work their labs are prioritizing, and the degree to which these companies have similar or different research agendas overall. The authors find that major “AI companies” are often focused on very different subfields within AI, and that the private sector may be failing to make research investments consistent with ensuring long-term national competitiveness.

Foreign investors comprise a significant portion of investors in top U.S. AI startups, with China as the leading location. The authors analyze investment data in the U.S. AI startup ecosystem both domestically and abroad, outlining the sources of global investment.

Corporate investors are a significant player in the U.S. AI startup ecosystem, funding 71 percent of top U.S. AI startups. The authors analyze the trends in top corporate funders and the startups receiving corporate money.

Based on news coverage alone, it can seem as if corporations dominate the research on artificial intelligence and machine learning when compared to the work of universities and academia. Authors Simon Rodriguez, Tim Hwang and Rebecca Gelles analyze the data over the past decade of research publications and find that, in fact, universities are the more dominant producers of AI papers. They also find that while corporations do tend to generate more citations to the work they publish in the field, these “high performing” papers are most frequently cross-collaborations with university labs.

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.

Artificial intelligence is of increasing interest to the private sector, but what exactly constitutes an “AI company?” This data brief offers a flexible, data-driven framework for identifying the companies most relevant in this field at the moment, providing policymakers and researchers with a tool for mapping technology transfer risks and gauging the overall health of America’s AI sector.