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.
한국 AI 생태계 분석August 2023
This is a Korean translation of the August 2023 CSET Data Brief "Assessing South Korea's AI Ecosystem."
Assessing South Korea’s AI EcosystemAugust 2023
This data brief examines South Korea’s progress in its development of artificial intelligence. The authors find that the country excels in semiconductor manufacturing, is a global leader in the production of AI patents, and is an important contributor to AI research. At the same time, the AI investment ecosystem remains nascent and despite having a highly developed AI workforce, the demand for AI talent may soon outpace supply.
U.S. and Chinese Military AI PurchasesAugust 2023
This data brief uses procurement records published by the U.S. Department of Defense and China’s People’s Liberation Army between April and November of 2020 to assess, and, where appropriate, compare what each military is buying when it comes to artificial intelligence. We find that the two militaries are prioritizing similar application areas, especially intelligent and autonomous vehicles and AI applications for intelligence, surveillance and reconnaissance.
“The Main Resource is the Human”April 2023
Progress in artificial intelligence (AI) depends on talented researchers, well-designed algorithms, quality datasets, and powerful hardware. The relative importance of these factors is often debated, with many recent “notable” models requiring massive expenditures of advanced hardware. But how important is computational power for AI progress in general? This data brief explores the results of a survey of more than 400 AI researchers to evaluate the importance and distribution of computational needs.
Measuring AI DevelopmentDecember 2021
By combining a versatile and frequently updated bibliometrics tool — the CSET Map of Science — with more hands-on analyses of technical developments, this brief outlines a methodology for measuring the publication growth of AI-related topics, where that growth is occurring, what organizations and individuals are involved, and when technical improvements in performance occur.
Mapping the AI Investment Activities of Top Global Defense CompaniesOctober 2021
Militaries around the world have often relied on the largest global defense companies to acquire and integrate cutting-edge technologies. This issue brief examines the investment and mergers and acquisition activities in artificial intelligence of the top 50 global defense companies — a key, if limited, approach to accessing AI innovation in the commercial sector — and assesses investment trends of their corporate venture capital subsidiaries and offers a geographic breakdown of defense companies and their AI target companies.
PARAT – Tracking the Activity of AI CompaniesJune 2021
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.
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.
Mapping Research Agendas in U.S. Corporate AI LaboratoriesApril 2021
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.
From China to San Francisco: The Location of Investors in Top U.S. AI StartupsFebruary 2021
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 in Top U.S. AI StartupsFebruary 2021
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.
Comparing Corporate and University Publication Activity in AI/MLJanuary 2021
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.
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.
Identifying AI-Related CompaniesJuly 2020
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.