This brief examines trends in patents generated through federally funded research, otherwise known as Bayh-Dole patents. We find that while Bayh-Dole patents make up a small proportion of U.S. patents overall, they are much more common in certain fields, especially in biosciences and national defense related fields. Academic institutions are major recipients of Bayh-Dole patents, and the funding landscape for patent-producing research has shifted since Bayh-Dole came into effect in 1980.

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.

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.

This data brief identifies the most influential AI researchers in the United States between 2010 and 2021 via three metrics: number of AI publications, citations, and AI h-index. It examines their demographic profiles, career trajectories, and research collaboration rates, finding that most are men in the later stages of their career, largely concentrated in 10 elite universities and companies, and that nearly 70 percent of America’s top AI researchers were born abroad.

Artificial intelligence-enabled systems are transforming society and driving an intense focus on what policy and technical communities can do to ensure that those systems are trustworthy and used responsibly. This analysis draws on prior work about the use of trustworthy AI terms to identify 18 clusters of research papers that contribute to the development of trustworthy AI. In identifying these clusters, the analysis also reveals that some concepts, like "explainability," are forming distinct research areas, whereas other concepts, like "reliability," appear to be accepted as metrics and broadly applied.

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.

When the technology and policy communities use terms associated with trustworthy AI, could they be talking past one another? This paper examines the use of trustworthy AI keywords and the potential for an “Inigo Montoya problem” in trustworthy AI, inspired by "The Princess Bride" movie quote: “You keep using that word. I do not think it means what you think it means.”

Creating adequate talent pipelines for the cybersecurity workforce is an ongoing priority for the federal government. Understanding the effectiveness of current education initiatives will help policymakers make informed decisions. This report analyzes the National Centers of Academic Excellence in Cyber (NCAE-C), a consortium of institutions designated as centers of excellence by the National Security Agency. It aims to determine how NCAE-C designated institutions fare compared to other schools in graduating students with cyber-related degrees and credentials.

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.

Biosafety Level-3 laboratories (BSL-3) are an essential part of research infrastructure and are used to develop vaccines and therapies. The research conducted in them provides insights into host-pathogen interactions that may help prevent future pandemics. However, these facilities also potentially pose a risk to society through lab accidents or misuse. Despite their importance, there is no comprehensive list of BSL-3 facilities, or the institutions in which they are housed. By systematically assessing PubMed articles published in English from 2006-2021, this paper maps institutions that host BSL-3 labs by their locations, augmenting current knowledge of where high-containment research is conducted globally.