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.

Technical talent is vital to innovation and economic growth, and attracting these highly mobile workers is critical to staying on the cutting-edge of the technological frontier. Conventional wisdom holds that the defense community generally struggles to access this talent pool. This policy brief uses LinkedIn data to track the movement of tech workers between industries and metro areas, with a particular focus on the U.S. Department of Defense, the defense industrial base, and the so-called “Big Tech” companies.

CSET submitted the following comment in response to a Request for Information (RFI) from the National Science Foundation (NSF) about the development of the newly established Technology, Innovation, and Partnerships (TIP) Directorate, in accordance with the CHIPS and Science Act of 2022.

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.

China has poured resources into improving the resilience of its space architecture. But how much progress has Beijing made? This issue brief analyzes China’s space resilience efforts and identifies areas where the United States may need to invest to keep pace.

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.

U.S. tech companies have played a critical role in the international effort to support and defend Ukraine against Russia. To better understand and envision how these companies can help U.S. strategic interests, CSET convened a group of industry experts and former government leaders to discuss lessons learned from the ongoing war in Ukraine and what those lessons might mean for the future. The workshop’s discussion and this accompanying report expand on the themes explored in the October 2022 "Foreign Affairs" article, "Big Tech Goes to War."

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This five-part series uses data from the U.S. Department of Education and other select sources to complement existing CSET work on the U.S. AI workforce.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This five-part series uses data from the U.S. Department of Education and other select sources to complement existing CSET work on the U.S. AI workforce.