Demand for talent is one of the core elements of technological competition between the United States and China. In this issue brief, we explore demand signals in China’s domestic AI workforce in two ways: geographically and within the defense and surveillance sectors. Our exploration of job postings from Spring 2021 finds that more than three-quarters of all AI job postings are concentrated in just three regions: the Yangtze River Delta region, the Pearl River Delta, and the Beijing-Tianjin-Hebei area.

This data brief analyzes over 200,000 U.S. government grants awarded to industry and academic recipients for artificial intelligence research between January 2017 and May 2023. The authors find that while the majority of federal grants are awarded to academic recipients, industry played an outsized role in U.S. government grant funding of AI research. Moreover, departments within the U.S. Department of Defense appear to prioritize funding industry and AI research relative to other funding agencies.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the second installment of a two-part series of data snapshots that explores export control data from the Bureau of Industry and Security (BIS), namely the 2021 and 2022 yearly reports for trade with China and Hong Kong.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the first installment of a two-part series of data snapshots that explores export control data from the Bureau of Industry and Security (BIS), namely the 2021 and 2022 yearly reports for trade with China and Hong Kong.

This report summarizes recent DOD digital workforce trends as an update to our 2021 report. We expanded our definition of AI talent to include data, analytics, software, and AI, referred to here as the “digital workforce,” to be more aligned with Department needs and current workforce planning efforts. We find that the Department of Defense continues to struggle with their ability to define, identify, develop, assign, promote, and retain digital talent.

How can policymakers credibly reveal and assess intentions in the field of artificial intelligence? Policymakers can send credible signals of their intent by making pledges or committing to undertaking certain actions for which they will pay a price—political, reputational, or monetary—if they back down or fail to make good on their initial promise or threat. Talk is cheap, but inadvertent escalation is costly to all sides.

This explainer overviews techniques to produce smaller and more efficient language models that require fewer resources to develop and operate. Importantly, information on how to leverage these techniques, and many of the subsequent small models, are openly available online for anyone to use. The combination of both small (i.e., easy to use) and open (i.e., easy to access) could have significant implications for artificial intelligence development.

In collaboration with colleagues from CNAS and the Atlantic Council, CSET Researchers Ngor Luong and Emily Weinstein provided this comment in request to Treasury's Advanced Notice of Rule-making request for public comment (TREAS-DO-2023-0009-0001).

This report summarizes more than 20 CSET reports, translations, and data analyses to provide insight into the steps China has taken to increase its technological competitiveness beyond its own borders.

This report summarizes more than 20 CSET reports, translations, and data analyses to provide insight into China’s internal actions to advance and implement its technology-related policy goals