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 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.

AI capabilities are evolving quickly and pose novel—and likely significant—risks. In these rapidly changing conditions, how can policymakers effectively anticipate and manage risks from the most advanced and capable AI systems at the frontier of the field? This Roundtable Report summarizes some of the key themes and conclusions of a July 2023 workshop on this topic jointly hosted by CSET and Google DeepMind.

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.

Australia, Canada, Japan, the United Kingdom, and the United States emphasize principles of accountability, explainability, fairness, privacy, security, and transparency in their high-level AI policy documents. But while the words are the same, these countries define each of these principles in slightly different ways that could have large impacts on interoperability and the formulation of international norms. This creates, what we call the “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.”

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.

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

CSET Senior Fellow Anna Puglisi testifies before the Senate Energy and Natural Resources Committee on the DOE lab complex and the research security threats it faces.

Translation Snapshots are short posts that highlight related translations produced by CSET’s in-house translation team. Each snapshot identifies relevant translations, provides short summaries, and links to the full translations. Check back regularly for additional Translation Snapshots highlighting our work.