Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the fourth in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

This brief illuminates the scale of Chinese government funding for higher education, science, and technology by exploring budget and expense reports for key government organizations and 34 of China’s most elite “Double First Class” universities. Chinese political leaders view elite universities as key components of the country’s military modernization, economic growth, and soft power; a situation that presents security risks for international partners.

The strength of a country’s talent pipeline depends in no small part on the quality of its universities. This data brief explores how Chinese and U.S. universities perform in two different global university rankings, why their standings have changed over time, and what those trends mean for graduates.

In the past decade, Chinese researchers have become increasingly prolific authors of highly cited AI publications, approaching the global research share of their U.S. counterparts. However, some analysts question the impact of Chinese publications; are they well respected internationally, and do they cover important topics? In this data brief, the authors build on prior analyses of top AI publications to provide a richer understanding of the two countries’ contributions to high-impact AI research.

This research agenda provides a roadmap for the next phase of CSET’s line of research on the U.S. AI workforce. Our goal is to assist policymakers and other stakeholders in the national security community to create policies that will ensure the United States maintains its competitive advantage in AI talent. We welcome comments, feedback and input on this vision at cset@georgetown.edu.

Advances in robotics technology are having a transformative effect on how people work, travel, communicate, and fight wars. This data brief provides an overview of global trends in robotics patents between 2005 and 2019, focusing in particular on the state of robotics patenting in Russia, as well as developments in military robotics patents both in Russia and across the globe.

This report examines nearly 350 artificial intelligence-related equipment contracts awarded by the People’s Liberation Army and state-owned defense enterprises in 2020 to assess how the Chinese military is adopting AI. The report identifies China’s key AI defense industry suppliers, highlights gaps in U.S. export control policies, and contextualizes the PLA’s AI investments within China’s broader strategy to compete militarily with the United States.

Created through a joint partnership between CSET and the AI Education Project, the AI Education Catalog aims to raise awareness of the AI-related programs available to students and educators, as well as to help inform AI education and workforce policy.

This policy brief addresses the need for a clearly defined artificial intelligence education and workforce policy by providing recommendations designed to grow, sustain, and diversify the U.S. AI workforce. The authors employ a comprehensive definition of the AI workforce—technical and nontechnical occupations—and provide data-driven policy goals. Their recommendations are designed to leverage opportunities within the U.S. education and training system while mitigating its challenges, and prioritize equity in access and opportunity to AI education and AI careers.

This policy brief, authored in collaboration with the MITRE Corporation, provides a new perspective on the U.S. Department of Defense’s struggle to recruit and retain artificial intelligence talent. The authors find that the DOD already has a cadre of AI and related experts, but that this talent remains hidden. Better leveraging this talent could go a long way in meeting the DOD’s AI objectives. The authors argue that this can be done through policies that more effectively identify AI talent and assignment opportunities, processes that incentivize experimentation and changes in career paths, and investing in the necessary technological infrastructure.