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.

CSET Research Analyst Dahlia Peterson testified before the U.S.-China Economic and Security Review Commission at a hearing on "China’s Challenges and Capabilities in Educating and Training the Next Generation Workforce."

CSET's Dr. Caroline Schuerger, Dr. Steph Batalis, and Vikram Venkatram submitted this comment with recommendations for the National Biotechnology and Biomanufacturing Initiative.

U.S. policies on artificial intelligence education and the AI workforce must grow, cultivate, attract, and retain the world’s best and brightest. Given China’s role as a producer of AI talent, understanding its AI workforce could provide important insight. This report provides an analysis of the AI workforce demand in China using a novel dataset of 6.8 million job postings. It then outlines potential implications along with future reports in this series.

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.

Universities are the engines that power the AI talent pipeline, but mounting evidence suggests that U.S. computer science departments do not have enough faculty to meet growing student interest. This paper explores the potential mismatch between supply and demand in AI education, discusses possible causes and consequences, and offers recommendations for increasing teaching capacity at U.S. universities.

China is banking on applying AI to biotechnology research in order to transform itself into a “biotech superpower.” In pursuit of that goal, it has emphasized bringing together different aspects of the development cycle to foster multidisciplinary research. This data brief examines the emerging trend of co-location of AI and biotechnology researchers and explores the potential impact it will have on this growing field.

Community and technical colleges offer enormous potential to grow, sustain, and diversify the U.S. artificial intelligence (AI) talent pipeline. However, these institutions are not being leveraged effectively. This report evaluates current AI-related programs and the associated number of graduates. The authors find that few AI and AI-related degrees and certificates are being awarded today. They propose five recommendations to address existing challenges and harness the potential of these institutions to train tomorrow’s AI workforce.

This issue brief uses data from the National Science Foundation’s Survey of Doctorate Recipients to explore how many of the international students who earn STEM PhDs from U.S. universities stay in the country after graduation. The authors trace the journeys that these graduates take through the immigration system and find that most remain in the United States long after earning their degrees.

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.