In this proof-of-concept project, CSET and Amplyfi Ltd. used machine learning models and Chinese-language web data to identify Chinese companies active in artificial intelligence. Most of these companies were not labeled or described as AI-related in two high-quality commercial datasets. The authors' findings show that using structured data alone—even from the best providers—will yield an incomplete picture of the Chinese AI landscape.

Foreign investors comprise a significant portion of investors in top U.S. AI startups, with China as the leading location. The authors analyze investment data in the U.S. AI startup ecosystem both domestically and abroad, outlining the sources of global investment.

Corporate investors are a significant player in the U.S. AI startup ecosystem, funding 71 percent of top U.S. AI startups. The authors analyze the trends in top corporate funders and the startups receiving corporate money.

Based on news coverage alone, it can seem as if corporations dominate the research on artificial intelligence and machine learning when compared to the work of universities and academia. Authors Simon Rodriguez, Tim Hwang and Rebecca Gelles analyze the data over the past decade of research publications and find that, in fact, universities are the more dominant producers of AI papers. They also find that while corporations do tend to generate more citations to the work they publish in the field, these “high performing” papers are most frequently cross-collaborations with university labs.

As demand for cybersecurity experts in the United States has grown faster than the supply of qualified workers, some organizations have turned to artificial intelligence to bolster their overwhelmed cyber teams. Organizations may opt for distinct teams that specialize exclusively in AI or cybersecurity, but there is a benefit to having employees with overlapping experience in both domains. This data brief analyzes hiring demand for individuals with a combination of AI and cybersecurity skills.

Is there a rift between the U.S. tech sector and the Department of Defense? To better understand this relationship, CSET surveyed U.S. AI industry professionals about their views toward working on DOD-funded AI projects. The authors find that these professionals hold a broad range of opinions about working with DOD. Among the key findings: Most AI professionals are positive or neutral about working on DOD-funded AI projects, and willingness to work with DOD increases for projects with humanitarian applications.

Half of Silicon Valley’s startups have at least one foreign-born founder, and immigrants are twice as likely as native-born Americans to start new businesses. To understand how immigration shapes AI entrepreneurship in particular in the United States, Huang, Arnold and Zwetsloot analyze the 2019 AI 50, Forbes’s list of the “most promising” U.S.-based AI startups. They find that 66 percent of these startups had at least one immigrant founder. The authors write that policymakers should consider lifting some current immigration restrictions and creating new pathways for entrepreneurs.

China’s surge in artificial intelligence development has been fueled, in large part, by advances in computer vision, the AI subdomain that makes powerful facial recognition technologies possible. This data brief compares U.S. and Chinese computer vision patent data to illustrate the different approaches each country takes to AI development.

Understanding AI activity in the private sector is crucial both to grasping its economic and security implications and developing appropriate policy frameworks. This data brief shows particularly robust AI activity in software publishing and manufacturing, along with a high concentration of companies in California, Massachusetts and New York.

Future U.S. competitiveness in artificial intelligence will require a robust AI workforce. This data brief analyzes market demand for AI-related jobs to determine the skills necessary in the field. It concerns jobs considered both “core AI” and “AI-adjacent.”