Dr. Melissa Flagg is a Senior Fellow at the Center for Security and Emerging Technology (CSET) at Georgetown University. Melissa is also a non-resident senior fellow at The Atlantic Council’s GeoTech Center. Previously she served as the Deputy Assistant Secretary of Defense for Research, responsible for policy and oversight of Defense Department science and technology programs including basic research through advanced technology development and the DoD laboratory enterprise. She has worked at the State Department, the Office of Naval Research, the Office of the Secretary of Defense for Research and Engineering, the John D. and Catherine T. MacArthur Foundation, and the Army Research Laboratory.
Melissa also ran her own consulting business and was the Chief Technology Officer of a small consumer start-up. She has served on numerous boards including the National Academy of Sciences Air Force Studies Board, the Department of Commerce Emerging Technology Research Advisory Committee and the Board of Humanity 2050. She holds a Ph.D. in Pharmaceutical Chemistry and a B.S. in Pharmacy.
U.S. research security requires trust and collaboration between those conducting R&D and the federal government. Most R&D takes place in the private sector, outside of government authority and control, and researchers are wary of federal government or law enforcement involvement in their work. Despite these challenges, as adversaries work to extract science, technology, data and know-how from the United States, the U.S. government is pursuing an ambitious research security initiative. In order to secure the 78 percent of U.S. R&D funded outside the government, authors Melissa Flagg and Zachary Arnold propose a new, public-private research security clearinghouse, with leadership from academia, business, philanthropy, and government and a presence in the most active R&D hubs across the United States.
Machine learning systems’ vulnerabilities are pervasive. Hackers and adversaries can easily exploit them. As such, managing the risks is too large a task for the technology community to handle alone. In this primer, Andrew Lohn writes that policymakers must understand the threats well enough to assess the dangers that the United States, its military and intelligence services, and its civilians face when they use machine learning.
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.
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.”
The United States must adopt a new approach to R&D policy to optimize the diversity of the current system, manage the risks of system dispersion and deliver the benefits of R&D to society. This policy brief provides a new framework for understanding the U.S. R&D ecosystem and recommendations for repositioning the role of the federal government in R&D.
"U.S. S&T policy must now progress from its successful postwar framework to a new framework fit for the twenty-first century," write CSET's Melissa Flagg and Paul Harris in Issues in Science and Technology.
The U.S. government and industry both see artificial intelligence as a pivotal technology for future growth and competitiveness. What skills will be needed to create, integrate, and deploy AI applications? This data brief analyzes market demand for AI-related jobs to determine their educational requirements, dominant sectors, and geographic distribution.
Research and development funding and technological leadership are crucial to sustaining America’s comparative advantages. While the prevailing narrative suggests that China leads in a bipolar competition, in reality, the United States and its allies comprise a majority of global R&D.
With the increasing importance of artificial intelligence and the competition for AI talent, it is essential to understand the U.S. domestic industrial AI landscape. A new CSET data brief maps where AI talent is produced, where it concentrates, and where AI equity funding goes. This mapping reveals distinct AI hubs emerging across the country, with different growth rates, investment levels, and potential access to talent.
To learn more, please review this policy. By continuing to browse the site, you agree to these terms.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.