Between 2012 and 2018, the amount of computing power used by record-breaking artificial intelligence models doubled every 3.4 months. Even with money pouring into the AI field, this trendline is unsustainable. Because of cost, hardware availability and engineering difficulties, the next decade of AI can't rely exclusively on applying more and more computing power to drive further progress.

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.

Artificial intelligence offers enormous promise to advance progress and powerful capabilities to disrupt it. This policy brief is the second installment of a series that examines how advances in AI could be exploited to enhance operations that automate disinformation campaigns. Building on the RICHDATA framework, this report describes how AI can supercharge current techniques to increase the speed, scale, and personalization of disinformation campaigns.

Artificial intelligence will play an increasingly important role in cyber defense, but vulnerabilities in AI systems call into question their reliability in the face of evolving offensive campaigns. Because securing AI systems can require trade-offs based on the types of threats, defenders are often caught in a constant balancing act. This report explores the challenges in AI security and their implications for deploying AI-enabled cyber defenses at scale.

Artificial intelligence offers enormous promise to advance progress, and powerful capabilities to disrupt it. This policy brief is the first installment of a series that examines how advances in AI could be exploited to enhance operations that automate disinformation. Introducing the RICHDATA framework—a disinformation kill chain—this report describes the stages and techniques used by human operators to build disinformation campaigns.

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.

In science and technology, U.S. federal prize competitions are a way to promote innovation, advance knowledge, and solicit technological solutions to problems. In this report, the authors identify the unique advantages of such competitions over traditional R&D processes, and how these advantages might benefit artificial intelligence research.

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.