As dual-use technologies transform the national security landscape, the U.S. Department of Defense has established a variety of offices and programs dedicated to bringing private sector innovation into the military. However, these efforts have largely failed to drive cutting-edge commercial technology into major military platforms and systems. This report examines the shortcomings of the DOD’s current approach to defense innovation and offers recommendations for a more effective strategy.

For the first time, a Chinese company—Huawei—is set to lead the global transition from one key national security infrastructure technology to the next. How did Washington, at the beginning of the twenty-first century, fail to protect U.S. firms in this strategic technology and allow a geopolitical competitor to take a leadership position in a national security relevant critical infrastructure such as telecommunications? This policy brief highlights the characteristics of 5G development that China leveraged, exploited, and supported to take the lead in this key technology. The Huawei case study is in some ways the canary in the coal mine for emerging technologies and an illustration of what can happen to U.S. competitiveness when China’s companies do not have to base decisions on market forces.

CSET Research Analyst Will Hunt testified before the Strategic Technologies and Advanced Research (STAR) Subcommittee on "Microelectronics: Levers for Promoting Security and Innovation." He offered recommendations to advance the U.S.' semiconductor industry.

AI technologies will likely alter great power competitions in foundational ways, changing both how nations create power and their motives for wielding it against one another. This paper is a first step toward thinking more expansively about AI & national power and seeking pragmatic insights for long-term U.S. competition with authoritarian governments.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

As modern machine learning systems become more widely used, the potential costs of malfunctions grow. This policy brief describes how trends we already see today—both in newly deployed artificial intelligence systems and in older technologies—show how damaging the AI accidents of the future could be. It describes a wide range of hypothetical but realistic scenarios to illustrate the risks of AI accidents and offers concrete policy suggestions to reduce these risks.

China’s National Cybersecurity Center (NCC) resides on a 40 km2 plot in Wuhan. As one indication of its significance, the Chinese Communist Party’s highest-ranking members have an oversight committee for the facility. Over the next decade, the NCC will provide the talent, innovation, and indigenization of cyber capabilities that China’s Ministry of State Security, Ministry of Public Security, and People’s Liberation Army Strategic Support Force hacking teams lack. Though still under construction, the NCC’s first class of graduates will cross the stage in June 2022.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

Modern machine learning often relies on open-source datasets, pretrained models, and machine learning libraries from across the internet, but are those resources safe to use? Previously successful digital supply chain attacks against cyber infrastructure suggest the answer may be no. This report introduces policymakers to these emerging threats and provides recommendations for how to secure the machine learning supply chain.

This issue brief explores whether artificial intelligence and AI-related certifications serve as potential pathways to enter the U.S. AI workforce. The authors find that according to U.S. AI occupation job postings data over 2010–2020, there is little demand from employers for AI and AI-related certifications. From this perspective, such certifications appear to present more hype than promise.