We aim to demonstrate the value of mathematical models for policy debates about technological progress in cybersecurity by considering phishing, vulnerability discovery, and the dynamics between patching and exploitation. We then adjust the inputs to those mathematical models to match some possible advances in their underlying technology.

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.

Adversarial patches are images designed to fool otherwise well-performing neural network-based computer vision models. Although these attacks were initially conceived of and studied digitally, in that the raw pixel values of the image were perturbed, recent work has demonstrated that these attacks can successfully transfer to the physical world. This can be accomplished by printing out the patch and adding it into scenes of newly captured images or video footage.

CSET Senior Fellow Andrew Lohn testified before the House of Representatives Homeland Security Subcommittee on Cybersecurity, Infrastructure Protection, and Innovation at a hearing on "Securing the Future: Harnessing the Potential of Emerging Technologies While Mitigating Security Risks." Lohn discussed the application of AI systems in cybersecurity and AI’s vulnerabilities.

As an integral player in advanced semiconductor supply chains, the United States enjoys advantages over China in producing and accessing chips for artificial intelligence and other leading-edge computing technologies. However, a lack of domestic production capacity threatens U.S. semiconductor access. The United States can strengthen its advantages by working with allies and partners to prevent China from producing leading-edge chips and by reshoring its own domestic chipmaking capacity.

The semiconductor industry and the U.S. government are engaged in ambitious plans to expand domestic semiconductor manufacturing capacity. Previous CSET research has found that the CHIPS for America Act incentives, if carefully targeted and augmented by adequate regulatory and workforce support, could reverse the observable decline in U.S. semiconductor manufacturing capacity since 1990. This paper argues that targeted investment incentives to increase U.S.-based advanced packaging capacity are also important for innovation, supply chain security, and ongoing semiconductor industry leadership.

CSET Senior Fellow Andrew Lohn testified before the House of Representatives Science, Space and Technology Subcommittee on Investigations and Oversight and Subcommittee on Research and Technology at a hearing on "Securing the Digital Commons: Open-Source Software Cybersecurity." Lohn discussed how the United States can maximize sharing within the artificial intelligence community while reducing risks to the AI supply chain.

Offshoring the production of semiconductor manufacturing equipment would remove an important source of leverage over China and make the United States more dependent on other countries for some of the most important inputs to semiconductor manufacturing. This brief explores the factors driving U.S. SME firms to offshore production and what can be done to slow or reverse offshoring.

CSET Senior Fellow Andrew Lohn testified before the U.S. Senate Armed Services Subcommittee on Cybersecurity hearing on artificial intelligence applications to operations in cyberspace. Lohn discussed AI's capabilities and vulnerabilities in cyber defenses and offenses.

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.