The National Artificial Intelligence Research Resource (NAIRR) pilot provides federal infrastructure, including computational resources, to U.S. AI researchers. This blog post estimates the compute provided through the pilot’s initial six resources. We find that the total compute capacity of the initial resources is roughly 3.77 exaFLOPS, the equivalent of approximately 5,000 H100 GPUs (using the tensor cores optimal for AI). Factoring in the amount of time these resources are available for use, we find that the overall compute allocated is roughly 3.26 yottaFLOPs. The pilot is a significant first step in providing compute to under-resourced organizations, although it is a fraction of what is available to industry.

Over the past year, artificial intelligence has quickly become a focal point in K-12 education. This blog post describes new and existing K-12 AI education efforts so that U.S. policymakers and other decision-makers may better understand what’s happening in practice.

This blog post assesses how different priorities can change the risk-benefit calculus of open foundation models, and provides divergent answers to the question of “given current AI capabilities, what might happen if the U.S. government left the open AI ecosystem unregulated?” By answering this question from different perspectives, this blog post highlights the dangers of hastily subscribing to any particular course of action without weighing the potentially beneficial, risky, and ambiguous implications of open models.

This guide provides a run-down of CSET’s research since 2019 for first-time visitors and long-term fans alike. Quickly get up to speed on our “must-read” research and learn about how we organize our work.

There’s a lot to digest in the October 30 White House’s AI Executive Order. Our tracker is a useful starting point to identify key provisions and monitor the government’s progress against specific milestones, but grappling with the substance is an entirely different matter. This blog post, focusing on Section 4 of the EO (“Developing Guidelines, Standards, and Best Practices for AI Safety and Security”), is the first in a series that summarizes interesting provisions, shares some of our initial reactions, and highlights some of CSET’s research that may help the USG tackle the EO.

On October 30, 2023, the Biden administration released its long-awaited Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. CSET has broken down the EO, focusing on specific government deliverables. Our EO Provision and Timeline tracker lists which agencies are responsible for actioning EO provisions and their deadlines.

“AI red-teaming” is currently a hot topic, but what does it actually mean? This blog post explains the term’s cybersecurity origins, why AI red-teaming should incorporate cybersecurity practices, and how its evolving definition and sometimes inconsistent usage can be misleading for policymakers interested in exploring testing requirements for AI systems.

Memory safety issues remain endemic in cybersecurity and are often seen as a never-ending source of cyber vulnerabilities. Recently the topic has increased in prominence with the White House Office of the National Cyber Director (ONCD) releasing a request for comments on how to strengthen the open-source ecosystem. But what exactly is memory safety? This blog describes the historical antecedents in computing that helped create one aspect of today’s insecure cyber ecosystem. There will be no quick fixes, but there is encouraging progress towards addressing these long-standing security issues.

Universities can build more inclusive computer science programs by addressing the reasons that students may be deterred from pursuing the field. This blog post explores some of those reasons, features of CS education that cause them, and provides recommendations on how to design learning experiences that are safer and more exploratory for everyone.

The much-anticipated National Cyber Workforce and Education Strategy (NCWES) provides a comprehensive set of strategic objectives for training and producing more cyber talent by prioritizing and encouraging the development of more localized cyber ecosystems that serve the needs of a variety of communities rather than trying to prescribe a blanket policy. This is a much-needed and reinvigorated approach that understands the unavoidable inequities in both cyber education and workforce development, but provides strategies for mitigating them. In this blog post, we highlight key elements that could be easily overlooked.