This year, CSET researchers returned to the DEF CON cybersecurity conference to explore how understandings of AI red-teaming practices have evolved among cybersecurity practitioners and AI experts. This blog post, a companion to "How I Won DEF CON’s Generative AI Red-Teaming Challenge", summarizes our takeaways and concludes with a list of outstanding research questions regarding AI red-teaming, some of which CSET hopes to address in future work.

In August 2024, CSET Research Fellow Colin Shea-Blymyer attended DEF CON, the world’s largest hacking convention to break powerful artificial intelligence systems. He participated in the AI red-teaming challenge, and won. This blog post details his experiences with the challenge, what it took to win the grand prize, and what he learned about the state of AI testing.

This blog post analyzes five major Chinese AI governance proposals, focusing on the key actors specified in each proposal. We find that older proposals lack specificity when identifying AI governance actors. Recent proposals, on the other hand, assign roles and responsibilities to a defined set of actors. The findings from this blog post can help policymakers and analysts better understand China’s fast-evolving AI governance landscape.

The European Union's Artificial Intelligence Act has officially come into force today after more than five years of legislative processes and negotiations. While marking a significant milestone, it also initiates a prolonged phase of implementation, refinement, and enforcement. This blog post outlines key aspects of the regulation, such as rules for general-purpose AI and governance structures, and provides insights into its timeline and future expectations.

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.

As the U.S. government tightens its controls on China’s semiconductor ecosystem, a new dimension is increasingly worrying Congress: the open-source chip architecture known as RISC-V (pronounced “risk-five”). This blog post provides an introduction to the RISC-V architecture and an explanation of what policy-makers can do to address concerns about this open architecture.

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.