CSET's AI Assessment team provides a template that helps organizations create profiles to guide the management and deployment of AI systems in line with NIST's AI Risk Management Framework.

Steph Batalis, Caroline Schuerger and Vikram Venkatram explore three notable areas in the life sciences where LLMs are catalyzing meaningful advances: drug discovery, genetics, and precision medicine.

The first in a series of posts, this article provides a reading guide on topics relevant to AI regulation. This article shares 101 resources on generative AI, writings on human-AI interactions, and a list of official documents and commentary on U.S. AI policy.

Former CSET intern, Lisa Lam, writes about how generative AI tools—like ChatGPT—can help students learn key concepts in cybersecurity.

In the second of a series of publications, CSET and CNAS outline one avenue the U.S. government could pursue to cut off China’s access to cloud computing services in support of military, security, or intelligence services end use(r)s. The authors discuss pros, cons, and limitations.

In the first of a series of publications, CSET and CNAS outline one potential avenue for the U.S. government to cut off Chinese access to controlled chips via cloud computing, as well its pros, cons, and limitations.

What exactly are the differences between generative AI, large language models, and foundation models? This post aims to clarify what each of these three terms mean, how they overlap, and how they differ.

As technology competition intensifies between the United States and China, governments and policy researchers are looking for metrics to assess each country’s relative strengths and weaknesses. One measure of technology innovation increasingly used by the policy community is research output. Drawing on CSET’s experiences over the last four years, this post shares our best practices for using research output to study national technological competition and inform public policy.

Executive Director Dewey Murdick shares a review of CSET's successes and growth in 2022.

Machine learning advances have powered the development of new and more powerful generative language models. These systems are increasingly able to write text at near human levels. In a new report, authors at CSET, OpenAI, and the Stanford Internet Observatory explore how language models could be misused for influence operations in the future, and they provide a framework for assessing potential mitigation strategies.