In an article published by NPR that discusses the accessibility and potential impact of generative artificial intelligence applications on political campaigns and public opinion, CSET's Micah Musser provided his expert insights.

The current AI-for-cybersecurity paradigm focuses on detection using automated tools, but it has largely neglected holistic autonomous cyber defense systems — ones that can act without human tasking. That is poised to change as tools are proliferating for training reinforcement learning-based AI agents to provide broader autonomous cybersecurity capabilities. The resulting agents are still rudimentary and publications are few, but the current barriers are surmountable and effective agents would be a substantial boon to society.

CSET's Andrew Lohn and Joshua A. Goldstein share their insights on the difficulties of identifying AI-generated text in disinformation campaigns in their op-ed in Lawfare.

CSET's Jenny Jun was featured in the Atlantic Council's The 5x5, a series that showcases five experts answering five questions on a common theme, trend, or current event in the world of cyber.

CSET's Andrew Lohn and Krystal Jackson discussed the potential for reinforcement learning to support cyber defense.

In an op-ed published in The Diplomat, Micah Musser discusses the concerns raised by policymakers in Washington about the disruptive potential of artificial intelligence technologies.

A report by CSET's Josh Goldstein, Micah Musser, and CSET alumna Katerina Sedova in collaboration with OpenAI and Stanford Internet Observatory was cited in an article published by Forbes.

CSET Research Analyst Micah Musser and Institute for Progress Fellow Tim Hwang discussed CSET research examining factors that will contribute to future AI development.

A CSET data brief by Micah Musser, Rebecca Gelles, Ronnie Kinoshita, Catherine Aiken, and Andrew Lohn was cited by Politico in a newsletter about the rapidly changing field of artificial intelligence and the debate surrounding its regulation.

Progress in artificial intelligence (AI) depends on talented researchers, well-designed algorithms, quality datasets, and powerful hardware. The relative importance of these factors is often debated, with many recent “notable” models requiring massive expenditures of advanced hardware. But how important is computational power for AI progress in general? This data brief explores the results of a survey of more than 400 AI researchers to evaluate the importance and distribution of computational needs.