In their op-ed in Foreign Policy, Josh A. Goldstein and Renée DiResta discuss recent efforts by the U.S. government to disrupt Russian influence operations, highlighting how Russia uses fake domains, media outlets, and social media influencers to manipulate global public conversations.

In his op-ed on The Diplomat, Jacob Feldgoise discusses the geopolitical significance of chips and the U.S. CHIPS and Science Act.

View this session of our Security and Emerging Technology Seminar Series on August 1 at 12 p.m. ET. This session featured a discussion on the President’s Council of Advisors on Science and Technology (PCAST) Report on Strategy for Cyber-Physical Resilience.

In her op-ed featured in War on the Rocks, CSET's Jenny Jun discussed the nuanced relationship between AI and cyber operations, highlighting both the optimism and caution within the U.S. government regarding AI's impact on cyber defense and offense.

In an article published by the Financial Time exploring the rapid rise of AI-generated conspiracy theories and spam content on social media platforms, CSET's Josh A. Goldstein provided his expert insights.

While recent progress in artificial intelligence (AI) has relied primarily on increasing the size and scale of the models and computing budgets for training, we ask if those trends will continue. Financial incentives are against scaling, and there can be diminishing returns to further investment. These effects may already be slowing growth among the very largest models. Future progress in AI may rely more on ideas for shrinking models and inventive use of existing models than on simply increasing investment in compute resources.

Concerns over risks from generative artificial intelligence systems have increased significantly over the past year, driven in large part by the advent of increasingly capable large language models. But, how do AI developers attempt to control the outputs of these models? This primer outlines four commonly used techniques and explains why this objective is so challenging.

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.

Large language models (LLMs) could potentially be used by malicious actors to generate disinformation at scale. But how likely is this risk, and what types of economic incentives do propagandists actually face to turn to LLMs? New analysis uploaded to arXiv and summarized here suggests that it is all but certain that a well-run human-machine team that utilized existing LLMs (even open-source ones that are not cutting edge) would save a propagandist money on content generation relative to a human-only operation.

In a BBC article that discusses the urgent need to integrate cybersecurity measures into artificial intelligence systems, CSET's Andrew Lohn provided his expert analysis.