CSET produces evidence-driven analysis in a variety of forms, from informative graphics and translations to expert testimony and published reports. Our key areas of inquiry are the foundations of artificial intelligence — such as talent, data and computational power — as well as how AI can be used in cybersecurity and other national security settings. We also do research on the policy tools that can be used to shape AI’s development and use, and on biotechnology.


Understanding the Global Gain-of-Function Research Landscape

Caroline Schuerger Steph Batalis Katherine Quinn Ronnie Kinoshita Owen Daniels Anna Puglisi
| August 2023

Gain- and loss-of-function research have contributed to breakthroughs in vaccine development, genetic research, and gene therapy. At the same time, a subset of gain- and loss-of-function studies involve high-risk, highly virulent pathogens that could spread widely among humans if deliberately or unintentionally released. In this report, we map the gain- and loss-of-function global research landscape using a quantitative approach that combines machine learning with subject-matter expert review.



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Onboard AI: Constraints and Limitations

Kyle Miller Andrew Lohn
| August 2023

Artificial intelligence that makes news headlines, such as ChatGPT, typically runs in well-maintained data centers with an abundant supply of compute and power. However, these resources are more limited on many systems in the real world, such as drones, satellites, or ground vehicles. As a result, the AI that can run onboard these devices will often be inferior to state of the art models. That can affect their usability and the need for additional safeguards in high-risk contexts. This issue brief contextualizes these challenges and provides policymakers with recommendations on how to engage with these technologies.

Data Brief

“The Main Resource is the Human”

Micah Musser Rebecca Gelles Ronnie Kinoshita Catherine Aiken Andrew Lohn
| April 2023

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.


AI and Compute

Andrew Lohn Micah Musser
| January 2022

Between 2012 and 2018, the amount of computing power used by record-breaking artificial intelligence models doubled every 3.4 months. Even with money pouring into the AI field, this trendline is unsustainable. Because of cost, hardware availability and engineering difficulties, the next decade of AI can't rely exclusively on applying more and more computing power to drive further progress.

Formal Response

Recommendations for the National AI Research Resource Task Force

Dakota Cary
| September 27, 2021

CSET submitted this comment to the Office of Science and Technology Policy and the National Science Foundation to support the work of the National Artificial Intelligence Research Resource (NAIRR) Task Force to develop an implementation roadmap that would provide AI researchers and students across scientific disciplines access to computational resources, high-quality data, educational tools, and user support.