Research participants who read propaganda generated by GPT-3 davinci (a large language model) were nearly as persuaded as those who read real propaganda from Iran or Russia, according to a new peer-reviewed study by Josh A. Goldstein and co-authors.

This data snapshot is the first in a series on CSET’s cybersecurity jobs data, a new dataset created by classifying data from 513 million LinkedIn user profiles. Here, we offer an overview of its creation and explore some use cases for analysis.

Strengthening the federal cyber workforce is one of the main priorities of the National Cyber Workforce and Education Strategy. The National Science Foundation’s CyberCorps Scholarship-for-Service program is a direct cyber talent pipeline into the federal workforce. As the program tries to satisfy increasing needs for cyber talent, it is apparent that some form of program expansion is needed. This policy brief summarizes trends from participating institutions to understand how the program might expand and illustrates a potential future artificial intelligence (AI) federal scholarship-for-service program.

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.

CSET submitted the following comment in response to a Request for Comment (RFC) from the Office of Management and Budget (OMB) about a draft memorandum providing guidance to government agencies regarding the appointment of Chief AI Officers, Risk Management for AI, and other processes following the October 30, 2023 Executive Order on AI.

AI capabilities are evolving quickly and pose novel—and likely significant—risks. In these rapidly changing conditions, how can policymakers effectively anticipate and manage risks from the most advanced and capable AI systems at the frontier of the field? This Roundtable Report summarizes some of the key themes and conclusions of a July 2023 workshop on this topic jointly hosted by CSET and Google DeepMind.

This explainer overviews techniques to produce smaller and more efficient language models that require fewer resources to develop and operate. Importantly, information on how to leverage these techniques, and many of the subsequent small models, are openly available online for anyone to use. The combination of both small (i.e., easy to use) and open (i.e., easy to access) could have significant implications for artificial intelligence development.

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.

Jenny Jun's testimony before the House Foreign Affairs Subcommittee on Indo-Pacific for a hearing titled, "Illicit IT: Bankrolling Kim Jong Un."