CSET submitted the following comment in response to a DHS Notice on Proposed Rule-Making from the U.S. Citizenship and Immigration Services about modernizing H-1B requirements, providing flexibility in the F-1 program, and program improvements affecting other nonimmigrant workers

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.

Demand for talent is one of the core elements of technological competition between the United States and China. In this issue brief, we explore demand signals in China’s domestic AI workforce in two ways: geographically and within the defense and surveillance sectors. Our exploration of job postings from Spring 2021 finds that more than three-quarters of all AI job postings are concentrated in just three regions: the Yangtze River Delta region, the Pearl River Delta, and the Beijing-Tianjin-Hebei area.

This data brief analyzes over 200,000 U.S. government grants awarded to industry and academic recipients for artificial intelligence research between January 2017 and May 2023. The authors find that while the majority of federal grants are awarded to academic recipients, industry played an outsized role in U.S. government grant funding of AI research. Moreover, departments within the U.S. Department of Defense appear to prioritize funding industry and AI research relative to other funding agencies.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the second installment of a two-part series of data snapshots that explores export control data from the Bureau of Industry and Security (BIS), namely the 2021 and 2022 yearly reports for trade with China and Hong Kong.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the first installment of a two-part series of data snapshots that explores export control data from the Bureau of Industry and Security (BIS), namely the 2021 and 2022 yearly reports for trade with China and Hong Kong.

This report summarizes recent DOD digital workforce trends as an update to our 2021 report. We expanded our definition of AI talent to include data, analytics, software, and AI, referred to here as the “digital workforce,” to be more aligned with Department needs and current workforce planning efforts. We find that the Department of Defense continues to struggle with their ability to define, identify, develop, assign, promote, and retain digital talent.

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.