A report from CSET was featured in a CNBC article that discusses Singapore's significant potential to become a global AI hub.

As the U.S. government tightens its controls on China’s semiconductor ecosystem, a new dimension is increasingly worrying Congress: the open-source chip architecture known as RISC-V (pronounced “risk-five”). This blog post provides an introduction to the RISC-V architecture and an explanation of what policy-makers can do to address concerns about this open architecture.

This guide provides a run-down of CSET’s research since 2019 for first-time visitors and long-term fans alike. Quickly get up to speed on our “must-read” research and learn about how we organize our work.

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.

Antimicrobial resistance (AMR) is one of the world’s most pressing global health threats. Basic research is the first step towards identifying solutions. This brief examines the AMR research landscape since 2000, finding that the amount of research is increasing and that the U.S. is a leading publisher, but also that novel solutions like phages and synthetic antimicrobial production are a small portion of that research.

In a recent Nature article, CSET's Helen Toner lends her expertise to the discussion on groundbreaking developments in governmental oversight of artificial intelligence (AI) in the United States and the United Kingdom.

How can policymakers credibly reveal and assess intentions in the field of artificial intelligence? Policymakers can send credible signals of their intent by making pledges or committing to undertaking certain actions for which they will pay a price—political, reputational, or monetary—if they back down or fail to make good on their initial promise or threat. Talk is cheap, but inadvertent escalation is costly to all sides.

Australia, Canada, Japan, the United Kingdom, and the United States emphasize principles of accountability, explainability, fairness, privacy, security, and transparency in their high-level AI policy documents. But while the words are the same, these countries define each of these principles in slightly different ways that could have large impacts on interoperability and the formulation of international norms. This creates, what we call the “Inigo Montoya problem” in trustworthy AI, inspired by "The Princess Bride" movie quote: “You keep using that word. I do not think it means what you think it means.”

The August 9 Executive Order aims to restrict certain U.S. investments in key technology areas. In a previous post, we proposed an end-user approach to crafting an AI investment prohibition. In this follow-on post, we rely on existing and hypothetical transactions to test scenarios where U.S. investments in China’s AI ecosystem would or would not be covered under the proposed program, and highlight outstanding challenges.

In collaboration with colleagues from CNAS and the Atlantic Council, CSET Researchers Ngor Luong and Emily Weinstein provided this comment in request to Treasury's Advanced Notice of Rule-making request for public comment (TREAS-DO-2023-0009-0001).