CSET Research Analyst Dahlia Peterson testified before the U.S.-China Economic and Security Review Commission at a hearing on "China’s Challenges and Capabilities in Educating and Training the Next Generation Workforce."

Artificial intelligence is so diverse in its range that no simple one-size-fits-all assessment approach can be adequately applied to it. AI systems have a wide variety of functionality, capabilities, and outputs. They are also created using different tools, data modalities, and resources, which adds to the diversity of their assessment. Thus, a collection of approaches and processes is needed to cover a wide range of AI products, tools, services, and resources.

Translation Snapshots are short posts that highlight related translations produced by CSET’s in-house translation team. Each snapshot identifies relevant translations, provides short summaries, and links to full translations. Check back regularly for additional Translation Snapshots highlighting our work.

CSET submitted the following comment in response to the National Institute for Standards and Technology's second draft of its AI Risk Management Framework.

China’s State Key Laboratory system drives the country’s innovation in science and technology. A key part of China’s aim to reduce its dependence on foreign technology, these labs conduct cutting-edge basic and applied research, attract and train domestic and foreign talent, and conduct academic exchanges with foreign counterparts. These laboratories are spread across almost all Chinese provinces except Tibet, with the majority clustered in large coastal cities.

CSET Director Dewey Murdick testified before the Senate Select Committee on Intelligence hearing on "Countering the People’s Republic of China’s Economic and Technological Plan for Dominance." Murdick discussed China's strategy to move towards self-sufficiency in key technologies and steps the United States can take to respond.

CSET Research Analyst Dakota Cary testified before the U.S.-China Economic and Security Review Commission hearing on "China’s Cyber Capabilities: Warfare, Espionage, and Implications for the United States." Cary discussed the cooperative relationship between Chinese universities and China’s military and intelligence services to develop talent with the capabilities to perform state-sponsored cyberespionage operations.

Problems of AI safety are the subject of increasing interest for engineers and policymakers alike. This brief uses the CSET Map of Science to investigate how research into three areas of AI safety — robustness, interpretability and reward learning — is progressing. It identifies eight research clusters that contain a significant amount of research relating to these three areas and describes trends and key papers for each of them.

​​This Classifying AI Systems Interactive presents several AI system classification frameworks developed to distill AI systems into concise, comparable and policy-relevant dimensions. It provides key takeaways and framework-specific results from CSET’s analysis of more than 1,800 system classifications done by survey respondents using the frameworks. You can explore the frameworks and example AI systems used in the survey, and even take the survey.

This paper is the fourth installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learning systems that operate as intended.