PARAT (Private-sector AI-Related Activity Tracker) is ETO's hub for data about private-sector companies and their AI activities. PARAT's easy-to-use interface brings together data on companies' AI research publications, patents, and hiring, enabling customizable, data-driven comparison and trend analysis. Use PARAT to explore how hundreds of leading companies around the world are engaged in AI, from Big Tech titans and leading generative AI startups to the entire S&P 500.

While the effects of the U.S. Department of Defense’s broad investments in research and development go far beyond what is publicly disclosed, authors affiliated with the DOD do publish papers about their research. This analysis examines more than 100,000 papers by DOD-affiliated authors since 2000 and offers insight into the patterns of research publication and collaboration by the DOD.

China’s leadership believes that artificial intelligence will play a central role in future wars. However, the author's comprehensive review of dozens of Chinese-language journal articles about AI and warfare reveals that Chinese defense experts claim that Beijing is facing several technological challenges that may hinder its ability to capitalize on the advantages provided by military AI. This report outlines these perceived barriers and identifies several technologies that Chinese experts believe may help the country develop and deploy military AI-enabled systems.

Since 2019, the U.S. government has imposed restrictive export controls on Huawei—one of China’s leading tech giants—seeking, in part, to hinder the company’s AI chip development efforts. This data snapshot reveals how exactly Huawei’s latest AI chip—the Ascend 910B—improves on the prior generation and demonstrates how export controls are likely hindering Huawei’s production.

Policy and research communities strive to mitigate AI harm while maximizing its benefits. Achieving effective and trustworthy AI necessitates the establishment of a shared language. The analysis of policies across different countries and research literature identifies consensus on six critical concepts: accountability, explainability, fairness, privacy, security, and transparency.

This paper is the fifth 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. This paper explores the opportunities and challenges of building AI systems that “know what they don’t know.”

Read our translation of a draft Chinese government framework for a system of standards for AI.

Read our translation of a report by a Chinese state-run think tank that describes how the Chinese government and foreign governments are using large AI models.

In February 2024, CSET introduced its new cybersecurity jobs dataset, a novel resource comprising ~1.4 million LinkedIn profiles of current U.S. cybersecurity workers. This data snapshot uses the dataset to identify top-producing institutions of cybersecurity talent.

CSET Non-Resident Senior Fellow Kevin Wolf testified before the US-China Economic and Security Review Commission on economic competition with China.