Sending cutting-edge technology to the frontline of Ukraine is an opportunity for the United States to get operational experience and give Ukrainians newer and helpful capabilities on the battlefield according to CSET Margarita Konaev.

In a May CSET webinar, Emily Weinstein and Kevin Wolf propose an export control regime that could effectively keep sensitive technologies from being missed by authoritarian governments and reduce pressure on the U.S. to impose unilateral controls.

The U.S. Department of Defense's bureaucratic structure could impede a robust AI culture according to CSET's Margarita Konaev.

China’s "Science and Technology Daily," a state-run newspaper, published a revealing series of articles in 2018 on 35 different Chinese technological import dependencies. The articles, accessible here in English for the first time, express concern that strategic Chinese industries are vulnerable to any disruption to their supply of specific U.S., Japanese, and European “chokepoint” technologies. This issue brief summarizes the article series and analyzes the Chinese perspective on these import dependencies and their causes.

Husanjot Chahal explores US-Indian cooperation in military applications of AI in a podcast interview with the Takshashila Institution.

Associate Director of Analysis Margarita Konaev discusses how satellite imagery is challenging disinformation about the war in Ukraine.

Senior Fellow Andrew Lohn discusses why AI and machine learning's vulnerabilities present limitations when applied in real-life defense applications in an interview with Forbes.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the third in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

CSET's Anna Puglisi discusses the future of biotechnology in Titus Talks.

Like traditional software, vulnerabilities in machine learning software can lead to sabotage or information leakages. Also like traditional software, sharing information about vulnerabilities helps defenders protect their systems and helps attackers exploit them. This brief examines some of the key differences between vulnerabilities in traditional and machine learning systems and how those differences can affect the vulnerability disclosure and remediation processes.