This paper is the third 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,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces interpretability as a means to enable assurance in modern machine learning systems.

This paper is the second 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,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces adversarial examples, a major challenge to robustness in modern machine learning systems.

This paper is the first 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. In it, the authors introduce three categories of AI safety issues: problems of robustness, assurance, and specification. Other papers in this series elaborate on these and further key concepts.

Stealth technology was one of the most decisive developments in military aviation in the last 50 years. With U.S. technological leadership now under challenge, especially from China, this issue brief derives several lessons from the history of Stealth to guide current policymakers. The example of Stealth shows how the United States produced one critical technology in the past and how it might produce others today.

The Chinese government is pouring money into public-private investment funds, known as guidance funds, to advance China’s strategic and emerging technologies, including artificial intelligence. These funds are mobilizing massive amounts of capital from public and private sources—prompting both concern and skepticism among outside observers. This overview presents essential findings from our full-length report on these funds, analyzing the guidance fund model, its intended benefits and weaknesses, and its long-term prospects for success.

China’s government is using public-private investment funds, known as guidance funds, to deploy massive amounts of capital in support of strategic and emerging technologies, including artificial intelligence. Drawing exclusively on Chinese-language sources, this report explores how guidance funds raise and deploy capital, manage their investment, and interact with public and private actors. The guidance fund model is no silver bullet, but it has many advantages over traditional industrial policy mechanisms.

Six Chinese universities have relationships with Advanced Persistent Threat (APT) hacking teams. Their activities range from recruitment to running cyber operations. These partnerships, themselves a case study in military-civil fusion, allow state-sponsored hackers to quickly move research from the lab to the field. This report examines these universities’ relationships with known APTs and analyzes the schools’ AI/ML research that may translate to future operational capabilities.

The rapid integration of artificial intelligence into military systems raises critical questions of ethics, design and safety. While many states and organizations have called for some form of “AI arms control,” few have discussed the technical details of verifying countries’ compliance with these regulations. This brief offers a starting point, defining the goals of “AI verification” and proposing several mechanisms to support arms inspections and continuous verification.

As the U.S. military integrates artificial intelligence into its systems and missions, there are outstanding questions about the role of trust in human-machine teams. This report examines the drivers and effects of such trust, assesses the risks from too much or too little trust in intelligent technologies, reviews efforts to build trustworthy AI systems, and offers future directions for research on trust relevant to the U.S. military.

In May 2020, the White House announced it would deny visas to Chinese graduate students and researchers who are affiliated with organizations that implement or support China’s military-civil fusion strategy. The authors discuss several ways this policy might be implemented. Based on Chinese and U.S. policy documents and data sources, they estimate that between three and five thousand Chinese students might be prevented from entering U.S. graduate programs each year.