Machine learning systems’ vulnerabilities are pervasive. Hackers and adversaries can easily exploit them. As such, managing the risks is too large a task for the technology community to handle alone. In this primer, Andrew Lohn writes that policymakers must understand the threats well enough to assess the dangers that the United States, its military and intelligence services, and its civilians face when they use machine learning.

Based on an in-depth analysis of artificial intelligence and machine learning systems, the authors consider the future of applying such systems to cyber attacks, and what strategies attackers are likely or less likely to use. As nuanced, complex, and overhyped as machine learning is, they argue, it remains too important to ignore.

As demand for cybersecurity experts in the United States has grown faster than the supply of qualified workers, some organizations have turned to artificial intelligence to bolster their overwhelmed cyber teams. Organizations may opt for distinct teams that specialize exclusively in AI or cybersecurity, but there is a benefit to having employees with overlapping experience in both domains. This data brief analyzes hiring demand for individuals with a combination of AI and cybersecurity skills.

China operates a number of party- and state-sponsored talent programs to recruit researchers -- Chinese citizens and non-citizens alike -- to bolster its strategic civilian and military goals. CSET has created a tracker to catalog publicly available information about these programs. This catalog is a work in progress; if you have further information on programs currently not included in it -- or if you spot an error -- please complete the form at http://bit.ly/ChineseTalent

Machine learning may provide cyber attackers with the means to execute more effective and more destructive attacks against industrial control systems. As new ML tools are developed, CSET discusses the ways in which attackers may deploy these tools and the most effective avenues for industrial system defenders to respond.

CSET submitted the following comment to the Department of Homeland Security regarding a fixed time period of admission for nonimmigrant students, exchange visitors and representatives of foreign information media.

The resizing of images, which is typically a required part of preprocessing for computer vision systems, is vulnerable to attack. Images can be created such that the image is completely different at machine-vision scales than at other scales and the default settings for some common computer vision and machine learning systems are vulnerable.

The global AI industry is booming, with privately held firms pulling in nearly $40 billion in disclosed investment in 2019 alone. U.S. companies continue to attract the majority of that funding—64 percent of it in 2019—but that lead is not guaranteed. This report analyzes AI investment data from 2015 to 2019 to help better understand trends in the global AI landscape.

The United States must adopt a new approach to R&D policy to optimize the diversity of the current system, manage the risks of system dispersion and deliver the benefits of R&D to society. This policy brief provides a new framework for understanding the U.S. R&D ecosystem and recommendations for repositioning the role of the federal government in R&D.

Preserving pathways for high-skilled foreign talent critical to U.S. leadership in artificial intelligence.