Growing popular and industry interest in high-performing natural language generation models has led to concerns that such models could be used to generate automated disinformation at scale. This report examines the capabilities of GPT-3--a cutting-edge AI system that writes text--to analyze its potential misuse for disinformation. A model like GPT-3 may be able to help disinformation actors substantially reduce the work necessary to write disinformation while expanding its reach and potentially also its effectiveness.

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.

As states turn to AI to gain an edge in cyber competition, it will change the cat-and-mouse game between cyber attackers and defenders. Embracing machine learning systems for cyber defense could drive more aggressive and destabilizing engagements between states. Wyatt Hoffman writes that cyber competition already has the ingredients needed for escalation to real-world violence, even if these ingredients have yet to come together in the right conditions.

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.