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.

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.

Psychology Today published an article on Microsoft's new Lobe application. The article featured original research from several CSET experts, including Research Fellow Zachary Arnold, Data Analyst Ilya Rahkovsky and Research Analyst Tina Huang.

The rise of deepfakes could enhance the effectiveness of disinformation efforts by states, political parties and adversarial actors. How rapidly is this technology advancing, and who in reality might adopt it for malicious ends? This report offers a comprehensive deepfake threat assessment grounded in the latest machine learning research on generative models.

Plus, cloud computing bill introduced, semiconductor industry association lobbies for $37B, and bill to advance AI research introduced

Plus, JAIC awards $800M to Booz Allen, Schumer proposes $100B for emerging tech, and deepfake detection bill advances

Plus, Lockheed Martin to develop cyber jamming pod, Huawei partners with chipmakers, and many AI COVID-19 models found to be unreliable

Plus, Nvidia purchases Mellanox, MIT ends collaboration with iFlyTek, and JAIC requests information on testing and evaluation

Plus, the Coronavirus and AI Jobs, DOE Funds $30M in AI Research, and China’s AI Advisory Committee

Tim G. J. Rudner is a Non-Resident AI/ML Fellow. He conducts research on probabilistic machine learning, reinforcement learning and AI safety.