Micah Musser is a Research Analyst at Georgetown’s Center for Security and Emerging Technology (CSET), where he works on the CyberAI Project. Previously, he worked as a Research Assistant at the Berkley Center for Religion, Peace, and World Affairs. He graduated from Georgetown University’s College of Arts and Sciences with a B.A. (summa cum laude) in Government focusing on political theory.
In an opinion piece for Lawfare, Research Analyst Micah Musser discussed the new regulations that entered into effect in China requiring companies deploying recommendation algorithms to file details about those algorithms with the Cyberspace Administration of China.
AI and ComputeJanuary 2022
Between 2012 and 2018, the amount of computing power used by record-breaking artificial intelligence models doubled every 3.4 months. Even with money pouring into the AI field, this trendline is unsustainable. Because of cost, hardware availability and engineering difficulties, the next decade of AI can't rely exclusively on applying more and more computing power to drive further progress.
Machine Learning and CybersecurityJune 2021
Cybersecurity operators have increasingly relied on machine learning to address a rising number of threats. But will machine learning give them a decisive advantage or just help them keep pace with attackers? This report explores the history of machine learning in cybersecurity and the potential it has for transforming cyber defense in the near future.
Truth, Lies, and AutomationMay 2021
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.
Automating Cyber AttacksNovember 2020
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.