The Nuclear Threat Initiative (NTI) and the Center for Advanced Defense Studies (C4ADS) last week released Signals in the Noise: Preventing Nuclear Proliferation with Machine Learning & Publicly Available Information, a 22-page report that provides a blueprint for identifying high-risk or illicit nuclear trade. (Machine learning can be defined as a branch of artificial intelligence focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.)
The main takeaway: According to the report, NTI and C4ADS worked together for two years on a pilot project to demonstrate the viability of this new approach to nuclear nonproliferation efforts. “The project succeeded,” the authors write in the report’s executive summary. “Trade network analysis—and the machine learning processes that supported it—uncovered previously unknown entities of elevated risk within millions of transactions. The work showed that automated data preparation could save hundreds of analyst hours and help identify twice as many potentially high-risk entities as previous manual efforts.”
Recommendations: The report calls on the leaders of nonproliferation efforts around the world to do the following:
■ Integrate publicly available information more deeply into existing monitoring and verification regimes.
■ Use modern analytical approaches, including machine learning, to enable using big data at scale.
■ Build partnerships to allow analysts to access shared data.
■ Embrace the use of publicly available information and modern analytical tools for future international nonproliferation initiatives.