Second, machine-learning algorithms could be used to gather, mine, and analyze large volumes of intelligence (open-source and classified) sources to detect correlations in heterogeneous - and possibly contradictory, compromised, or otherwise manipulated - datasets.

First, machine learning, in conjunction with cloud computing, unmanned aerial vehicles (or drones), and big-data analytics, could be used to enable mobile intelligence, surveillance, and reconnaissance platforms to be deployed in geographically long ranges, and in complex, dangerous environments (e.g., contested anti-access/area-denial zones, urban counterinsurgency, or deep-sea) to process real-time data and alert commanders of potentially suspicious or threatening situations such as military drills and suspicious troop or mobile missile launcher movements. It is worth considering how advances in AI technology are being researched, developed, and, in some cases, are deployed and operational in the context of the broader nuclear deterrence architecture - early-warning and intelligence, surveillance, and reconnaissance command and control nuclear weapon delivery systems and non-nuclear operations.Įarly-Warning and Intelligence, Surveillance, and ReconnaissanceĪI machine learning might, in three ways, quantitatively enhance existing early-warning and intelligence, surveillance, and reconnaissance systems. How and to what degree does AI augmentation mark a departure from automation in the nuclear enterprise, which goes back several decades? How transformative are these developments? And what are the potential risks posed by fusing AI technology with nuclear weapons? While we can’t answer these questions fully, only by extrapolating present trends in AI-enabling capabilities can we illuminate the potential risks of the current trajectory and thus consider ways to manage them.

Nuclear time journey to