Machine learning at the edge to improve in-field safeguards inspections

Abstract: Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term inspecta, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. This work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.

Recommended citation: Shoman, N., Williams, K., Balsara, B., Ramakrishnan, A., Kakish, Z., Coram, J., ... & Smartt, H. (2024). Machine learning at the edge to improve in-field safeguards inspections. Annals of Nuclear Energy, 200, 110398.
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