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

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 decrea...

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Veröffentlicht in:Annals of nuclear energy 2024-06, Vol.200 (1), p.110398, Article 110398
Hauptverfasser: Shoman, Nathan, Williams, Kyle, Balsara, Burzin, Ramakrishnan, Adithya, Kakish, Zahi, Coram, Jamie, Honnold, Philip, Rivas, Tania, Smartt, Heidi
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Sprache:eng
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Zusammenfassung: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.
ISSN:0306-4549
1873-2100
DOI:10.1016/j.anucene.2024.110398