Over the past decade, artificial intelligence (AI) has evolved rapidly, becoming increasingly sophisticated and capable of solving ever more complex problems. AI is deployed in sectors as diverse as manufacturing, transportation, finance, education and healthcare. In a similar vein, it has the potential to advance the development of nuclear applications, science and technology. Harnessing its capabilities in the nuclear field can positively contribute to addressing some of today’s most pressing challenges, from food security to climate change.
Here are some ways in which AI has and will continue to benefit the peaceful applications of nuclear technology. These are discussed in more detail in a new
AI can contribute to combating diseases. It is already applied to support the diagnosis and treatment of cancer through improved image interpretation and precise tumour contouring, enabling more accurate treatment plans and adaptive radiotherapy – a process tailored to the anatomical characteristics of the individual patient. The
AI will also play an important role in the
AI tools combined with nuclear technologies can help make food systems more sustainable and climate change resilient, while also addressing food and nutrition insecurity.
Experts deploy AI to process and analyse data to increase crop yields, estimate soil moisture, remediate radioactively contaminated land, detect and predict food fraud events and improve irrigation.
Isotopic methods allow experts to study and track how water moves through different stages of the hydrological cycle and what transformations occur in this cycle due to climate change. Experts already apply AI-based approaches to quickly analyse huge amounts of water-related isotopic data stored in global repositories, such as the Global Network of Isotopes in Precipitation maintained by the
Effective and efficient analysis of data facilitated with AI helps scientists understand climate change and its impact on water availability worldwide.
Artificial intelligence plays an increasingly important role in nuclear science. AI are used in data analysis, theoretical modelling and experiment design, helping to accelerate fundamental research, for example in the realm of nuclear and atomic data evaluation and compilation, and advancing technological innovation.
A particular area that benefits from the application of AI is fusion research. With its ability to solve large and complex problems, AI can aid experiments and scientific discovery through modelling and simulations. These applications of AI are included in a new five-year
Nuclear power is a reliable, low carbon source of energy, and it can benefit significantly from the inclusion of AI. By combining digital simulations of real nuclear facilities with AI systems, the industry can optimize complex procedures and improve reactor design, performance and safety. Such optimization can increase the efficiency of operations and reduce maintenance costs.
Machine learning – a process whereby AI learns by analysing large amounts of data – helps to automate tasks and thereby increase reliability and avoid errors. Furthermore, AI has considerable analytical and predictive potential to help monitor power plant processes and detect anomalies.
As more and more countries choose to use nuclear technology for peaceful purposes and adopt nuclear power programmes, the
AI can contribute to nuclear security and safety in several ways. It can be used in the processing of data from radiation detection systems to enhance the detection and identification of nuclear and other radioactive material. It can be applied to analyse data from physical protection systems to improve the detection of intruders. It can also help spot anomalies that could indicate a cyber-attack on a nuclear facility. Furthermore, in the realm of radiation protection, the integration of AI in safety standards-related software can reinforce the protection of the millions of workers with occupational exposure in medicine, construction, mining, shipping, agriculture and nuclear power.
Safeguards are technical verification measures through which the
Safeguards rely on large amounts of data obtained by various means, such as satellite imagery, environmental sampling, gamma ray spectroscopy and video surveillance. AI can help nuclear inspectors and safeguard analysts with the analysis of these data. Machine learning methods have already been used to detect outliers in large datasets and assist in verifying spent fuel and analysing surveillance recordings. AI is expected to further improve the efficiency of safeguards implementation by reducing the number of repetitive tasks performed by inspectors.
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