AI OPPORTUNITIES EXPLORED | SPECIAL REPORT
Above: AI can substitute for human presence across a wide range of tasks in a nuclear plant and will minimise the need for direct human involvement by simultaneously carrying out complex cognitive tasks involving many engineering disciplines
Modern artificial intelligence approaches are designed
to be able to react in unpredictable ways to create novel output. Nuclear’s requirements for safety and other industry-specific needs may require AI to be qualified for nuclear applications. Experts in AI will need to be aware of the potential threat vectors that can be used against the developed model so they can incorporate security into the design from the ground up, and also build in cybersecurity plans throughout the development lifecycle to prevent misuse of the training data and vulnerabilities in the model, as well as safe operation throughout its lifecycle.
Five actions recommended The report makes five recommendations to allow the industry to take advantage of some of the AI opportunities
it has identified: ● Develop a leadership computing capability ecosystem The report highlights the AI4E initiative, which would see an ecosystem of high-performance computing capabilities replace stand-alone individual machines, which it says are under-used. The increase in computational capability would help solve problems in nuclear energy that are presently beyond the reach of any single machine. High-speed ‘data pipes’ are needed to facilitate communication, with algorithms to coordinate tasks and to seamlessly exchange data.
● Expand on base foundational models to include nuclear energy Because the specialised domain knowledge associated with nuclear generation is not well represented in existing foundation models more needs to be dome to expand this capacity.
● Ensure that AI enabling technologies and tools are available for nuclear Current tools are application- oriented and confined to the needs of subject matter experts without regard necessarily for what a larger enabling ecosystem might look like. Nuclear energy has unique security and safety issues that are not adequately addressed by current AI environments.
● Provide for testbeds for validating and evaluating AI methods It will be necessary to train on a platform with rich scientific data sets, ideally with a physical
test bed. This will be necessary because the value proposition of AI for nuclear energy will almost certainly involve improving the efficiency of plant operations and maintenance of equipment.
● Integrate existing infrastructure in a nationwide research resource A new mindset that aims to spur collaboration would better fit an environment where AI is expected to house all that has been learned about nuclear energy. In the infrastructure domain, work could start with training related systems independently. Later, when the AI infrastructure has sufficiently developed, they would be trained together.
Even before these steps are taken, it is critical to create a comprehensive data repository and an ecosystem around it. While data are abundant, databases’ formats, availability and provenance are highly inconsistent. Some are in the form of hybrid data forms with qualitative and quantitative descriptions and inhomogeneous data. There is an additional wealth of experimental data that
reaches back to the early days of the development of the peaceful use of nuclear energy. It is largely inaccessible as the data owners do not have the resources to retrieve and order it in a form ready for use but this may be an opportunity to use large language models, as has been demonstrated in the private sector. AI has to access the output of computer codes that
represent the physics of a nuclear system if it is to have its own internal and accurate representation of the physical system. It needs to be able to understand when insufficient data is provided, such as in first-of-a-kind designs. In parallel to data collection and methods development, a test platform is needed to evaluate and tune the models that can mine existing test data. The report says the cost involved in moving to an AI-enabled industry means there will have to be early financial benefits. In the first step, it recommends four main teams to take it forward (for data, methods, testing and infrastructure) supported by functions such as legal, to ensure that data sharing is compliant with laws, establish agreements with private industry and ensure models meet industry regulations. ■
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