Nuclear energy, primarily used for power generation, can indirectly support AI development through providing clean and sustainable energy for the computational resources that AI models require. Advanced machine learning models like deep learning, requires massive computational resources. Training large models, especially those used in natural language processing requires substantial processing power and energy. This high demand for energy is one of the main challenges for scaling AI. AI research and applications rely heavily on large data centers and supercomputers. These facilities consume significant amounts of electricity. Nuclear energy could help power these data centers, providing a low-carbon, stable, and reliable energy source, especially in regions where renewable energy sources might not be enough to meet the demand.

Nuclear energy produces very low carbon emissions compared to fossil fuels. Using nuclear energy to power the infrastructure that supports AI would contribute to a reduction in overall carbon emissions, aligning with the goals of sustainable AI development. As AI becomes more ubiquitous, its environmental impact is a growing concern due to the energy it consumes. If nuclear energy, which produces minimal greenhouse gas emissions, is used to power AI data centers, it could help mitigate the environmental impact of large-scale AI systems. This is crucial for making AI more sustainable. According to International Atomic Energy Agency, ML tools require large amounts of data for training and testing to achieve an accurate output.

AI also has a role in improving the safety, efficiency, and performance of nuclear power plants. The nuclear industry is leveraging machine learning and artificial intelligence (AI) to drive technical advancements. The nuclear industry utilises AI techniques, such as integrated massive language models, for various purposes. A wide range of applications include design, construction optimisation, and operating efficiency. AI in advanced manufacturing improves efficiency, flexibility, and customisation, while also lowering costs and increasing quality. These innovations help nuclear energy remain sustainable and competitive in today’s energy economy.
These applications may address obstacles that have slowed industrial growth in many areas.

AI models can be used to optimize the operation of reactors, predict maintenance needs, or even enhance safety protocols. In this sense, AI helps in the operation and management of nuclear energy, and nuclear energy could potentially support the energy demands of AI infrastructure. Research into nuclear fusion could benefit from AI models to optimize experimental setups, simulate plasma behavior, and analyze huge amounts of data generated from fusion experiments. If fusion becomes commercially viable, it could become an almost limitless and sustainable energy source, supporting AI and other high-energy applications.

AI is being employed to monitor and manage nuclear power plants, improving their efficiency and safety. AI can predict potential issues and optimize plant operations, helping to ensure that nuclear energy is reliably available for various industrial applications, including AI research. Small modular reactors (SMRs) are a new type of nuclear reactor designed to be more flexible and scalable. These reactors could potentially be used to power remote or isolated AI research facilities, offering a reliable, low-emission energy source.

Nuclear power plants are expensive to build and maintain. For AI applications, particularly at a smaller scale, other energy sources (like solar or wind) may be more cost-effective. However, nuclear could be ideal for large-scale AI data centers that need a constant and reliable power source. While nuclear energy is not directly integrated into the AI systems themselves, it plays a crucial role in ensuring the availability of the large amounts of energy needed to support the growing demands of AI infrastructure. Additionally, AI can enhance nuclear energy production and management, creating a symbiotic relationship between the two fields in terms of sustainability and optimization.

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