The United Nations has integrated artificial intelligence into its scientific programs to support nuclear fusion research and monitor radioactive contamination. These are two distinct fields, yet both illustrate how AI is becoming an operational tool within international organizations — not merely a topic of debate.
On the fusion side, AI’s contribution is technical and direct. Machine learning algorithms are being applied to handle the complexity of data generated during plasma experiments, where temperatures must be sustained above 100 million degrees. Tokamaks and high-power lasers are the two dominant architectures in current research, and both are increasingly relying on intelligent systems to keep plasma stable and refine reactor designs. AI also enables the development of detailed simulations to rapidly assess the performance of materials exposed to the extreme conditions inside fusion devices, cutting the time and cost of physical testing.
A recent study published in Nuclear Fusion and Plasma Physics and Controlled Fusion describes a plasma monitoring system based on a multi-task learning model capable of simultaneously identifying different operational modes and detecting localized edge instabilities — known as ELMs — with 94% accuracy. Results of this kind, which would have been impossible to achieve in real time just a few years ago, are reshaping the pace of experiments at international research facilities.
In parallel, the IAEA — the UN’s atomic energy agency — is using AI to process data from radiation detection systems, improving the ability to identify nuclear and radioactive materials. These models are also being applied to the isotopic analysis of large global hydrological databases, such as the global network of isotopes in precipitation managed jointly by the IAEA and the World Meteorological Organization. In this context, environmental monitoring is achieving a level of precision that traditional methods simply could not deliver.
The underlying rationale is straightforward: fusion experiments generate volumes of data that no team of human researchers can process manually at the required speed. AI bridges that gap. The same holds true for planetary-scale environmental monitoring, where the sheer quantity of measurements to be analyzed exceeds the operational capacity of any organization that does not rely on automated systems. International bodies have recognized this reality and have equipped themselves accordingly.
The road to commercial fusion remains long. But the integration of AI into the day-to-day instrumentation of research laboratories and IAEA control structures is concretely narrowing the distance between fundamental research and industrial-scale applications. If predictive models continue to improve — and the data from recent years suggests they will — the United Nations’ contribution to this energy transition will prove far more substantial than its institutional profile might suggest.
