The International Atomic Energy Agency is the world’s central intergovernmental forum for scientific and technical co-operation in the nuclear field. It works for the safe, secure and peaceful uses of nuclear science and technology, contributing to international peace and security and the United Nations’ Sustainable Development Goals.
Description of Activities on AI
Project 1: AI for Atoms – Report from the IAEA Technical Meeting on AI for Nuclear Technology and Applications
As the output of the 2021 Technical Meeting on AI for Nuclear Technology and Applications, this report will serve as a roadmap of ideas and opportunities where IAEA can have a supporting and transformative role in aiding progress towards the realization of the transformative impacts of AI in nuclear science, technology, and applications.
Project 2: IAEA Technical Meeting on AI for Nuclear Technology and Applications
The IAEA Technical Meeting on Artificial Intelligence for Nuclear Technology and Application provided an international, cross-cutting forum to discuss, identify and foster cooperation on AI applications, methodologies, tools and enabling infrastructure that have the potential to advance nuclear science, technology and applications.
Project 3: Working Group on the Ethics of Nuclear and AI (WG-ENAI)
The Working Group (WG) – established in connection with the IAEA Technical Meeting on AI for Nuclear Technology and Applications – is working towards developing a new Project Domain of normative applied ethics at the intersection of AI and nuclear science, technology and applications, referred to as the Ethics of Nuclear and AI (ENAI).
Project 4: Working Group on AI for Nuclear Fusion (WG-AI4NF)
The Working Group on AI for Nuclear Fusion (WG-AI4NF) – established in connection with the IAEA Technical Meeting on AI for Nuclear Technology and Applications – focuses on AI for enabling prediction and control solutions necessary for sustained, safe, and efficient future fusion power plant operation, as well as the opportunities and associated needs in AI areas that would help address challenges in nuclear fusion research through targeted collaborations. The WG-AI4NF is developing three Work Packages (WPs) which will be executed inside an IAEA-sponsored activity/framework (IAEA Coordinated Research Project) planned to start by the end of 2022.
Project 5: Artificial intelligence to Assess Climate Impact on Global Lakes
Global warming is considered a major threat to Earth’s lakes water budgets and quality. However, flow regulation, over-exploitation, lack of hydrological data, and disparate evaluation methods hamper comparative global estimates of lake vulnerability to evaporation. The stable isotope composition of 1,257 global lakes was analyzed using Artificial Intelligence techniques. It was found that in most of the lakes, this depends on precipitation and groundwater recharge subsequently altered by catchment and lake evaporation processes. Isotope mass-balance modelling shows that ca. 20 % of water inflow in global lakes is lost through evaporation and ca. 10 % of lakes in arid and temperate zones experience extreme evaporative losses >40 % of the total inflow. Precipitation amount, limpidity, wind speed, relative humidity, and solar radiation are predominant controls on lake isotope composition and evaporation, regardless of the climatic zone. The promotion of systematic global isotopic monitoring of Earth’s lakes provides a direct and comparative approach to detect the impacts of climatic and catchment-scale changes on water-balance and evaporation trends. This project indicates that the stable water isotopes of global lakes are highly relevant indicators that integrate multiple processes at the watershed scale and are sensitive to the hydroclimate response of both lakes and their catchment system. Stable isotope assays provide a low-cost efficacious tool to study lake-catchment changes with regards to sample collection and analysis. Additionally, stable isotope data from lakes is fully comparative globally, thereby providing a competitive advantage under the current scenario of different international methods and approaches that are not easy to compare in time and scale and which result in the current lack of the comparable data for lakes and catchments.
Project 6: Working Group on Artificial Intelligence for Water and Environment
The Working Group (WG) on Artificial Intelligence for Water and Environment – established in connection with the IAEA Technical Meeting on Artificial Intelligence (AI) for Nuclear Technology and Application – aimed to promote and enable the use of isotopic techniques with AI tools for better management of water and environmental resources, as well as adaptation to climate change worldwide. Recognizing that with the increasing availability of data from satellites, unmanned airborne vehicles and sensor networks, there is a myriad of data available to couple and explore in conjunction with the IAEA’s global isotope databases.
Taking the IAEA Technical Meeting as a starting point, the WG intends to create a platform for scientists working with AI tools at the interface of isotope hydrology, water resources protection and management. This will facilitate sharing of experiences in the use of machine and deep learning for hydrological and environmental modelling, challenges, and research opportunities to move forward. The WG aims to find synergies between isotope techniques, high-frequency or remote sensing, open-source resources, and AI to show how these can help inform policies to mitigate the world’s water problems.
Project 7: Working Group on AI for Safeguards Verification
The Working Group on Artificial Intelligence for Safeguards Verification – established in connection with the IAEA Technical Meeting on Artificial Intelligence (AI) for Nuclear Technology and Application – focused on two different applications of AI in safeguards activities: verification of spent fuel and video surveillance. Using AI for spent fuel verification is extremely relevant to safeguards due to growing inventory of fissile material. Gamma spectroscopy and Cerenkov imaging data are utilized and numerical simulations supply training and test datasets. These AI algorithms are interpretable and can be justified with physics. The accuracy for AI methods in spent fuel verification is sometimes on par with traditional instruments; however, the technology is not mature enough to make autonomous decisions.
Implementing AI for video surveillance could allow for large productivity gains in safeguards. Surveillance is challenging and time-consuming and AI could help with these issues. Data is acquired from similar facilities under surveillance and from simulations/digital twins. Algorithms can be used for many different applications including object detection, object tracking, anomaly detection, and processing of open-source data. Because of the consequences of missed events, improvements are needed to penalize false negatives.
Project 8: Working Group on AI for Nuclear Security (AI4NS)
The Working Group (WG) on Artificial Intelligence for Nuclear Security (AI4NS) – established in connection with the IAEA Technical Meeting on Artificial Intelligence (AI) for Nuclear Technology and Application – discussed AI across different areas of nuclear security, including cyber and information security, forensics, detection, material security, and insider threats. During the AI4NS WG sessions, 16 IAEA Member State experts led discussions on present uses and future opportunities for AI and related technology for Nuclear Security. The experts shared challenges, observations, and lessons learned in developing, using, and regulating AI technology; and discussed potential risks introduced or reintroduced by the use of AI techniques and technology. The need for more collaboration, investigation, and information exchange on the positive and negative impact and implications of AI in NS was identified.
The WG on AI4NS sessions were dedicated to the following thematic areas: anomaly detection; data analysis (flow, sensor, image); data integration; data management; defensive computer security (network) architecture; internet of things – cloud services; information protection; performance assessment; systems design analysis; threat analysis; training; vulnerability management; adversarial AI.
Project 9: Working Group on Artificial Intelligence for Nuclear Power
The Working Group on Artificial Intelligence for Nuclear Power– established in connection with the IAEA Technical Meeting on Artificial Intelligence (AI) for Nuclear Technology and Application – aims at discussing the potential applications of AI, as well as the main opportunities for AI to have positive impact on the nuclear power industry. The broad areas applications such as automation, optimization, analytics, prediction, and insights that can be leveraged to enhance the development and deployment of nuclear power were discussed. AI methods and various data science approaches can be leveraged to predict events, including failures, and assess current asset conditions, such as remaining useful life. It can also be leveraged to expedite the characterization and validation of materials for newer generation designs, reducing the time and cost of the necessary materials research. In addition, to optimize complex processes, plans and strategies such as inventory management, outage scheduling and fuel cycle parameters. Several challenges with respect to deployment were also identified, and suggestions were collected for IAEA with the objective of accelerating progress, including both R&D phase as well as the transition from R&D through deployment. The working group serves as an effective platform to share the ideas and put forth the vision for possible activities in the area of AI for nuclear power.
Project 10: Working Group on Artificial Intelligence for Food and Agriculture
The Working Group on AI for Food and Agriculture – established in connection with the IAEA Technical Meeting on AI for Nuclear Technology and Applications – aimed to promote and enable the use of nuclear and related techniques with AI tools to make agri-food systems sustainable and climate change resilient. Decision-making about agriculture approaches and food production requires gathering extensive information at all scales. Over the past years enhanced data availability through the implementation of open data policies and innovative data acquisition methods enabled the use of Artificial Intelligence to inform policies in the food and agriculture sector. The Working Group on AI for Food and Agriculture highlighted some examples of the use of Artificial Intelligence techniques in applications, such as the estimation of soil moisture through nuclear techniques, but also the remediation of environmental pollution, and the monitoring and prediction of food fraud events. These examples presented opportunities already in hand but also key aspects that still need to be considered to fully exploit the potential of Artificial Intelligence in Food and Agriculture applications.
The expected outcomes of the IAEA’s activities in accelerating the use of AI in Food and Agriculture are that AI would help to fuse and integrate data and datasets from a local to global scale; AI would innovate model development for enhanced decision support and enforcement in a scientific and ethical way (based on FAIR principles – findability, accessibility, interoperability, and reusability); AI would become a mainstream tool for better use of nuclear and isotopic data; and AI will be integrated into education programmes at all levels.
Department/Division: Department of Nuclear Sciences and Applications/Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture
Project 11: Working Group on AI for Human Health
The Working Group on AI for Human Health (WG-AI4HH) – established in connection with the IAEA Technical Meeting on AI for Nuclear Technology and Applications– focuses on possible approaches to the use of AI in specific human health domains. The WG-AI4HH is working on identifying the current and future support that should be provided to Member States in the field of AI applied to radiation oncology, nuclear medicine, medical imaging, medical physics and nuclear nutrition assessment. The potential and challenges of AI are investigated to ensure an informed, safe, ethically responsible and meaningful use of AI-based tools in the clinical environment. The working group is aware that quality of data and their curation is also fundamental to obtain reliable AI applications. Furthermore, the WG-AI4HH is monitoring trends of AI in health education to be eventually considered for education/training activities in the future.