The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities. It has two main strategic goals: 1) to develop the next generation of decision-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with the resilience, safety, and security of critical infrastructures as core requirements, and 2) to boost the development and validation of novel AI algorithms, by the consortium and AI community, through existing open-source digital environments capable of emulating realistic scenarios of physical systems operation and human decision-making. The core elements are: a) AI algorithms mainly composed by supervised and reinforcement learning, unifying the benefits of existing heuristics, physical modelling of these complex systems and learning methods, as well as, a set of complementary techniques to enhance transparency, safety, explainability and human acceptance; b) human-in-the-loop decision making for co-learning between AI and humans, considering integration of model uncertainty, human cognitive load and trust; c) autonomous AI systems relying on human supervision, embedded with human domain knowledge and safety rules. The AI4REALNET framework will be validated in 6 uses cases driven by industry requirements, across 3 network infrastructures with common properties. The use cases are focused on critical challenges and tasks of network operators, considering strategic long-term goals, such as decarbonisation, digitalisation, and resilience to disturbances, and are formulated in a unified sequential decision problem where many AI and non-AI algorithms can be applied and benchmarked.

AI-EFFECT will establish a European Testing Experimentation Facility (TEF) for developing, testing, and validating AI applications in the energy sector. It will be distributed across nodes, virtually connecting existing European facilities. The solution includes a digital platform leveraging European building blocks for interoperability, flexibility, and scalability. AI-EFFECT aims to be a central hub for testing energy sector AI algorithms, fostering collaboration across utilities, industry, academia, and regulatory authorities. Resilience is ensured through a decentralized design, aligning with the EU Energy Data Spaces framework. The project involves developing 4 use cases/nodes addressing key energy challenges, focusing on district heating, transmission congestion management, DERs integration, and energy communities. The framework involves utilities proposing challenges, vendors developing algorithms, and researchers contributing solutions. Each use case has evaluation criteria, baselines, and benchmarks. AI certification procedures, including interpretability and verification, will be implemented, and the evaluation process will be automated. Benchmarks and certifications are publicly available, encouraging open-source contributions. The project breaks sector barriers, leveraging existing infrastructures and technologies for cross-sectoral collaboration. The platform enforces policies for data quality, integrity, and privacy, promoting controlled data sharing and collaboration. Secure APIs ensure controlled interactions, including risk and security assessments. The consortium explores certification, standardization, and quality requirements in line with the EU AI Act. Governance and business models for the enduring AI-EFFECT will be examined, considering the EU AI Act. The consortium aims to make AI-EFFECT a sustained business beyond initial funding, seeking input from members, other TEFs, and regulatory authorities for the preferred model.

ELISE aims to make Europe competitive in AI through a network of excellence. The best European researchers in machine learning and AI have worked together to attract talent, to foster research through collaboration, and to inspire and be inspired by industry and society. While ELISE starts from machine learning as the current most prominent method of AI, the network invites in all ways of reasoning, considering all types of data, applicable for almost all sectors of science and industry. While being aware of data safety and security, and while striving to explainable and trustworthy outcomes we aim to create a force to Europe. ELISE will run a PhD student and a postdoc programme to attract and to educate world-class talents to Europe. It will operate a Fellows programme for groundbreaking research and high-profile workshops to develop AI methods applications further. Industry involvement is guaranteed by the many connections members of ELISE have with industry, on average one for every member and one start-up for every second member of ELISE. ELISE will demonstrate a fraction of their research in use cases to be implemented in AI4EU and the SME’s of Europe. Additional impact will be created to SME’s through open calls. The current practice of ELISE members of spin-off research in SME’s once a break-through is achieved will be stimulated through incubators. The current practice of participating in dissemination and debate that many members of ELISE are used to will be continued to develop a mature acceptance of AI throughout Europe for the benefit of all and in cooperation with all. ELISE is built on 105 organisations in total, in which the 202 core contributors have actively indicated they will help build and profit from the networks of PhD-students and scholars. ELISE includes 60 ERC grants of their active supporters. By their citation and other accepted scores of scientific quality, ELISE is the network that combines in Europe excellence in AI.