The need to implement complex physics systems is critical across various scientific and engineering domains. However, traditional numerical models for simulating these systems are computationally expensive, requiring significant time, resources, and cost. Recent advancements in AI present a promising alternative, with AI models demonstrating the ability to capture the dynamics of complex physical systems. Despite these successes, AI models suffer from key limitations, including challenges with generalization, vulnerability to bias, ethical concerns, and accuracy, particularly when applied to unseen tasks or variable-range predictions. These limitations are collectively viewed as issues of robustness. The TURING project aims to address these shortcomings by developing robust AI-driven solutions. It integrates multidisciplinary advancements from Machine Learning, Computer Engineering, Physics, and SSH to pre-train generative, multimodal foundation models capable of capturing the physics of dynamic systems that share common properties. Starting with a cautious approach, the models will incorporate representations of increasingly complex physical systems as robustness is ensured. Once pre-trained, these foundation models will be fine-tuned for specific tasks, enhancing their domain-specific robustness. The tasks will target critical engineering and physics problems in nuclear energy, particle physics, and meteorology, which are of high priority for the EU. The task-specific and foundation models, collectively termed "TURING models", will be developed in collaboration with partners from India, Canada, and Switzerland. To maximize the impact of TURING models, the project will ensure compliance of its activities with regulations such as the EU AI Act and then publicly release those models, along with the TURING Framework (MLOps SW tools and web-based app with conversational capabilities), enabling developers and end users to leverage this technology for their applications.

Science is clear: Artificial Intelligence will be the defining development of the 21st century. Experts estimate that due to the rise of AI within only 2 decades aspects of daily human life will be unrecognisable. The influence of AI is about to challenge the very organising principles of our economic and social order. It can generate unprecedented wealth, revolutionise medicine and education, but it can bring existential perils for life as we know it. This makes the EC2020 report that the EU is lagging behind USA and Asia in AI adoption and development all the more worrying. Among many reasons behind that, the lack of skilled workforce is definitively one of the more prominent ones. The objective of AI4VET4AI is to contribute to the digital transformation of the EU labour market by adding new innovative teaching content and methods to VET curricula across 11 European countries and 18 EU NUTS2 regions, in order to support the growth of AI-skilled workers. We start from the ground up: we investigate the most potent sectors for AI deployment in our 17 regions, and for those sectors, in close cooperation with enterprises and their cluster organisations we create 14 MOOCs and TT materials that can easily be implemented in VET programmes (IVET and CVET). We organise 11 innovative AI VET campuses and 7 VET innovation AI incubators, in which VET learners hone their creative and entrepreneurial skills. We use project activities to connect partners closely and to raise awareness of the AI potential in our regions among representatives of public and private sector, as well as civil society. In turn, this helps us to create a joint and active platform of concerned EU citizens and institutions, interested, informed and motivated in supporting AI development further -- this being the basis of our ambitious CoVE, which aims to attract many more institutions (HEIs, VETs, companies, agencies, individuals) in its pursuit of sustainable, inclusive and just AI-powered future for all.