The rapid development and adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies have brought significant opportunities and challenges. While AI has the potential to revolutionise industries and improve lives, there are growing concerns related to privacy, security, fairness, transparency and the environmental footprint. The Olympics motto "Faster, Higher, Stronger" also applies to recent impressive AI advancements, but now is the time to update it to "Lighter, Clearer, Safer". We propose ACHILLES to build an efficient, compliant, and trustworthy AI ecosystem. At its core is an iterative development cycle inspired by clinical trials encompassing four modules. It begins with human-centric methodologies, followed by data-centric operations, model-centric strategies, and deployment-centric optimisations. It returns to human-centric approaches, focusing on explainability and model monitoring. This iterative cycle aims to enhance AI systems' performance, robustness and efficiency while ensuring they comply with the legal requirements and highest ethical standards. Another innovation is the development of an ML-driven Integrated Development Environment (IDE). The ACHILLES IDE will facilitate seamless integration between the iterative cycle's modules, enabling users to develop efficient, compliant, and trustworthy AI solutions more effectively and responsibly. The project aims to significantly impact European AI development, aligning with the region's guidelines and values. Through innovative techniques and methodologies based on the collaboration of a multidisciplinary team of 16 partners from 10 countries, ACHILLES will foster a strong AI ecosystem that respects privacy, security, and ethical principles across various sectors. By validating the results in real use cases (including healthcare, ID verification, content creation and pharmaceuticals), ACHILLES will showcase its practical applicability and potential for widespread adoption.

Gathering 17 partners from 7 countries, METHAREN aims to demonstrate a cost-effective, innovative, more sustainable and circular biomethane production system enabling renewable energy sources intermittency management. To do so, METHAREN is providing improvements beyond the state-of-the art along four main axes related to: i) the biogas plant efficiency; ii) flexibility and energy management for RES integration; iii) the circularity approach for sustainable production and iv) innovative business models and adapted policies. The consortium will use the results of the engineering specifications (WP1) to develop the technologies related to the gasification plant (WP2), the methanation plant (WP3) and the development of the circularity (WP4) in the various processes as METHAREN will reuse water, O2 from the electrolysis, and heat to foster overall efficiency and sustainability. All individually developed and tested systems will then be integrated and tested in a pilot site (WP5) before being operated and optimised for more than a year (WP6). In parallel, the market uptake and exploitation of solutions will be carried out all along the project to ensure that the technologies develop will answer market needs (WP7) while dedicated communication and dissemination activities (WP8) will ensure that the results of the project are known and used by relevant stakeholders. Coordinated by a European industrial engineering world leader, the management of the project (WP9) will also be ensured by WP leaders, who all have strong experience and excellent expertise in their fields as research and technical centres, engineering development companies, or industrials. This combined expertise will allow METHAREN to demonstrate an increase of cost effectiveness by at least 20% while reaching a carbon conversion rate from biowaste to methane higher than 80%, a reduction of GHG emission compared to current process by 50% and the potential of replication on at least 30 other sites in Europe.

Europe is poised to play a pivotal role in the technological revolution, particularly in the field of edge AI, which promises sustainable growth, performance, and reliability. The NeAIxt project is a strategic initiative designed to foster European independence and control over edge AI technology, benefiting both companies and citizens. The project presents a golden opportunity for European SMEs to grow, network, and enhance skills, leveraging exposure to the global market. Research labs and RTOs will bridge the gap to the future by developing necessary technologies and competencies, while universities will cultivate and disseminate advanced skills required for this technological evolution. NeAIxt aims to solidify Europe's position in edge AI and eNVM technology by enhancing AI enablers, evolving eNVM for edge applications, and demonstrating AI capabilities at both chip and system levels. The project is committed to ensuring the safety and security of AI services, adhering to EU regulations. Key technical developments include the advancement of 18nm FD-SOI and next-generation embedded Phase Change Memory (ePCM). These innovations will lead to high-performance, secure microcontrollers with AI capabilities, offering low power consumption and high security for smart applications. The project will integrate advances in non-volatile memory technologies with cutting-edge MCU design to enable efficient in-memory computing. This synergy will deliver a fully European solution for reliable, safe, and independent edge AI applications. NeAIxt will address the entire edge AI value chain, from academia to industry, and from design to end-user applications, building on Europe's strong technological foundation. The project's outcomes will alleviate societal concerns about AI proliferation by ensuring compliance with European privacy standards, fostering AI adoption in various sectors.

AISym4Med aims at developing a platform that will provide healthcare data engineers, practitioners, and researchers access to a trustworthy dataset system augmented with controlled data synthesis for experimentation and modeling purposes. This platform will address data privacy and security by combining new anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems. Moreover, data quality controlling measures, such as unbiased data and respect to ethical norms, context-aware search, and human-centered design for validation purposes will also be implemented to guarantee the representativeness of the synthetic data generated. Indeed, an augmentation module will be responsible for exploring and developing further the techniques of creating synthetic data, also dynamically on demand for specific use cases. Furthermore, this platform will exploit federated technologies for reproducing un-indentifiable data from closed borders, promoting the indirect assessment of a broader number of databases, while respecting the privacy, security, and GDPR-compliant guidelines. The proposed framework will support the development of innovative unbiased AI-based and distributed tools, technologies, and digital solutions for the benefit of researchers, patients, and providers of health services, while maintaining a high level of data privacy and ethical usage. AISym4Med will help in the creation of more robust machine learning (ML) algorithms for real-world readiness, while considering the most effective computation configuration. Furthermore, a machine-learning meta-engine will provide information on the quality of the generalized model by analyzing its limits and breaking points, contributing to the creation of a more robust system by supplying on-demand real and/or synthetic data. This platform will be validated against local, national, and cross-border use-cases for both data engineers, ML developers, and aid for clinicians’ operations.