PLIADES is a ground-breaking project that aims to revolutionize the use of data for significant advances in AI (Artificial Intelligence) and robotics. The overarching goal of the project is to advance the state-of-the-art dataspaces through the development of innovative tools that leverage AI capabilities to optimise the entire lifecycle of data – from creation to application. This includes developing novel approaches to reduce data volume, improve storage efficiency and ensure robust security and privacy measures.
In addition to improving data processing, PLIADES also aims to facilitate the seamless exchange of data between different organisations. By implementing AI-powered systems that are able to understand data content, the aim is to create a standardised framework for data exchange. In addition, the project seeks to promote connectivity and interoperability between different dataspaces, ensuring the quality and integrity of the data.
To test the effectiveness of these concepts, PLIADES plans to implement them in six different scenarios focusing on direct advancements in key AI and robotics technologies for everyday use, in the fields of mobility, healthcare, manufacturing, energy and the environment. These real-life applications will serve as testing grounds and allow the project to evaluate the practicality of its innovations.
PLIADES officially started in January 2024 and will run for three and a half years. The consortium met in the kick-off meeting, held the 17 and 18 January at the NOESIS premises in Thessaloniki. The project it is coordinated by CERTH and consists of a total of 27 partners, including renowned industrial partners (AVL, TNO, Blue Ocean Robotics, etc.) as well as research centres and universities (TECNALIA, KUL, EURECAT, etc.) from 11 countries.
White’s role in this project is two-fold: on the one hand, we are responsible for ensuring the ethical, gender and societal aspects are adequately addressed in the development and deployment of these technologies, considering regulations at national, international and European level. On the other hand, our research focuses on identifying human factors that play an important role across the data life cycle in multiple dataspaces, with the aim to create resilient management processes.
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