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Celebrating Collaborative Achievements Towards Sustainable AI at GREEN.DAT.AI’s 3rd Plenary Meeting in Maribor, Slovenia

Event, Feb 13-14, 2024

GREEN.DAT.AI’s 3rd Plenary Meeting in Maribor, Slovenia

Maribor, Slovenia – GREEN.DAT.AI proudly announces the successful conclusion of its 3rd Plenary Meeting, held on the 13th and 14th of February 2024, in the charming city of Maribor. This gathering marked a significant milestone in the journey of GREEN.DAT.AI, as partners from across the globe convened to discuss and propel sustainable AI initiatives forward.

Attended by esteemed partners, the meeting served as a platform to exchange ideas, share insights, and showcase the remarkable progress achieved by the project. From groundbreaking algorithms to transformative applications, our collective efforts are reshaping the landscape of AI with sustainability at its core.

We extend our heartfelt gratitude to our gracious hosts, the Univeristy of Maribor and ITC – Innovation Technology Cluster, for their impeccable organization. Their warm hospitality ensured a seamless and productive meeting, leaving all participants inspired and invigorated for the challenges that lie ahead. We also commend Domen Mongus for orchestrating an unforgettable experience, infusing the event with Slovenian charm.

As we continue our journey, we remain committed to fostering collaboration and driving meaningful change through sustainable AI innovation. Together, we are paving the way towards a greener, smarter future.

At GREEN.DAT.AI, our mission is to develop novel Energy-Efficient Large-Scale data analytics services, ready-to-use in industrial AI-based systems, aimed at reducing the environmental impact of data management processes. Central to our vision is the creation of an AI-ready data space, a cutting-edge data management framework tailored to support the application of AI techniques.
Beyond project coordination, INLECOM drives the Data Foundation and Reference Architecture task, and the commercialisation activities.

Learn more about the project

This project has received funding from the Horizon Europe research and innovation programme under GA 101070416. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the granting authority can be held responsible for them.