Imagine if AI could think and learn without needing massive, power-hungry data centers. This project moves the 'brain' of the AI closer to where the data is actually born, like on a sensor or a local device. It's like switching from a giant central power plant to small, efficient solar panels on every roof to save energy and keep data private.
AI systems currently consume massive amounts of energy and produce a high carbon footprint. Businesses struggle to implement powerful AI at the edge without sacrificing performance or increasing operational costs.
An open-source, AI-ready Data Space platform and a TRL7/8 Toolbox of energy-efficient AI services including Federated Learning and AutoML.
If you are a farming service provider dealing with inefficient resource use — this project developed a digital twin solution that employs farming optimisation techniques. This allows for better crop management while reducing the energy cost of the AI used to run it.
If you are a bank dealing with slow or energy-intensive fraud detection — this project developed models for near-real time fraud detection and interpretable feature learning. This helps discover hidden patterns without the massive carbon footprint of traditional AI.
If you are a fleet operator dealing with unpredictable charging demands — this project developed a platform that collects EV data and uses forecasting algorithms to manage charging needs. It uses learning-based prediction to optimize energy demand in electric vehicles.
Based on available project data, specific pricing is not mentioned, but the project aims to deliver an open-source platform and a go-to-market Toolbox.
Yes, the project focuses on large-scale data analytics services and uses 6 distinct pilots across 4 industries to prove scalability.
The project intends to deliver an open-source platform, though specific licensing terms for the TRL7/8 Toolbox are not detailed in the provided text.
It uses Data Spaces to ensure interoperability and data sovereignty, allowing for seamless data exchange between different industrial systems.
The project period runs from 2023-01-01 to 2025-12-31, with the goal of delivering a validated go-to-market Toolbox by the end of the term.
The consortium is heavily industry-driven, with 14 industrial partners (67% of the group) and 5 SMEs. This high ratio of commercial entities across 10 countries suggests the project is focused on practical application and market adoption rather than pure academic research.
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