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October 25, 0202

IIT has three new projects in the big wave of 42 AI & Robotics European projects

For the first time ever, the European Commission (EC) has simultaneously launched 42 new projects in the field of Artificial Intelligence (AI) and Robotics of the Horizon Europe programme between June and November 2022. The Institute of Informatics and Telecommunications has won three new projects in the domain and is part of the wider ecosystem of these projects.

This launch event was organised by the EC and was held online on 17 October 2022 bringing together 42 newly funded projects (full list available here) arising from themes that improve the society we live in, addressing important technological or application-driven challenges, as prioritised in the strategy developed by the ADRA, public-private partnership for Artificial Intelligence, Data and Robotics.

The event is available to watch here:


More specifically, IIT participates as a partner in three projects – AI4EUROPE, ENEXA, EVENFLOW  on the following Horizon Europe topics:

AI4EUROPE aims to develop and maintain the AI on-demand Platform (AIOD), a resource for the research community, facilitating experimentation, knowledge sharing and the development of stateof- the-art AI solutions in Europe.

ENEXA focuses on efficient explainable ML on polymorphic knowledge graphs. We aim to develop scalable, hybrid and symbolic ML algorithms for large, real-world, noisy knowledge graphs able to achieve precise and explainable predictions. The rationale behind the ENEXA concept lies in exploiting the polymorphy of knowledge graphs, especially the act that KGs can be represented as using several representations, e.g., tensors and formal logics. These different representations allow for different types of ML algorithms to be implemented on KGs and used as proxies for each other.

EVENFLOW approached is the online nature of the learning methods, which makes them applicable to evolving data flows and allows to utilize rich domain knowledge that is becoming available progressively. To deal with the brittleness of neural predictors and the high volume/velocity of temporal data flows, the EVENFLOW techniques will rely on novel, formal verification techniques for machine learning, in addition to a suite of scalability algorithms for federated training and incremental model construction. The learnt forecasters will be interpretable and scalable, allowing for fully explainable insights, delivered in a timely fashion and enabling proactive decision making.


Photo gallery of the event

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