Less accidents on the road, and more operational uptime. That is the expected outcome of this research project which uses data from trucks to develop new machine learning models. The models will let us know when maintenance is needed – before the vehicle breaks down.

Large language models (LLMs) power today’s AI assistants, yet they often behave like black boxes. This project explores why LLMs can be unreliable and how explainability methods can make their decisions more transparent, trustworthy and sustainable.

The aim of this EU project is to increase food safety for citizens. Today there is a wide array of data sources holding crucial information about the food that we eat. The problem is that these sources are heterogeneous – and sometimes hidden. We explore how data can be mined, aggregated and analysed using AI.

Machine learning has been developed and adopted in different areas where both accuracy and interpretability is required. However, most high-performance models lack interpretability. Therefore, additional algorithms are required to guarantee the complex models’ interpretability.

How can we prevent heavy vehicles from breaking down? REMAP develops explainable AI that learns from complex workshop and sensor data, detects changes over time, and updates itself through feedback. The goal is reliable maintenance and sustainable transport.