KEY COMPETENCE
AI for critical systems
Recent progress in Artificial Intelligence, especially in Machine Learning, has aroused unprecedented interest in these technologies. Many industrial sectors are now considering using them. However, this has led to strong scientific obstacles.
Machine learning, especially deep neural networks, can perform well enough to consider critical applications such as autonomous vehicles, predictive maintenance, and medical diagnosis, but their theoretical properties are not well-known yet.
Objectives
These scientific challenges make it difficult to meet the industrial constraints required for a general application such as certification, qualification, and explainability of algorithms. To this end, our purpose is to create knowledge on:
- Standards, regulations: how to determine new certification guidelines that will increase confidence in complex and adaptive systems;
- Defining the algorithmic and mathematical challenges of integrating machine learning (including neural networks) algorithms in critical systems
- Developing new algorithms and mathematical frameworks with improved properties regarding certification and qualification
- Development process (design pattern) and incremental maintenance/correction of AI-based systems;
- Embedding AIs: quantifying/reducing neural networks implementation, assessing performances, regarding specific infrastructures/material targets.
R&T fields
ROAD MAP
2019 > 2025

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