An important crosscutting need for Artificial Intelligence is to create technologies for trustworthy autonomous and frugal learning, i.e. the ability of a system to adapt and learn from its environment, including from user supervision, for a reasonable cost and without intervention from expert developers nor regression. Such technologies can be highly disruptive and have high impacts for many capabilities, especially when the information to manage is highly variable or unpredictable and high adaptability is needed. These technologies can also alleviate the current need to provide data to the system developers to get improvements depending on such data, which can be critical when the data is confidential, and is thus critical for defence. They can more generally enhance technological independence. Selected actions should include the organisation of technological challenges addressing well-defined goals in order to bootstrap and drive progress toward answering identified defence needs, while leveraging civil research and generating spill over effects. Within the FaRADAI project, current advances in AI technologies will be thoroughly researched in parallel with a detailed study of the main challenges imposed by a defence system. Aiming at significant breakthroughs in AI, the models will accelerate their wider application and deployment in defence systems increasing their impact and the overall performance

MKLab acts as the project’s coordinator and scientific manager. In addition, the team is leading the development of machine learning technologies that are continuously trained by incrementally exploiting incoming and considering previously collected data. In addition, MKLab leads the developments of AI-based multimodal fusion schemes that merges outcomes of different components and modalities in a complementary manner as well as significantly involved in the developments of frugal AI techniques by processing visual data.




  • Ioannidis Konstantinos
  • Vrochidis Stefanos