GSS EU Science Funding Gateway

The proposals must address in particular the following objectives:

• Design of relevant military use cases where trustworthy and frugal AI algorithms are needed (i.e. targeting specific tasks for which data-greedy algorithms are currently outperforming other methods), and for which representative data can be collected and performance can be measured in an objective way.
• Development of new methods for reducing the need for data and supervision to train and adapt AI systems (and simultaneous monitoring of the state of the art, which is important in this quickly evolving domain but should take place as a background activity integrated with system development), for example through:
  • simulation and generative models,
  • transfer, semi-supervised, self-supervised, and active learning,
  • hybridisation with user-defined rules.
• Development of new methods for improving robustness guarantees by design, for example through:
  • new algorithms increasing the robustness of existing neural networks methods that are intrinsically not robust,
  • evaluation and surveillance of both the output space and the environment.
• Implementation of benchmarking experiments on the use cases to demonstrate the advantages and drawbacks of the proposed methods.