ELLIOT

European Large Open Multi-Modal Foundation Models For Robust Generalization On Arbitrary Data Streams

01/07/2025 to 30/06/2029

For improving the capabilities of general-purpose AI models and for extending their applicability to domains where the temporal
dimension – among several others – is of importance, we will target the development of the next generation of Multimodal Space-Time
Foundation Models (MSTFMs). These will combine spatio-temporal understanding, which is important even for modalities such as the
visual one that have already been introduced in large generative models, with the effective management of new time-relevant modalities
that are yet to be supported in foundation models, such as industrial time series data, remote sensing data and health-related measurements.
Real and synthetic data, to mitigate data scarcity, will be leveraged for training general-purpose MSTFMs and for further adapting them
for specific downstream tasks. Real data used for training will include data directly provided by members of the consortium as well as
data from relevant European Data Spaces, while complementary synthetic data will be generated by exploiting existing generative AI
capabilities as well as new ones developed in the project. European HPC infrastructure is directly included in the consortium to ensure
the availability of the necessary computing resources.