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

Project description

Developing open multimodal foundation models for real-world, data-rich applications

Leveraging Europe’s AI talent and supercomputing infrastructure, the EU-funded ELLIOT project will develop a family of open trustworthy Multimodal Generalist Foundation Models: AI systems designed to learn general knowledge and patterns from massive amounts of data of various types – from videos, images and text to sensor signals, industrial time series and satellite feeds – and efficiently transfer the knowledge gained to different downstream tasks. Unlike current foundation models, which face significant challenges in terms of generalisation and support for multimodal data, ELLIOT’s models will be capable of robust generalisation and reasoning across conditions not seen during training, coping well with dynamic, noisy, temporally-evolving multimodal data streams. This will empower new applications for media, earth modelling, robots, mobility, computer engineering and workflow automation.

Objective

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.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-CL4-2024-HUMAN-03

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Coordinator

ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 4 410 862,50
Address
CHARILAOU THERMI ROAD 6 KM
57 001 THERMI THESSALONIKI
Greece

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Region
Βόρεια Ελλάδα Κεντρική Μακεδονία Θεσσαλονίκη
Activity type
Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 4 410 862,50

Participants (29)

Partners (2)

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