Advancing Large AI Models: Integration of New Data Modalities and Expansion of Capabilities (AI, Data and Robotics Partnership) (RIA)
Programme Category
Programme Name
Programme Description
Horizon Europe is the European Union (EU) funding programme for the period 2021 – 2027, which targets the sectors of research and innovation. The programme’s budget is around € 95.5 billion, of which € 5.4 billion is from NextGenerationEU to stimulate recovery and strengthen the EU’s resilience in the future, and € 4.5 billion is additional aid.
Identifier Code
Call
Summary
This topic centres around the development of innovative multimodal large AI models, covering both the training of foundation models and their subsequent fine-tuning.
Proposals should address at least one of the following focus areas:
- the integration of innovative modalities of data for large AI models during training and inference. Examples of innovative modalities include event streams, structured data and sensor measurements. The incorporation of such new modalities could potentially bring unforeseen enhancements to model performance and enable their application in new domains like weather forecasting, robotics, and manufacturing.
- enhanced multimodal models that exceed the current state of the art, with either significantly improved capabilities or the ability to handle a larger number of modalities. This focus area also encompasses models capable of multi-modal output generation. Current large-scale multimodal models most commonly engage with only vision and language.
Detailed Call Description
Large artificial intelligence (AI) models refer to a new generation of general-purpose AI models (i.e., generative AI) capable of adapting to diverse domains and tasks without significant modification.
Projects should contribute to reinforcing Europe’s research excellence in the field of large AI models by driving substantial scientific progress and innovation in key large AI areas. This includes the development of novel methods for pretraining multimodal foundation models.
Research activities should explore innovative methodologies for enhancing the representation, alignment, and interaction among the different data modalities, thereby substantially improving the overall performance and trustworthiness of these models. Advances in efficient computation for the pre-training, execution and fine-tuning of foundation models to reduce their computational and environmental impact, and increasing the safety of models are also topics of interest.
Proposals should outline how the models will incorporate trustworthiness, considering factors such as explainability, security, and privacy in line with provisions in the upcoming Artificial Intelligence Act. Additionally, the models should incorporate characteristics that align with European values, and provide improved multilingual capabilities, where relevant.
Each proposal is expected to address all of the following:
- Data Collection, Processing and Cross-modal Alignment. The proposal should describe convincingly the characteristics and availability of the large, trustworthy data sources, as well as the trustworthy data processing to be utilised within the project, detailing the data processing steps to ensure reliability, accountability and transparency, and the alignment of data among the different modalities. A modest portion (up to 10%) of the budget may be allocated to data collection activities; proposals may involve relevant data owners in this task, if necessary. Importantly, the proposal should delineate how potential privacy and IPR issues associated with the data will be managed and mitigated.
- Multimodal Foundation Model Pretraining. The pretrained multimodal foundation model is expected to demonstrate high capabilities across a wide range of tasks. The pretraining tasks used should be agnostic of down-stream tasks. These activities also cover the development of the codebase and implementation of small-scale experiments. A minor portion (up to 10%) of the budget may be allocated for the acquisition of computing resources for codebase development and small-scale experiments, though the primary source of computing resources for pretraining should be sought from external high-performance computing facilities such as EuroHPC or National centres. The proposal should describe convincingly the strategy to access these computing resources.
- Fine-Tuning of Multimodal Foundation Models: The proposal should clearly detail the activities pursued to fine-tune the model for diverse downstream tasks demonstrating illustrative potential use-cases. The tasks’ output may either be of a single modality or multimodality. Research activities should investigate innovative methodologies designed to bolster the interplay between different data modalities, thereby enhancing the overall performance of these models.
- Testing and Evaluation: The proposal should detail the development of workflows, benchmarks, testing procedures, and pertinent tools for evaluating both foundation and fine-tuned models. Attention should be paid to the performance, transparency, bias, robustness, accuracy, and security of the models, through appropriate testing procedures (e.g., red teaming for safety and security), in compliance with the future AI Act.
Call Total Budget
Financing percentage by EU or other bodies / Level of Subsidy or Loan
100%
Expected EU contribution per project: €25.00 million
Thematic Categories
- Research, Technological Development and Innovation
Eligibility for Participation
- Researchers/Research Centers/Institutions
Eligibility For Participation Notes
If projects use satellite-based earth observation, positioning, navigation and/or related timing data and services, beneficiaries must make use of Copernicus and/or Galileo/EGNOS (other data and services may additionally be used).
In order to achieve the expected outcomes, and safeguard the Union’s strategic assets, interests, autonomy, and security, participation in this topic is limited to legal entities established in Member States, associated countries, OECD and Mercosur countries, countries with which the EUB cooperates under a Trade and Technology Council, and countries with which the EU has a Digital Partnership. Proposals including legal entities which are not established in these countries will be ineligible.
This decision has been taken on the grounds that, in the area of research covered by this topic, EU open strategic autonomy is particularly at stake.
It is important to avoid a situation of technological dependency on a nonEU source, in a global context that requires the EU to take action to build on its strengths, and to carefully assess and address any strategic weaknesses, vulnerabilities and high-risk dependencies which put at risk the attainment of its ambitions.
For the duly justified and exceptional reasons listed in the paragraph above, in order to guarantee the protection of the strategic interests of the Union and its Member States, entities established in an eligible country listed above, but which are directly or indirectly controlled by a noneligible country or by a non-eligible country entity, may not participate in the action unless it can be demonstrated, by means of guarantees provided by their eligible country of establishment, that their participation to the action would not negatively impact the Union’s strategic, assets, interests, autonomy, or security.
Call Opening Date
Call Closing Date
National Contact Point(s)
Research and Innovation Foundation
29a Andrea Michalakopoulou, 1075 Nicosia,
P.B. 23422, 1683 Nicosia
Telephone: +357 22205000
Fax: +357 22205001
Email: support@research.org.cy
Website: https://www.research.org.cy/en/
Contact Persons:
Dr Angelos Ntantos
Scientific Officer
Telephone: +357 22 205 033
Email: antantos@research.org.cy
Mr George Christou
Scientific Officer
Telephone: +357 22 205 030
Email: gchristou@research.org.cy