Projects are expected to contribute to one or more of the following outcomes:
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. Notable examples, such as OpenAI's GPT-4V and META’s Llama 2 or DinoV2, have demonstrated a wide and growing variety of capabilities.
The swift progression of large AI models in recent years holds immense potential to revolutionize various industries, due to their ability to adapt to diverse tasks and domains. For them to achieve their potential, access to vast data repositories, significant computing resources, and skilled engineers is required. A promising avenue of research is the development of multi-modal large AI models that can seamlessly integrate multiple modalities, including text, structured data, computer code, visual or audio media, robotics or IoT sensors, and remote sensing data.
This topic centres around the development of innovative multimodal large AI models, covering both the training of foundation models and their subsequent fine-tuning. These models should show superior capabilities across a wide array of down-stream tasks. The emphasis is both on integrating new input data modalities into large AI models and on developing multimodal large AI models with either significantly higher capabilities and/or the ability to handle a greater number of modalities.
Moreover, 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. Additionally, novel approaches to effective and efficient fine-tuning of such models should be pursued.
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.
Proposals should address at least one of the following focus areas:
Each proposal is expected to address all of the following:
Proposals should adopt a multidisciplinary research team, as appropriate, to cover all the above issues.
Proposals should adhere to Horizon Europe's guidelines regarding Open Science practices as well as the FAIR data principles. Open access should be provided to research outputs - including training datasets, software tools, model architecture and hyperparameters, as well as model weights - unless a legitimate interest or constraint applies. Additionally, proposals are encouraged to deliver results under open-source licenses.
All proposals are expected to embed mechanisms to assess and demonstrate progress (with qualitative and quantitative KPIs, benchmarking and progress monitoring, including participation to international evaluation contests, as well as illustrative application use-cases demonstrating concrete potential added value), and share communicable results with the European R&D community, through the AI-on-demand platform, and Common European data spaces, and if necessary other relevant digital resource platforms in order to enhance the European AI, Data and Robotics ecosystem through the sharing of results and best practice.
Proposals are also expected to dedicate tasks and resources to collaborate with and provide input to the open innovation challenge under HORIZON-CL4-2023-HUMAN-01-04. Research teams involved in the proposals are expected to participate in the respective Innovation Challenges.
This topic implements the co-programmed European Partnership on AI, data and robotics.
Specific Topic Conditions:Activities are expected to start at TRL 2-3 and achieve TRL 4-5 by the end of the project – see General Annex B.
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