Projects are expected to contribute to one or more of the following outcomes:
- Enhanced applicability of large AI systems to new domains through the integration of innovative data modalities, such as sensor measurements (e.g. in robotics, IoT) or remote sensing (e.g. earth observation), as input.
- Improvement of current multimodal large AI systems capabilities and expansion of the number of data modalities jointly handed by one AI system, leading to broader application potential and improved AI performance.
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
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