MSCA Data-efficient model training for sustainable artificial intelligence (S Wang)

AI has become integral to modern life, with applications ranging from healthcare, medical diagnostics to cultural heritage preservation. Recent progress in foundation models, notably vision-language models (VLMs) and multimodal large language models, has demonstrated remarkable capabilities across diverse tasks. Yet, their training and deployment demand enormous computational resources, creating significant environmental costs and limiting accessibility. Current research emphasises scaling models and datasets to boost performance, but comparatively little attention is given to the usage efficiency and quality of the underlying data.

DeTAI is a Horizon Europe Marie Curie Postdoctoral Fellowship 2025 project involving an innovative, data-driven approach to improve data efficiency in VLM training. By advancing state of the art in data-efficient VLM training, DeTAI promotes sustainable AI development and reliable deployment. It democratises access to foundation model research, particularly for under-resourced groups.