Data Infrastructures as a Foundation for AI Projects

This presentation will examine the critical components that drive AI progress, with a focus on the need for large-scale, machine-interpretable data that adapts to the unique demands of different sectors. Key topics will include transparency and reproducibility in AI, as well as the importance of making essential resources—publications, data, models, and code—accessible, interconnected, and scalable.

A robust data infrastructure is essential for the AI community, supporting every stage of the data lifecycle—from collection and creation to processing, analysis, publishing, archiving, and reuse of resources. This talk will highlight the foundational role of these infrastructures in AI-driven initiatives, showcasing several ongoing infrastructure initiatives and illustrating how they contribute to a modern data ecosystem.