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AI in the EGI e-infrastructure -- Past experiences and future strategies — arXiv:2610.40494

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40494
License (as archived): CC0-1.0
Archived: 2026-10-31T06:14:57.542Z
SHA-256 of the archived text: 9c52a8c02e44d2425d08139452380407424757d22d8fb479275967e6b0b643ba
Stable URL of this page: https://metraipolis.com/documento/dc_579cfee207fe

How to cite

AI in the EGI e-infrastructure -- Past experiences and future strategies — arXiv:2610.40494 — arXiv (en), https://arxiv.org/abs/2610.40494 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_579cfee207fe (Metraipolis archive dc_579cfee207fe, sha256 9c52a8c02e44d2425d08139452380407424757d22d8fb479275967e6b0b643ba)

BibTeX

@misc{dc579cfee207fe,
  title = {AI in the EGI e-infrastructure -- Past experiences and future strategies — arXiv:2610.40494},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40494},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_579cfee207fe; sha256 9c52a8c02e44d2425d08139452380407424757d22d8fb479275967e6b0b643ba},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40494. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The EGI Federation has provided federated e-infrastructure services to European and global science for over 15 years. We review EGI's early experiences with AI in the interTwin and iMagine Horizon Europe projects, which built digital twins in physics and environmental sciences and an image-analysis platform for aquatic research. We then examine how the AI landscape has changed since 2023 and argue that the focus of AI in science has shifted from model training to adapting and applying AI to specific domains, projects and questions. Building on these lessons, we present the AI strategy emerging from the EGI Federation Strategy 2026-2030, organised around three enablers: (1) an AI-ready infrastructure connecting compute, data and application resources across the continuum; (2) human skills to know what is possible with AI and how to implement it; and (3) trust, to run AI with confidence even in high-risk environments. We describe the projects implementing each enabler, including federated LLM inference, AI-ready data platforms and AI competence centres, and discuss a division of labour with EuroHPC's AI Factories, RAISE, research infrastructures and EOSC, in which EGI specialises in the customisation and long-term operation of AI for research.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2610.40494 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T06:07:08.777Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).