A review of green artificial intelligence: Towards a more sustainable future
| UDC.coleccion | Investigación | es_ES |
| UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | es_ES |
| UDC.grupoInv | Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) | es_ES |
| UDC.journalTitle | Neurocomputing | es_ES |
| UDC.startPage | 128096 | es_ES |
| UDC.volume | 599 | es_ES |
| dc.contributor.author | Bolón-Canedo, Verónica | |
| dc.contributor.author | Morán-Fernández, Laura | |
| dc.contributor.author | Cancela, Brais | |
| dc.contributor.author | Alonso-Betanzos, Amparo | |
| dc.date.accessioned | 2024-07-10T11:39:23Z | |
| dc.date.available | 2024-07-10T11:39:23Z | |
| dc.date.issued | 2024-09-28 | |
| dc.description | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | es_ES |
| dc.description.abstract | [Abstract]: Green artificial intelligence (AI) is more environmentally friendly and inclusive than conventional AI, as it not only produces accurate results without increasing the computational cost but also ensures that any researcher with a laptop can perform high-quality research without the need for costly cloud servers. This paper discusses green AI as a pivotal approach to enhancing the environmental sustainability of AI systems. Described are AI solutions for eco-friendly practices in other fields (green-by AI), strategies for designing energy-efficient machine learning (ML) algorithms and models (green-in AI), and tools for accurately measuring and optimizing energy consumption. Also examined are the role of regulations in promoting green AI and future directions for sustainable ML. Underscored is the importance of aligning AI practices with environmental considerations, fostering a more eco-conscious and energy-efficient future for AI systems. | es_ES |
| dc.description.sponsorship | This work was supported by CITIC, a Research Center accredited by Galician University System, which is funded by "Consellería de Cultura, Educación e Universidade from Xunta de Galicia", supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by "Secretaría Xeral de Universidades" (Grant ED431G 2019/01). It was also partially funded by Xunta de Galicia/FEDER-UE under Grant ED431C 2022/44; Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 and "NextGenerationEU"/PRTR under Grants [PID2019-109238GB-C22; PID2021-128045OA-I00; ED2021-130599A-I00] and Ministry for Digital Transformation and Civil Service under grant TSI-100925-2023-1. Funding for open access charge: Universidade da Coruña/CISUG | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431C 2022/44 | es_ES |
| dc.identifier.citation | V. Bolón-Canedo, L. Morán-Fernández, B. Cancela, and A. Alonso-Betanzos, "A review of green artificial intelligence: Towards a more sustainable future", Neurocomputing, Vol. 599, 128096, 28 Sept. 2024, doi: 10.1016/j.neucom.2024.128096 | es_ES |
| dc.identifier.doi | 10.1016/j.neucom.2024.128096 | |
| dc.identifier.issn | 0925-2312 | |
| dc.identifier.uri | http://hdl.handle.net/2183/37880 | |
| dc.language.iso | eng | es_ES |
| dc.publisher | Elsevier B.V. | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128045OA-I00/ES/APRENDIZAJE PROFUNDO ETICO | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-130599A-I00/ES/ALGORITMOS DE SELECCIÓN DE CARACTERÍSTICAS VERDES Y RÁPIDOS | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TSI-100925-2023-1/ES/CÁTEDRA UDC-INDITEX DE INTELIGENCIA ARTIFICIAL EN ALGORITMOS VERDES | es_ES |
| dc.relation.uri | https://doi.org/10.1016/j.neucom.2024.128096 | es_ES |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
| dc.subject | Green machine learning | es_ES |
| dc.subject | Green-by AI | es_ES |
| dc.subject | Green-in AI | es_ES |
| dc.subject | Sustainability | es_ES |
| dc.title | A review of green artificial intelligence: Towards a more sustainable future | es_ES |
| dc.type | journal article | es_ES |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | c114dccd-76e4-4959-ba6b-7c7c055289b1 | |
| relation.isAuthorOfPublication | dfd64126-0d31-4365-b205-4d44ed5fa9c0 | |
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| relation.isAuthorOfPublication | a89f1cad-dbc5-471f-986a-26c021ed4a95 | |
| relation.isAuthorOfPublication.latestForDiscovery | c114dccd-76e4-4959-ba6b-7c7c055289b1 |
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