{"id":118208,"date":"2026-06-24T18:25:30","date_gmt":"2026-06-24T18:25:30","guid":{"rendered":"https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/?p=118208"},"modified":"2026-06-24T18:25:31","modified_gmt":"2026-06-24T18:25:31","slug":"calcolo-scientifico-e-ai-la-nuova-corsa-allenergia","status":"publish","type":"post","link":"https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/calcolo-scientifico-e-ai-la-nuova-corsa-allenergia\/","title":{"rendered":"Calcolo scientifico e AI: la nuova corsa all\u2019energia"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><strong>Tipo di documento:<\/strong> articolo accademico \/ <em>Policy Forum<\/em> pubblicato su <em>Science<\/em>.<br><strong>Link per consultare\/scaricare il documento:<\/strong> <a href=\"https:\/\/www.science.org\/doi\/10.1126\/science.aef4214?utm_source=chatgpt.com\">Science \u2013 \u201cScientific computing in an AI world\u201d<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Un articolo pubblicato su <em>Science<\/em> avverte che il calcolo scientifico deve adattarsi all\u2019era dell\u2019intelligenza artificiale: non basta pi\u00f9 aumentare la potenza dei supercomputer, servono simulazioni integrate con AI, nuovi benchmark e sistemi molto pi\u00f9 efficienti dal punto di vista energetico.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Il calcolo scientifico entra in una fase di trasformazione profonda. Nel <em>Policy Forum<\/em> \u201cScientific computing in an AI world\u201d, pubblicato il 18 giugno 2026 su <em>Science<\/em>, Jack Dongarra, Daniel Reed e Dennis Gannon sostengono che la comunit\u00e0 dell\u2019High Performance Computing non pu\u00f2 pi\u00f9 guidare da sola l\u2019innovazione hardware come in passato: il baricentro tecnologico si \u00e8 spostato verso infrastrutture dominate dall\u2019AI e dai grandi operatori cloud.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Secondo gli autori, la priorit\u00e0 \u00e8 integrare intelligenza artificiale e simulazione scientifica. L\u2019AI pu\u00f2 accelerare modelli climatici, fisici, biologici e ingegneristici, ma la validazione scientifica resta decisiva: dati e modelli diventano \u201ccapitale intellettuale\u201d da preservare, documentare e rendere riutilizzabile. Il lavoro propone anche una suite minima di benchmark costruiti sui flussi di lavoro reali, non solo su misure astratte di velocit\u00e0.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Il tema energetico \u00e8 centrale. L\u2019exascale computing, definito dal Dipartimento dell\u2019Energia statunitense come capacit\u00e0 di eseguire circa 10\u00b9\u2078 operazioni al secondo, promette progressi in energia, salute, sicurezza e ricerca scientifica, ma richiede infrastrutture costose e ad alto consumo. Il programma Exascale Computing Project del DOE, concluso nel 2024, ha coinvolto circa 2.800 collaboratori e 1,8 miliardi di dollari per costruire un ecosistema exascale.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Il messaggio etico \u00e8 chiaro: la scienza computazionale deve evitare una dipendenza opaca da piattaforme chiuse, consumi non sostenibili e modelli non verificabili. La proposta finale \u00e8 un \u201cmoonshot\u201d per ridurre drasticamente l\u2019energia necessaria per ottenere risultati scientifici validati, combinando architetture, software, algoritmi e governance dei dati.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Cronologia essenziale<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2016-2024:<\/strong> il DOE coordina l\u2019Exascale Computing Project per creare l\u2019ecosistema exascale statunitense.<br><strong>18 giugno 2026:<\/strong> <em>Science<\/em> pubblica il <em>Policy Forum<\/em> di Dongarra, Reed e Gannon.<br><strong>2026:<\/strong> il dibattito si concentra su AI, simulazione, energia e benchmark orientati ai workflow.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Approfondimenti consigliati<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/42313946\/?utm_source=chatgpt.com\">Scheda PubMed dell\u2019articolo<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.eurekalert.org\/news-releases\/1132057?utm_source=chatgpt.com\">Comunicato AAAS\/EurekAlert<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.energy.gov\/science\/doe-explainsexascale-computing?utm_source=chatgpt.com\">DOE: che cos\u2019\u00e8 l\u2019exascale computing<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.exascaleproject.org\/?utm_source=chatgpt.com\">Exascale Computing Project<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Abstract: pro, rischi e impatto sociale<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Il vantaggio principale \u00e8 una scienza pi\u00f9 rapida e precisa: AI e simulazione possono accelerare scoperte in clima, medicina, materiali ed energia. I rischi riguardano consumo energetico, concentrazione tecnologica, dipendenza da infrastrutture private, opacit\u00e0 dei modelli e disuguaglianze di accesso. In futuro, la qualit\u00e0 dell\u2019interazione sociale con la scienza dipender\u00e0 dalla capacit\u00e0 di rendere trasparenti dati, modelli, costi ambientali e criteri di validazione.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/wp-content\/uploads\/2026\/06\/calcolo-scientifico-e-ai-la-nuova-corsa-all-energia-1024x576.jpg\" alt=\"Calcolo scientifico e AI: la nuova corsa all\u2019energia\" class=\"wp-image-118213\" srcset=\"https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/wp-content\/uploads\/2026\/06\/calcolo-scientifico-e-ai-la-nuova-corsa-all-energia-1024x576.jpg 1024w, https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/wp-content\/uploads\/2026\/06\/calcolo-scientifico-e-ai-la-nuova-corsa-all-energia-300x169.jpg 300w, https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/wp-content\/uploads\/2026\/06\/calcolo-scientifico-e-ai-la-nuova-corsa-all-energia-768x432.jpg 768w, https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/wp-content\/uploads\/2026\/06\/calcolo-scientifico-e-ai-la-nuova-corsa-all-energia-1536x864.jpg 1536w, https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/wp-content\/uploads\/2026\/06\/calcolo-scientifico-e-ai-la-nuova-corsa-all-energia-696x392.jpg 696w, https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/wp-content\/uploads\/2026\/06\/calcolo-scientifico-e-ai-la-nuova-corsa-all-energia-1068x601.jpg 1068w, https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/wp-content\/uploads\/2026\/06\/calcolo-scientifico-e-ai-la-nuova-corsa-all-energia.jpg 1600w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Tipo di documento: articolo accademico \/ Policy Forum pubblicato su Science.Link per consultare\/scaricare il documento: Science \u2013 \u201cScientific computing in an AI world\u201d Un articolo pubblicato su Science avverte che il calcolo scientifico deve adattarsi all\u2019era dell\u2019intelligenza artificiale: non basta pi\u00f9 aumentare la potenza dei supercomputer, servono simulazioni integrate con AI, nuovi benchmark e sistemi [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":118213,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"footnotes":""},"categories":[429],"tags":[63],"class_list":["post-118208","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ricerca-scientifica-aiopenmind","tag-report"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Calcolo scientifico e AI: la nuova corsa all\u2019energia &#8212; 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