{"id":117954,"date":"2026-06-20T11:08:30","date_gmt":"2026-06-20T11:08:30","guid":{"rendered":"https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/?p=117954"},"modified":"2026-06-20T11:08:31","modified_gmt":"2026-06-20T11:08:31","slug":"ia-sostenibile-la-rete-ant-promette-meno-energia","status":"publish","type":"post","link":"https:\/\/www.aiopenmind.it\/ArtificialIntelligence\/ia-sostenibile-la-rete-ant-promette-meno-energia\/","title":{"rendered":"IA sostenibile: la rete ANT promette meno energia"},"content":{"rendered":"\n

Link per scaricare\/consultare il documento:<\/strong>
https:\/\/www.umass.edu\/news\/article\/next-gen-ai-can-learn-continuously-while-consuming-fraction-computing-energy-required<\/a><\/p>\n\n\n\n

Tipo di documento:<\/strong> notizia\/comunicato universitario su ricerca scientifica. Il documento tecnico collegato \u00e8 un articolo accademico peer-reviewed pubblicato su Nature Communications<\/em>: \u201cTuring universal neural networks do not require global clocks\u201d.<\/p>\n\n\n\n

Una ricerca dell\u2019Universit\u00e0 del Massachusetts Amherst propone una nuova architettura di intelligenza artificiale, chiamata ANT, capace di apprendere in modo continuo e asincrono. L\u2019obiettivo \u00e8 ridurre drasticamente il consumo energetico rispetto ai sistemi di IA oggi basati su calcoli sincronizzati e data center ad alta intensit\u00e0 elettrica.<\/p>\n\n\n\n

Come funziona l\u2019IA ANT ispirata al cervello<\/h1>\n\n\n\n

L\u2019intelligenza artificiale di nuova generazione potrebbe diventare pi\u00f9 efficiente copiando un principio biologico: il cervello non attiva tutti i neuroni nello stesso momento. Il gruppo guidato da Hava Siegelmann, docente al Manning College of Information and Computer Sciences dell\u2019UMass Amherst, ha sviluppato le Asynchronous Neural Turing networks<\/strong>, o ANT<\/strong>, un\u2019architettura che elimina la necessit\u00e0 di un \u201corologio globale\u201d nei calcoli neurali.<\/p>\n\n\n\n

Nei modelli attuali, milioni o miliardi di parametri vengono aggiornati in modo sincronizzato. Questo sistema ha favorito l\u2019espansione delle reti neurali profonde, ma comporta costi energetici crescenti. ANT, invece, aggiorna solo i neuroni necessari in una specifica fase computazionale, mantenendo la possibilit\u00e0 di apprendimento differenziabile che rende efficaci le reti moderne.<\/p>\n\n\n\n

Energia, data center e sostenibilit\u00e0 dell\u2019IA<\/h1>\n\n\n\n

Il tema \u00e8 centrale. Secondo l\u2019Agenzia internazionale dell\u2019energia, il consumo elettrico globale dei data center potrebbe quasi raddoppiare entro il 2030, raggiungendo circa 945 TWh. In questo scenario, l\u2019efficienza non \u00e8 solo una questione tecnica, ma anche ambientale, industriale e geopolitica.<\/p>\n\n\n\n

La ricerca pubblicata su Nature Communications<\/em> indica che reti neurali universali secondo Turing non richiedono necessariamente aggiornamenti globali sincronizzati. \u00c8 un risultato teorico, ma potenzialmente importante per robotica, edge computing, veicoli autonomi e dispositivi intelligenti con limiti energetici stringenti.<\/p>\n\n\n\n

Opportunit\u00e0 e limiti etici<\/h1>\n\n\n\n

ANT non risolve da sola l\u2019impatto ambientale dell\u2019IA, n\u00e9 dimostra ancora un\u2019applicazione industriale su larga scala. Tuttavia, apre una direzione: costruire sistemi pi\u00f9 adattivi, meno energivori e capaci di apprendere in tempo reale. Il rischio \u00e8 che maggiore efficienza produca anche maggiore diffusione dell\u2019IA, aumentando sorveglianza, automazione opaca e dipendenza tecnologica. La sfida sar\u00e0 collegare innovazione, trasparenza, governance e sostenibilit\u00e0.<\/p>\n\n\n\n

\n\n\n\n

Breve approfondimento: cronologia essenziale<\/h2>\n\n\n\n

1995<\/strong> \u2014 Hava Siegelmann dimostra il potenziale computazionale delle reti neurali ricorrenti in relazione alle macchine di Turing.
2023-2025<\/strong> \u2014 Cresce l\u2019interesse per NeuroAI, calcolo neuromorfico e architetture ispirate al cervello.
2025<\/strong> \u2014 L\u2019IEA pubblica analisi sul rapporto tra energia e intelligenza artificiale, segnalando la crescita della domanda elettrica dei data center.
8 giugno 2026<\/strong> \u2014 UMass Amherst annuncia la ricerca su ANT, pubblicata su Nature Communications<\/em>.<\/p>\n\n\n\n

Consigli di approfondimento<\/h2>\n\n\n\n
    \n
  • UMass Amherst \u2014 comunicato sulla ricerca ANT<\/li>\n\n\n\n
  • Nature Communications \u2014 articolo scientifico \u201cTuring universal neural networks do not require global clocks\u201d<\/li>\n\n\n\n
  • International Energy Agency \u2014 report \u201cEnergy and AI\u201d<\/li>\n\n\n\n
  • Nature Communications \u2014 studi su NeuroAI e calcolo neuromorfico<\/li>\n<\/ul>\n\n\n\n

    Abstract: pro, rischi e conseguenze future<\/h2>\n\n\n\n

    La nuova architettura ANT suggerisce che l\u2019intelligenza artificiale possa apprendere in modo continuo consumando meno energia, con potenziali benefici per sostenibilit\u00e0, robotica autonoma, dispositivi edge e riduzione della pressione sui data center. I rischi riguardano l\u2019effetto rimbalzo: sistemi pi\u00f9 efficienti potrebbero accelerare la diffusione pervasiva dell\u2019IA, ampliando sorveglianza, automazione decisionale e disuguaglianze tecnologiche. Le conseguenze sociali dipenderanno dalla capacit\u00e0 di integrare queste innovazioni con regole su trasparenza, sicurezza, responsabilit\u00e0 e impatto ambientale.<\/p>\n\n\n\n

    \"IA<\/figure>\n","protected":false},"excerpt":{"rendered":"

    Link per scaricare\/consultare il documento:https:\/\/www.umass.edu\/news\/article\/next-gen-ai-can-learn-continuously-while-consuming-fraction-computing-energy-required Tipo di documento: notizia\/comunicato universitario su ricerca scientifica. Il documento tecnico collegato \u00e8 un articolo accademico peer-reviewed pubblicato su Nature Communications: \u201cTuring universal neural networks do not require global clocks\u201d. Una ricerca dell\u2019Universit\u00e0 del Massachusetts Amherst propone una nuova architettura di intelligenza artificiale, chiamata ANT, capace di apprendere in modo […]<\/p>\n","protected":false},"author":1,"featured_media":117985,"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-117954","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ricerca-scientifica-aiopenmind","tag-report"],"acf":[],"yoast_head":"\nIA sostenibile: la rete ANT promette meno energia — AIopenmind<\/title>\n<meta name=\"description\" content=\"IA sostenibile: la rete ANT di UMass promette apprendimento continuo con meno energia e nuovi scenari etici.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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