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artificiale",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1454163424":{"id":"flexible-column-container-2d6be0d587","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"seo-pillar-hero__content","layout":"SIMPLE","id":"container-93b68f233b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy":{"id":"text-6b94424e32","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGuida di riferimento\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-066ece7a6a","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Intelligenza artificiale: la guida completa"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-9f0d8f83e6","additionalClasses":"seo-hub-hero__subheadline","text":"\u003Cp\u003EIn ambito aziendale l’AI è già in produzione, ma architettura, strategia dati e governance devono ancora adeguarsi. Questa guida illustra come vengono sviluppati i sistemi di AI, dove hanno successo o falliscono e quali aspetti devono considerare le imprese per gestirli in modo efficace.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-78264fdc81",":type":"snowflake-site/components/container",":items":{"content_chip_copy":{"id":"content-chip-260be882c0","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d2c1126d-3244-486b-9007-1d2bc56e1893/laurie-macpherson.jpg?quality=85&preferwebp=true","alt":"Laurie MacPherson","lazyEnabled":true,"isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip":{"id":"content-chip-de3f72a0b5","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/david-gaule/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--af497c82-10f0-4f45-805e-7f1c4677a824/david.jpg?quality=85&preferwebp=true","alt":"David Gaule","lazyEnabled":true,"isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Technical Editor, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip_copy","content_chip"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["text_copy","title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"additionalClasses":"seo-hub-hero__video","layout":"SIMPLE","id":"container-416b0154f2",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-1eb34ce851","youtubeVideoId":"-HWNc-Hd90U","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_841934424":{"id":"flexible-column-container-85a5008713","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"border-top","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container","layout":"SIMPLE","id":"container-41e697b3e4",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-898ed8127c","text":"\u003Cp\u003EApprofondimenti sull’intelligenza artificiale:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-f38ffebf8c","additionalClasses":"related-topics","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-compliance/\"\u003ECompliance dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-ethics/\"\u003EEtica dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/\"\u003EGovernance dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/business\"\u003EAI in azienda\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/observability/\"\u003EOsservabilità dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EGestione del rischio dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003ETracciabilità dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003ETrasparenza dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003EBias algoritmico\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/deep-learning/\"\u003EDeep learning\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EAI Act dell’Unione europea\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/feature-engineering/\"\u003EFeature engineering\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/iso-42001/\"\u003ENorma ISO/IEC 42001\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/\"\u003EMachine learning\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/inference/\"\u003EInferenza ML\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/responsible-ai/\"\u003EAI responsabile\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont","flexible_column_cont_1454163424","flexible_column_cont_841934424"],"appliedCssClassNames":"snowflake-container"}},":itemsOrder":["container"],"appliedCssClassNames":"ui-background-05 snowflake-responsive-container-inner-padding-small"},"container_626207224_":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"container_1561483981_602520842":"aem-GridColumn aem-GridColumn--default--12","container_805795510":"aem-GridColumn aem-GridColumn--default--12","container_1561483981":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"hubBody",":type":"snowflake-site/components/container",":items":{"container_805795510":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-f14ce07b46",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-0013d860d8","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"hub-body","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"container-b9b2fdec9e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_1521899678":{"id":"text-45e6d270f7","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EINTELLIGENZA ARTIFICIALE: LA DEFINIZIONE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EL’intelligenza artificiale è l’ampio campo dei sistemi in grado di svolgere task associati al ragionamento umano, come il riconoscimento di pattern, la previsione, la comprensione del linguaggio e il supporto decisionale.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-f480d45262","text":"\u003Cp\u003ELa maggior parte delle organizzazioni aziendali ha già intrapreso il proprio percorso nell’intelligenza artificiale: dispone di modelli in produzione e progetti pilota in corso, mentre il dibattito sulla governance deve ancora adeguarsi al ritmo del deployment. I team devono ora scegliere l’architettura AI, le dipendenze dai dati e la strategia di valutazione appropriate, applicando controlli coerenti di governance, sicurezza e operatività lungo l’intero ciclo di vita dell’AI, spesso tramite pratiche di \u003Ca href=\"https://www.snowflake.com/it/fundamentals/mlops/\"\u003EMLOps\u003C/a\u003E come pipeline di deployment, monitoraggio e controllo delle versioni dei modelli.\u003C/p\u003E\n\u003Cp\u003EServe quindi più di una strategia generale per l’AI. I modelli predittivi, i classificatori di documenti, le app RAG e i sistemi agentici vengono sviluppati in modi diversi, presentano modalità di errore differenti e impongono requisiti specifici a dati, infrastruttura e supervisione. Le decisioni su cosa sviluppare, scalare e sottoporre a governance devono tenere conto di queste differenze senza frammentare i controlli aziendali applicati ai sistemi.\u003C/p\u003E\n\u003Cp\u003EQuesta guida costituisce un riferimento per tali attività. Descrive le principali discipline dell’AI, le modalità di addestramento e valutazione dei sistemi, gli ambiti in cui le imprese li implementano con efficacia e gli aspetti di governance, sicurezza e gestione dei dati che determinano la solidità di un deployment nel tempo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_1818028417":{"id":"title-v2-79093a4a5b","additionalClasses":"anchor title anchor-title--what-is-artificial-intelligence","type":"heading2","lines":["Che cos’è l’intelligenza artificiale?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-2caa1b62c6","text":"\u003Cp\u003EL’intelligenza artificiale non è una singola disciplina, ma un ampio campo costituito da numerose sottodiscipline interconnesse. Ad esempio, il \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E (ML) è un sottoinsieme dell’AI: comprende sistemi che apprendono modelli statistici dai dati anziché seguire regole esplicite. Il deep learning è un sottoinsieme del ML: utilizza \u003Ca href=\"https://www.snowflake.com/it/fundamentals/neural-network/\"\u003Ereti neurali\u003C/a\u003E a più livelli che apprendono rappresentazioni di dati complessi e ad alta dimensionalità. L’AI generativa è un prodotto del deep learning: i foundation model generano nuovi output anziché classificare input esistenti.\u003C/p\u003E\n\u003Cp\u003EOgni livello eredita i requisiti di qualità dei dati, infrastruttura e valutazione del livello superiore. Un’implementazione di AI generativa è anche un’implementazione di ML, con tutto ciò che ne consegue.\u003C/p\u003E\n\u003Cp\u003EIl settore distingue inoltre tra AI ristretta e intelligenza artificiale generale (AGI). L’AI ristretta, categoria che comprende ogni sistema AI attualmente in produzione, è ottimizzata per un’attività o un dominio specifico. Le decisioni aziendali sull’AI riguardano sempre l’AI ristretta e i framework di governance, le pratiche di valutazione e i modelli di rischio applicabili. L’AGI, un sistema capace di ragionare e apprendere in qualsiasi dominio a un livello pari o superiore a quello umano, rimane un obiettivo di ricerca.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_item_1152526200":{"id":"quote-item-d6fe76eeca","alignment":"left","layout":"simple","quoteSourceName":"Baris Gultekin","quoteSourceTitle":"VP of Product, AI Snowflake","showQuoteIcon":true,"quote":"L'AI sta permettendo alle organizzazioni di ricavare insight da dati non strutturati, cosa che prima era semplicemente impossibile.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_v2_copy_1903657955":{"id":"title-v2-6e8e21061c","additionalClasses":"anchor-title anchor-title--how-does-artificial-intelligence-work","type":"heading2","lines":["Come funziona l’intelligenza artificiale?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy":{"id":"text-c6577f20c1","text":"\u003Cp\u003ELa maggior parte dei sistemi AI segue lo stesso ciclo di base: i dati vengono acquisiti, un modello apprende da essi e viene quindi utilizzato per generare previsioni o output su nuovi dati. Le tre fasi, addestramento, inferenza e fine‑tuning, comportano requisiti distinti di \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-infrastructure/\"\u003Einfrastruttura\u003C/a\u003E e \u003Ca href=\"https://www.snowflake.com/it/data-governance/\"\u003Egovernance\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003EL’\u003Cb\u003Eaddestramento\u003C/b\u003E è il processo che espone un modello a grandi quantità di dati etichettati o non etichettati affinché impari a riconoscere schemi ricorrenti. Nei sistemi di deep learning, questo processo utilizza in genere architetture transformer con meccanismi di attenzione per apprendere quali parti dell’input sono più rilevanti per un determinato output. L’addestramento dei modelli foundation, i grandi modelli di base su cui si fonda la maggior parte delle app di AI generativa, richiede capacità di calcolo su una scala storicamente accessibile solo alle grandi organizzazioni di ricerca e ai cloud provider.\u003C/p\u003E\n\u003Cp\u003EL’\u003Cb\u003Einferenza\u003C/b\u003E è la fase in cui viene utilizzato un modello addestrato. Un utente invia una query, il modello genera una risposta, che viene restituita quasi in tempo reale. La latenza, il costo e l’affidabilità dell’inferenza sono aspetti operativi che assumono rilievo su larga scala, in particolare per le applicazioni destinate alla clientela.\u003C/p\u003E\n\u003Cp\u003EIl \u003Cb\u003Efine‑tuning\u003C/b\u003E adatta un modello preaddestrato a un dominio o a un’attività specifici mediante un set di dati più piccolo e mirato. È un approccio comune per le organizzazioni che vogliono utilizzare le capacità di un foundation model senza addestrarlo da zero, ma introduce il rischio di deriva del modello se, nel tempo, i dati di fine‑tuning divergono dagli input di produzione.\u003C/p\u003E\n\u003Cp\u003EIl vero collo di bottiglia nell’implementazione dell’AI è raramente il modello stesso. In un sondaggio di \u003Ca href=\"https://www.technologyreview.com/2025/10/23/1125651/redefining-data-engineering-in-the-age-of-ai/\" target=\"_blank\"\u003EMIT Technology Review Insights\u003C/a\u003E condotto su 400 dirigenti senior responsabili di dati e tecnologia, il 45% ha indicato la complessità dell’integrazione e il 40% la governance dei dati come le principali sfide delle iniziative di AI. La qualità dei dati, la governance e l’infrastruttura di calcolo incidono sui risultati con maggiore regolarità rispetto alla scelta del modello.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_2059046899":{"id":"text-cfa0151d50","additionalClasses":"callout callout--general","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EIl 45% \u003C/b\u003Edei dirigenti indica la complessità dell’integrazione come una delle principali sfide delle iniziative di AI.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIl 40% \u003C/b\u003Econsidera la governance dei dati un ostacolo primario al successo dell’AI.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIl 37%\u003C/b\u003E del tempo dedicato al data engineering è ora impiegato in progetti di AI, rispetto al 19% del 2023.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text"},"quote_item":{"id":"quote-item-cd36d5fcbb","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"Misurare il valore generato dall’AI è fondamentale per giustificare l’investimento, dare priorità alle sue applicazioni e bilanciare i benefici futuri con i rischi potenziali.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_1948751468":{"id":"text-c7eb586366","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003EL’ERRORE COMUNE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EI team che si concentrano sulla selezione o sul fine‑tuning dei modelli prima di predisporre solide basi per i dati, la valutazione e la governance riscontrano spesso problemi di produzione difficili da diagnosticare e risolvere.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_2049864016":{"id":"text-b49b5ae768","text":"\u003Cp\u003E\u003Ci\u003E\u003Cu\u003E\u003C/u\u003EGuarda un video tutorial per iniziare a usare Snowflake Cortex Agents:\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube_1156321798":{"id":"embed-2447c709ae","youtubeVideoId":"SDqyB-yWaAE","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_v2_copy":{"id":"title-v2-ed3a99581c","additionalClasses":"anchor-title anchor-title--types-of-ai","type":"heading2","lines":["Tipi di AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy_831105251":{"id":"text-7e48772720","text":"\u003Cp\u003EI sistemi AI sono comunemente classificati in quattro categorie in base alle loro capacità di memoria e ragionamento:\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EReattiva → Memoria limitata → Teoria della mente → Autoconsapevole\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EIn pratica, tutti i sistemi attualmente in produzione rientrano nei primi due tipi.\u003C/p\u003E\n\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ETipo\u003C/th\u003E\n\u003Cth\u003EDescrizione\u003C/th\u003E\n\u003Cth\u003EEsempio\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EReattiva\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003ERisponde agli input senza conservare memoria delle interazioni precedenti\u003C/td\u003E\n\u003Ctd\u003ESegnalazioni di anomalie in tempo reale, filtri antispam\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMemoria limitata\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EUtilizza i dati storici per orientare le decisioni attuali\u003C/td\u003E\n\u003Ctd\u003ESistemi di raccomandazione, modelli di rilevamento delle frodi\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ETeoria della mente\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EComprenderebbe le intenzioni e lo stato emotivo delle persone\u003C/td\u003E\n\u003Ctd\u003ENon esiste ancora al di fuori della ricerca\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAutoconsapevole\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EAvrebbe una coscienza soggettiva e consapevolezza di sé\u003C/td\u003E\n\u003Ctd\u003ETeorica\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003EL’implicazione pratica è questa: l’AI odierna opera in contesti circoscritti. Un modello offre buone prestazioni in un’attività perché è stato addestrato per quell’attività e valutato su dati simili a quelli che incontra in produzione. I sistemi che sembrano generalizzare sono in genere modelli a memoria limitata di dimensioni molto grandi oppure sistemi specializzati con un’ampia copertura dei dati di addestramento, non forme di intelligenza generale.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_565269666":{"id":"title-v2-ed68085feb","additionalClasses":"anchor-title anchor-title--core-ai-disciplines","type":"heading2","lines":["Discipline fondamentali dell’AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1898884637":{"id":"text-7b1b5da5e7","text":"\u003Cp\u003ELe discipline seguenti rappresentano i principali rami dell’AI applicata. Ciascuna presenta metodi, benchmark e modalità di errore specifici. Per i team che sviluppano o valutano sistemi di AI, comprendere su quale disciplina si basa una determinata applicazione è il punto di partenza per formulare le domande corrette sui requisiti dei dati, sui criteri di valutazione e sui rischi.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EMachine learning \u003C/b\u003Eaddestra modelli statistici su dati storici per classificare gli input, prevedere i risultati o individuare pattern. Le principali decisioni di progettazione, tra cui feature engineering, tuning degli iperparametri e metodi ensemble, richiedono di bilanciare complessità del modello, interpretabilità e generalizzazione. La maggior parte dell’analisi predittiva aziendale si basa sul ML.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EDeep learning\u003C/b\u003E utilizza reti neurali con molti livelli per apprendere rappresentazioni di dati complessi: immagini, audio, testo e segnali di serie temporali. Diverse tecniche fondamentali, tra cui la discesa del gradiente e la retropropagazione, consentono a queste reti di apprendere automaticamente le feature senza richiedere un’attività esplicita di feature engineering. Il deep learning è alla base della maggior parte delle funzionalità attualmente descritte come AI nelle app consumer. \u003Ca href=\"https://www.snowflake.com/it/fundamentals/deep-learning/\"\u003EScopri di più sul deep learning >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EL’elaborazione del linguaggio naturale\u003C/b\u003E (NLP) consente ai sistemi di interpretare, classificare e generare il linguaggio umano. La NLP moderna si basa in larga misura su modelli linguistici fondati sui transformer ed è alla base di app che spaziano dalla classificazione dei documenti alle interfacce conversazionali e alla generazione di codice. \u003Ca href=\"https://www.snowflake.com/it/fundamentals/natural-language-processing/\"\u003EScopri di più sull’elaborazione del linguaggio naturale >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ELa computer vision\u003C/b\u003E applica il deep learning ai dati visivi, come immagini e video, per task che includono il rilevamento degli oggetti, la classificazione delle immagini, l’identificazione dei difetti e il riconoscimento facciale. È ampiamente utilizzata nel settore manifatturiero, nella logistica, nel settore sanitario e nella sicurezza. \u003Ca href=\"https://www.snowflake.com/it/fundamentals/computer-vision/\"\u003EScopri di più sulla computer vision >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EL’apprendimento per rinforzo\u003C/b\u003E addestra agenti a eseguire azioni in un ambiente per massimizzare un segnale di ricompensa. È alla base di sistemi di gioco come AlphaGo e assume un’importanza crescente per la robotica, i sistemi autonomi e, in forma modificata, l’addestramento dei modelli linguistici mediante apprendimento per rinforzo da feedback umano (RLHF). \u003Ca href=\"https://www.snowflake.com/en/fundamentals/reinforcement-learning/\"\u003EScopri di più sull’apprendimento per rinforzo >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EL’AI generativa\u003C/b\u003E utilizza modelli foundation di grandi dimensioni, spesso definiti \u003Ca href=\"https://www.snowflake.com/it/fundamentals/large-language-model/\"\u003Emodelli linguistici di grandi dimensioni\u003C/a\u003E (LLM) quando applicati al testo, per produrre nuovi contenuti: testo, codice, immagini, audio e dati sintetici. Le funzionalità principali includono lo zero-shot learning, ossia l’esecuzione di task senza esempi di addestramento specifici, e il chain-of-thought prompting, che guida i modelli nel ragionamento graduale su un problema. In molte organizzazioni, la velocità di adozione ha superato lo sviluppo delle pratiche di valutazione, creando un rischio di governance che emerge nell’ambiente di produzione prima che nella pianificazione. \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EScopri di più sull’AI generativa >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EL’AI multimodale \u003C/b\u003Ecombina input provenienti da diverse modalità, come testo, immagini, audio e video, in uno spazio di embedding condiviso, consentendo a un unico modello di ragionare su diversi tipi di dati. Rappresenta l’attuale frontiera dello sviluppo dei modelli foundation e assume un’importanza crescente per le app che devono elaborare insieme documenti, diagrammi e contenuti multimediali non strutturati.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_2089711109":{"id":"title-v2-4c168bd1b0","additionalClasses":"anchor-title anchor-title--ai-agents-and-agentic-systems","type":"heading2","lines":["Agenti AI e sistemi agentici"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1819706580":{"id":"text-fc9bb0a7f7","text":"\u003Cp\u003EUn agente AI dispone di capacità molto più avanzate rispetto a un semplice chatbot. Un agente può pianificare più passaggi, utilizzare strumenti esterni, recuperare il contesto da fonti esterne ed eseguire azioni. Ad esempio, un chatbot per l’assistenza clienti può rispondere alle domande, mentre un agente può ricevere una richiesta, recuperare la cronologia dell’account pertinente, consultare una knowledge base, formulare una soluzione e aprire un ticket di assistenza, operando perlopiù in autonomia.\u003C/p\u003E\n\u003Cp\u003EQuesta capacità rende inoltre i sistemi agentici la categoria a più alto rischio nello stack di \u003Ca href=\"https://www.snowflake.com/en/fundamentals/enterprise-ai/\"\u003Eenterprise AI\u003C/a\u003E. Gli errori si accumulano nei diversi passaggi, quindi la supervisione umana deve essere pianificata prima del deployment.\u003C/p\u003E\n\u003Cp\u003EInoltre, la qualità degli agenti AI dipende dalle informazioni a cui possono accedere: \u003Ca href=\"https://www.snowflake.com/it/blog/easy-button-context-rich-ai-agents/\"\u003Equalità del recupero, gestione del contesto e affidabilità degli strumenti\u003C/a\u003E sono importanti quanto il modello stesso. Un agente che opera su dati obsoleti, incompleti o con un ambito definito in modo inadeguato produce rapidamente errori plausibili e formulati con sicurezza.\u003C/p\u003E\n\u003Cp\u003EL’Agentic AI è attualmente l’ambito dell’enterprise AI che evolve più rapidamente e i framework di governance non ne seguono ancora il ritmo. I team che sviluppano sistemi agentici devono definire l’ambito, i percorsi di escalation e le modalità di errore prima del deployment, non dopo. \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/agents/\"\u003EScopri di più sugli agenti AI \u003E\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_1848786618":{"id":"title-v2-799c169ba6","additionalClasses":"anchor-title anchor-title--enterprise-ai-use-cases","type":"heading2","lines":["Casi d’uso di AI in ambito aziendale"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_992156203":{"id":"text-a1946c4b09","text":"\u003Cp\u003EL’adozione dell’enterprise AI tende a concentrarsi negli ambiti in cui workflow, input di dati e criteri di valutazione sono ben compresi. Gli esempi seguenti riguardano aree in cui l’AI è già applicata in produzione, anziché esperimenti ipotetici o in fase iniziale.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EIl supporto decisionale \u003C/b\u003Eintegra il giudizio di analisti e operatori con raccomandazioni generate dai modelli, come scoring del rischio, valutazione del merito creditizio, supporto alle decisioni cliniche e ottimizzazione della supply chain.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E \u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cb\u003EL’automazione\u003C/b\u003E g\u003C/b\u003Eestisce attività ad alto volume strettamente legate a regole: elaborazione dei documenti, classificazione delle email, instradamento dei ticket di assistenza, inserimento dei dati e revisione del codice. Il valore risiede nella capacità operativa e nella coerenza, non nella novità.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E \u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cb\u003ELa generazione di contenu\u003C/b\u003Eti \u003C/b\u003Eutilizza l’AI generativa per produrre su larga scala bozze, riepiloghi, traduzioni, descrizioni dei prodotti e documentazione interna. In questo ambito, il controllo della qualità e l’infrastruttura di valutazione sono più importanti della selezione del modello.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cb\u003E \u003C/b\u003E\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cb\u003E\u003Cb\u003ELa personalizzazione\u003C/b\u003E\u003C/b\u003E\u003C/b\u003E è alla base dei motori di raccomandazione, del pricing dinamico, del marketing individualizzato e delle esperienze di prodotto adattive. Questi sistemi sono spesso le implementazioni ML operative da più tempo in un’organizzazione e presentano il maggiore debito tecnico accumulato\u003Cb\u003E.\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cb\u003E \u003C/b\u003E\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cb\u003E\u003Cb\u003EIl rilevamento di frodi e anomalie\u003C/b\u003E\u003C/b\u003E\u003C/b\u003E utilizza il deep learning per identificare pattern che si discostano dai valori di riferimento: frodi finanziarie, intrusioni di rete, difetti del settore manifatturiero e problemi di qualità dei dati\u003Cb\u003E.\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Cb\u003E \u003C/b\u003E\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ELa previsione\u003C/b\u003E applica modelli di serie temporali e ML alla pianificazione della domanda, alla gestione della capacità, alle proiezioni finanziarie e alla modellazione del rischio.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cp\u003E\u003Ci\u003EGuarda questa presentazione per capire come l’AI generativa e il calcolo accelerato stanno trasformando ogni settore:\u003C/i\u003E\u003C/p\u003E\r\n\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube_1030707130":{"id":"embed-b0d5f8c435","youtubeVideoId":"O8sQxPgw8Ws","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"card_v2":{"id":"card-v2-e048d829dc","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer story: TS Imagine"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/ts-imagine/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi l’esperienza del cliente"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3b812ed0-848d-4714-96e8-ce82ea5f8ebb/ts-imagine%25403x.png?quality=85&preferwebp=true","alt":"TSImagine Logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","layoutStyle":"horizontal","text":{"id":"text","text":"\u003Cp\u003ETS Imagine, azienda che sviluppa software per il trading finanziario e la gestione dei rischi, ha automatizzato l’acquisizione delle email e lo smistamento dei ticket di assistenza utilizzando Snowflake Cortex AI RAG, recuperando 4000 ore di lavoro manuale all’anno e riducendo i costi del 30%.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"}},"title_v2_copy_copy":{"id":"title-v2-22a9505a4e","additionalClasses":"anchor-title anchor-title--ai-governance-risk-and-security","type":"heading2","lines":["Governance, rischi e sicurezza dell’AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy_83110":{"id":"text-c08d1a0da3","text":"\u003Cp\u003ELa governance dell’AI non è più un’attività di pianificazione per un’implementazione futura. Molte organizzazioni governano ora sistemi già in produzione, compresi modelli e app sviluppati prima che fossero pienamente definiti i controlli aziendali, le strutture di responsabilità e le pratiche di monitoraggio.\u003C/p\u003E\n\u003Cp\u003EIl quadro normativo ha reso la questione più urgente. L’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EAI Act dell’Unione europea\u003C/a\u003E (regolamento UE 2024/1689) stabilisce requisiti differenziati in base al livello di rischio: i sistemi utilizzati nella selezione del personale, nel credito, nelle attività di contrasto e nelle infrastrutture critiche sono soggetti agli obblighi più rigorosi. L’\u003Ca href=\"https://csrc.nist.gov/projects/risk-management/about-rmf\" target=\"_blank\"\u003EAI Risk Management Framework del NIST\u003C/a\u003E fornisce una struttura volontaria, ma ampiamente adottata, per identificare, valutare e gestire i rischi dell’AI durante l’intero ciclo di vita del modello. Entrambi presuppongono che la governance inizi in fase di progettazione, non di implementazione. Di conseguenza, le organizzazioni che hanno saltato questo passaggio stanno ora costruendo retroattivamente le strutture che avrebbero dovuto predisporre fin dall’inizio.\u003C/p\u003E\n\u003Cp\u003EI rischi dell’enterprise AI rientrano in tre categorie:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EIl \u003Cb\u003Erischio dei dati\u003C/b\u003E riguarda qualità, bias, provenienza e privacy dei dati di addestramento.\u003C/li\u003E\n\u003Cli\u003EIl \u003Cb\u003Erischio del modello\u003C/b\u003E riguarda accuratezza, calibrazione, drift e interpretabilità.\u003C/li\u003E\n\u003Cli\u003EIl \u003Cb\u003Erischio operativo\u003C/b\u003E riguarda gli effetti a valle degli output dei modelli su decisioni, persone e sistemi.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ELa gestione di questi rischi richiede valutazioni sistematiche, ossia processi definiti per testare il comportamento dei modelli prima dell’implementazione, monitorarlo in produzione e rilevare il drift prima che diventi un problema aziendale.\u003C/p\u003E\n\u003Cp\u003ELo stesso schema si osserva nelle implementazioni aziendali: i team che investono tempestivamente nelle valutazioni rilevano le modalità di errore prima che raggiungano la produzione, mentre gli altri finiscono per ricostruire i problemi a partire dalle segnalazioni degli utenti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_item_1218174521":{"id":"quote-item-47ca5d5888","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"Il modo per espandere un utilizzo efficace dell’AI, e aumentare il valore che ne deriva, è implementare e industrializzare. Questo richiede l’allineamento tra la tecnologia e il business, il coordinamento delle risorse e la standardizzazione delle policy e dei processi.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_1896381897":{"id":"text-f6a980f768","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/\"\u003EScopri di più sulla governance dell’AI >\u003C/a\u003E | \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/\"\u003EScopri di più sulla sicurezza dell’AI >\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"title_v2_copy_copy_1670228814":{"id":"title-v2-429ddc9f6b","additionalClasses":"anchor-title anchor-title--the-data-foundation-behind-ai","type":"heading2","lines":["L’infrastruttura dati alla base dell’AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy_83110_19424502":{"id":"text-9d353417ea","text":"\u003Cp\u003ELa qualità dell’AI dipende dalla qualità dei dati. Questa relazione vale per ogni disciplina trattata nella guida: un modello di deep learning addestrato con dati non rappresentativi fallirà negli stessi modi di una regressione logistica addestrata con dati scadenti, ma in modo meno evidente.\u003C/p\u003E\n\u003Cp\u003EI requisiti dei dati per l’AI moderna vanno oltre set di dati puliti ed etichettati. Includono controlli per l’accesso governato, affinché i modelli siano addestrati con dati che i team sono autorizzati a utilizzare; definizioni aziendali chiare, affinché le feature abbiano lo stesso significato negli ambienti di addestramento e produzione; lineage e provenienza, affinché i team possano ricondurre il comportamento di un modello ai relativi input; e un’infrastruttura ad alte prestazioni, capace di fornire dati ai modelli in fase di inferenza senza introdurre latenza o colli di bottiglia.\u003C/p\u003E\n\u003Cp\u003ESul piano operativo, il lavoro di data engineering si è spostato in misura significativa verso l’AI. Secondo il sondaggio di MIT Technology Review Insights, i data engineer dedicano ora il 37% del proprio tempo a progetti di AI, rispetto al 19% del 2023, una quota quasi raddoppiata in due anni. Questo cambiamento spinge i team responsabili delle piattaforme dati a rendere disponibili in modo affidabile dati pronti per l’AI, non solo teoricamente accessibili.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_946526351":{"id":"card-v2-9f5f25a1bf","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer story: State Street"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/state-street/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Vedi i dettagli"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d747d8ae-ec39-45c3-b3f3-c4c3f9a7e443/state-street%25403x.png?quality=85&preferwebp=true","alt":"State Street logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","layoutStyle":"horizontal","text":{"id":"text","text":"\u003Cp\u003EState Street ha incrementato di 25 volte la produttività delle operazioni sui dati implementando su Snowflake il rilevamento delle anomalie basato sul deep learning, identificando il 100% delle eccezioni effettive ed eliminando l’87% dei falsi positivi. I costi del lavoro per l’onboarding dei clienti sono diminuiti del 50%.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"}},"text_copy_copy_83110_69230348":{"id":"text-afeed05edc","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/product/ai/\"\u003EScopri di più su Snowflake per l’AI \u003E\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_1534133931":{"id":"title-v2-fcf33eaca2","additionalClasses":"anchor-title anchor-title--history-of-ai","type":"heading2","lines":["Storia dell’intelligenza artificiale"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy_83110_670475437":{"id":"text-6fb9d67ec8","additionalClasses":"callout callout--general","text":"\u003Cdiv\u003E \u003C/div\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E1950\u003C/b\u003E: il pioniere dell’informatica Alan Turing propone il test di Turing come misura dell’intelligenza delle macchine\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E1956\u003C/b\u003E: la Dartmouth Conference conia il termine “intelligenza artificiale”\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E1997\u003C/b\u003E: Deep Blue di IBM sconfigge il campione del mondo di scacchi Garry Kasparov\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E2012\u003C/b\u003E: AlexNet vince la competizione ImageNet e il deep learning diventa il paradigma dominante nella computer vision\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E2017\u003C/b\u003E: i ricercatori Google introducono l’architettura transformer in “Attention Is All You Need”\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E2022\u003C/b\u003E: debutta ChatGPT e l’AI generativa entra nel dibattito aziendale mainstream\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E2024+\u003C/b\u003E: i sistemi di Agentic AI emergono come nuova frontiera delle implementazioni aziendali\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_685568339":{"id":"title-v2-04e9510ba8","additionalClasses":"anchor-title anchor-title--next-steps","type":"heading2","lines":["Prossimi passi"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1142311968":{"id":"text-311c71c747","text":"\u003Cp\u003EL’affidabilità dell’enterprise AI dipende dal sistema operativo che circonda il modello: metodi di valutazione, accesso governato ai dati, pratiche di monitoraggio, strutture di responsabilità e percorsi di escalation per output errati, drift, cali delle prestazioni e comportamenti imprevisti. Le guide seguenti illustrano le discipline che consentono ai sistemi di AI di passare da implementazioni isolate a servizi di produzione affidabili.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_617860334":{"id":"text-3fad1af372","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EIN SINTESI\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EL’enterprise AI ha maggiori probabilità di successo quando le organizzazioni allineano architettura, dati, valutazione e governance lungo l’intero ciclo di vita, riconoscendo che sistemi di AI diversi impongono requisiti differenti, ma devono comunque operare secondo controlli coerenti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["text_1521899678","text","title_v2_1818028417","text_copy","quote_item_1152526200","title_v2_copy_1903657955","text_copy_copy","text_2059046899","quote_item","text_1948751468","text_2049864016","youtube_1156321798","title_v2_copy","text_copy_copy_831105251","title_v2_565269666","text_1898884637","title_v2_2089711109","text_1819706580","title_v2_1848786618","text_992156203","youtube_1030707130","card_v2","title_v2_copy_copy","text_copy_copy_83110","quote_item_1218174521","text_1896381897","title_v2_copy_copy_1670228814","text_copy_copy_83110_19424502","card_v2_946526351","text_copy_copy_83110_69230348","title_v2_copy_copy_1534133931","text_copy_copy_83110_670475437","title_v2_685568339","text_1142311968","text_617860334"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"container-de9860bfc4",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-ed64724868",":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-885e8b4278","additionalClasses":"eyebrow-text","text":"\u003Cp\u003E\u003Cu\u003E\u003C/u\u003ECosa troverai in questa guida\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"text_copy":{"id":"text-599dd30125","additionalClasses":"page-toc","text":"\u003Cul\u003E\n\u003Cli data-anchor=\"what-is-artificial-intelligence\"\u003EChe cos’è l’intelligenza artificiale?\u003C/li\u003E\n\u003Cli data-anchor=\"how-does-artificial-intelligence-work\"\u003ECome funziona l’intelligenza artificiale?\u003C/li\u003E\n\u003Cli data-anchor=\"types-of-ai\"\u003ETipi di AI\u003C/li\u003E\n\u003Cli data-anchor=\"core-ai-disciplines\"\u003EDiscipline fondamentali dell’AI\u003C/li\u003E\n\u003Cli data-anchor=\"ai-agents-and-agentic-systems\"\u003EAgenti AI e sistemi agentici\u003C/li\u003E\n\u003Cli data-anchor=\"enterprise-ai-use-cases\"\u003ECasi d’uso di AI in ambito aziendale\u003C/li\u003E\n\u003Cli data-anchor=\"ai-governance-risk-and-security\"\u003EGovernance, rischi e sicurezza dell’AI\u003C/li\u003E\n\u003Cli data-anchor=\"the-data-foundation-behind-ai\"\u003EL’infrastruttura dati alla base dell’AI\u003C/li\u003E\n\u003Cli data-anchor=\"history-of-ai\"\u003EStoria dell’intelligenza artificiale\u003C/li\u003E\n\u003Cli data-anchor=\"next-steps\"\u003EProssimi passi\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small"},"container_1561483981":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"topics",":type":"snowflake-site/components/container",":items":{"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_cont_1969625716":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_470142727":"aem-GridColumn 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aem-Grid--default--12","id":"container-cca86a4359",":type":"snowflake-site/components/container",":items":{"flexible_column_cont_470142727":{"id":"flexible-column-container-47f59ed596","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-e40e125307",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy":{"id":"title-v2-d33216c0b5","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Domande frequenti"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-f73c7cba71","text":"\u003Cp\u003ELe risposte degli esperti Snowflake alle domande più comuni sull’intelligenza artificiale.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-f4c25597ae",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-7557b34826","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Che cos’è l’AI in termini semplici?","richText":"\u003Cp\u003EL’AI indica sistemi software che svolgono task associati al ragionamento umano: riconoscere schemi, formulare previsioni, comprendere il linguaggio e generare contenuti. In pratica, la maggior parte dei sistemi di AI è costituita da strumenti specializzati, ottimizzati per task specifici, anziché da motori di ragionamento generico.\u003C/p\u003E\n"},{"title":"Qual è la differenza tra AI e machine learning?","richText":"\u003Cp\u003EIl machine learning è un sottoinsieme dell’AI. L’AI è il campo più ampio che comprende i sistemi in grado di svolgere task di ragionamento simili a quelli umani. Il ML è un approccio specifico alla creazione di sistemi di AI che apprendono schemi statistici dai dati anziché seguire regole programmate esplicitamente.\u003C/p\u003E\n"},{"title":"ChatGPT è un’intelligenza artificiale?","richText":"\u003Cp\u003ESì. ChatGPT è un’applicazione di AI generativa basata su modelli linguistici di grandi dimensioni (LLM), che derivano dal deep learning, un sottoinsieme del machine learning, a sua volta sottoinsieme dell’AI. Pur avendo capacità avanzate nel proprio ambito, non è un sistema di ragionamento generico.\u003C/p\u003E\n"},{"title":"Quali sono gli obiettivi principali dell’AI?","richText":"\u003Cp\u003EA livello aziendale, gli obiettivi dell’AI includono l’aumento della velocità, dell’accuratezza e della scalabilità di decisioni e operazioni, la riduzione del lavoro manuale e l’individuazione di schemi nei dati che le persone non riuscirebbero a identificare senza supporto.\u003C/p\u003E\n"},{"title":"Come viene utilizzata l’AI nelle aziende? ","richText":"\u003Cp\u003EI casi d’uso aziendali con i volumi più elevati sono il supporto decisionale, l’automazione dei processi, la generazione di contenuti, la personalizzazione, il rilevamento di frodi e anomalie e le previsioni. L’elemento comune è l’applicazione dell’apprendimento statistico a task che in precedenza richiedevano molto tempo umano per ogni unità di output.\u003C/p\u003E\n"},{"title":"Qual è la differenza tra addestramento e inferenza di un modello di AI?","richText":"\u003Cp\u003EL’addestramento è il processo mediante il quale un \u003Ca href=\"https://www.snowflake.com/it/fundamentals/ai-models/\"\u003Emodello di AI\u003C/a\u003E apprende schemi da grandi set di dati regolando i propri parametri interni. L’inferenza è l’utilizzo del modello addestrato per formulare previsioni o generare output su nuovi dati. In sintesi, l’addestramento crea il modello e l’inferenza lo applica.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"title_v2":{"id":"title-v2-9e79e1856e","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Esplora le risorse sull’AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"flexible_column_cont_1605976184":{"id":"flexible-column-container-c899fc3f88","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-c184ceeeaa",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"flexible_column_cont":{"id":"flexible-column-container-ad490dc392","propertiesId":"seo-hub-resources","type":"2-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-9a06ef5713",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"content_chip":{"id":"content-chip-bfafe58417","tagText":"EBOOK","tagColor":"#71D3DC","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/it/lp/radical-roi-generative-ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Scopri di più"},"headline":{"id":"title","type":"heading5","lines":["Il ROI dell’AI generativa e degli agenti AI nel 2026"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip","appliedCssClassNames":"snowflake-content-chip-white-bg"},"content_chip_1350384":{"id":"content-chip-fa23b34bf8","tagText":"WHITE PAPER","tagColor":"#FFD9AF","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/white-paper/from-prompt-to-purpose-unlocking-business-value-with-agentic-ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Scopri di più"},"headline":{"id":"title","type":"heading5","lines":["Dal prompt allo scopo: generare valore aziendale con l’Agentic 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