{"id":11235,"date":"2024-04-03T14:29:19","date_gmt":"2024-04-03T14:29:19","guid":{"rendered":"https:\/\/dailyai.com\/?p=11235"},"modified":"2024-04-03T14:29:19","modified_gmt":"2024-04-03T14:29:19","slug":"berkeley-researchers-build-ai-forecasting-system-that-bests-humans","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/","title":{"rendered":"I ricercatori di Berkeley costruiscono un sistema di previsione AI che supera gli esseri umani"},"content":{"rendered":"

I ricercatori dell'Universit\u00e0 di Berkeley, in California, hanno sviluppato un sistema di previsione AI in grado di prevedere gli eventi futuri con un'accuratezza simile a quella della saggezza della folla umana.\u00a0<\/b><\/p>\n

Poich\u00e9 gli LLM non sono stati creati appositamente per la previsione degli eventi, il team ha costruito un sistema di previsione sulla base del GPT-4, utilizzando un approccio innovativo chiamato retrieval-augmented reasoning.<\/p>\n

Questo processo a pi\u00f9 fasi ha comportato l'addestramento del GPT-4 a cercare informazioni pertinenti, valutarne la rilevanza e integrarle nel suo processo di ragionamento prima di fare una previsione.\u00a0<\/span><\/p>\n

Ecco come funziona:<\/span><\/p>\n

    \n
  1. Recupero<\/b>: Il sistema di intelligenza artificiale utilizza il GPT-4 per generare query di ricerca basate sulla domanda di previsione e sulle sotto-domande, recuperando un'ampia serie di articoli di notizie potenzialmente rilevanti.<\/span><\/li>\n
  2. Valutazione della rilevanza<\/b>: GPT-4 valuta la rilevanza di ogni articolo recuperato, scartando gli articoli a basso punteggio per restringere il bacino di informazioni.<\/span><\/li>\n
  3. Riassunto<\/b>: GPT-4 riduce ogni articolo ai suoi punti chiave, concentrandosi sui dettagli relativi alla domanda di previsione.<\/span><\/li>\n
  4. Ragionamento<\/b>: Utilizzando i \"prompt di scratchpad\", il GPT-4 analizza gli articoli riassunti e produce una previsione dettagliata con una motivazione esplicativa. Questi suggerimenti guidano il processo di pensiero del modello, incoraggiando un approccio sistematico al ragionamento.<\/span><\/li>\n<\/ol>\n

    Il team di Berkeley ha poi fatto un ulteriore passo avanti con la messa a punto auto-supervisionata del sistema.\u00a0<\/span><\/p>\n

    Hanno generato un gran numero di previsioni dell'IA su domande passate con risposte note e hanno selezionato gli esempi in cui l'IA aveva superato la \"saggezza della folla\", definita come le previsioni aggregate dei previsori umani.<\/span><\/p>\n

    Mettendo a punto il GPT-4 su questi esempi, i ricercatori hanno insegnato al modello a emulare i modelli di ragionamento che hanno creato le previsioni migliori.<\/span><\/p>\n

    Risultati<\/h2>\n

    Quando \u00e8 stata testata su domande di previsione da giugno 2023 in poi, l'IA ha ottenuto un punteggio Brier di 0,179, rispetto al punteggio di 0,149 del previsore umano.\u00a0<\/span><\/p>\n

    L'IA si \u00e8 comportata particolarmente bene su domande con un'elevata incertezza umana nelle prime fasi del processo di previsione e quando ha avuto accesso a un numero sufficiente di articoli rilevanti su un determinato argomento.\u00a0<\/span><\/p>\n

    \"Previsioni
    (a) Il sistema ottiene risultati migliori di un gruppo di persone quando ha da 0 a 10 articoli rilevanti.
    (b) Quando le persone non sono sicure delle loro previsioni (livelli di confidenza tra 0,3 e 0,7), il sistema fa meglio, con un punteggio Brier di 0,199 rispetto a quello di 0,246. Tuttavia, quando le persone sono molto sicure (previsioni inferiori a 0,05), fanno meglio del nostro sistema.
    (c) L'accuratezza del sistema \u00e8 maggiore all'inizio della raccolta delle informazioni. Fonte: ArXiv<\/a> (accesso libero).<\/figcaption><\/figure>\n

    Gli autori scrivono nel <\/span>studio<\/span><\/a>Per quanto ne sappiamo, questo \u00e8 il primo sistema automatizzato con una capacit\u00e0 di previsione che si avvicina al livello della folla umana, che in genere \u00e8 pi\u00f9 forte dei singoli previsori umani\".<\/span><\/p>\n

    C'era una piccola stranezza: il sistema sembrava peggiorare con un maggior numero di articoli su cui lavorare e, quindi, con una maggiore certezza delle previsioni. Questo potrebbe essere dovuto al fatto che il modello \"copre\" le sue previsioni.<\/p>\n

    I ricercatori lo descrivono come segue: \"Ipotizziamo che ci\u00f2 derivi dalla tendenza del nostro modello a coprire le previsioni grazie alla sua formazione in materia di sicurezza\".<\/p>\n

    Implicazioni<\/h2>\n

    Secondo i ricercatori, i responsabili politici, le imprese e i funzionari della sanit\u00e0 pubblica potrebbero trarre vantaggio da questa forma di previsione dell'intelligenza artificiale basata sul linguaggio.<\/span><\/p>\n

    \"In futuro, i responsabili delle decisioni politiche potrebbero consultare le IA per sapere quali azioni porterebbero pi\u00f9 facilmente ai risultati desiderati\", afferma Dan Hendrycks del Center for AI Safety in California.<\/p>\n

    Propone che i modelli di previsione possano affrontare i rischi futuri posti dall'intelligenza artificiale. \"I bot di previsione ci aiuterebbero ad anticipare ed evitare questi rischi\", spiega Hendrycks. ha dichiarato al New Scientist<\/a>.<\/p>\n

    Sono stati fatti altri tentativi di prevedere eventi di vita complessi con l'IA, tra cui un modello addestrato da ricercatori danesi per <\/span>prevedere i rischi di morte prematura<\/span><\/a>.\u00a0<\/span><\/p>\n

    Sfruttare l'IA per applicazioni predittive che influiscono sulla vita delle persone pone questioni etiche, come garantire che questi sistemi siano trasparenti, imparziali ed eticamente fondati. <\/span><\/p>\n

    Questo nuovo studio di Berkeley illustra come l'IA sia in grado di fare previsioni efficaci, ma non siamo in grado di valutare con quanta precisione arrivi alle sue decisioni.<\/span><\/p>\n

    L'uso dell'intelligenza artificiale per prevedere i principali eventi sociali e individuali pu\u00f2 sembrare un concetto distopico, ma \u00e8 gi\u00e0 una pratica diffusa in molte parti del mondo.<\/span><\/p>\n

    In diversi paesi democratici, tra cui Stati Uniti, Regno Unito, Brasile, Australia e Paesi Bassi, l'IA viene utilizzata per polizia<\/a>, sorveglianza e processo decisionale in materia di welfare<\/a>.\u00a0<\/span><\/p>\n

    Un'intelligenza artificiale potrebbe prevedere aspetti del vostro futuro in questo momento? \u00c8 certamente possibile.<\/p>","protected":false},"excerpt":{"rendered":"

    I ricercatori dell'Universit\u00e0 di Berkeley, in California, hanno sviluppato un sistema di previsione dell'intelligenza artificiale in grado di prevedere gli eventi futuri con un'accuratezza simile alla saggezza della folla umana. Poich\u00e9 i LLM non sono stati creati appositamente per la previsione degli eventi, il team ha costruito un sistema di previsione sulla base di GPT-4 utilizzando un approccio innovativo chiamato retrieval-augmented reasoning. Questo processo in pi\u00f9 fasi prevedeva l'addestramento di GPT-4 a cercare informazioni pertinenti, valutarne la rilevanza e integrarle nel suo processo di ragionamento prima di fare una previsione. Ecco come funziona: Recupero: Il sistema di intelligenza artificiale utilizza il GPT-4 per generare query di ricerca basate sulla domanda di previsione e sulle sotto-domande, recuperando un'ampia serie di informazioni potenzialmente<\/p>","protected":false},"author":2,"featured_media":11237,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[105],"class_list":["post-11235","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-machine-learning"],"yoast_head":"\nBerkeley researchers build AI forecasting system that bests humans | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/it\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Berkeley researchers build AI forecasting system that bests humans | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at the University of Berkeley, California, developed an AI forecasting system to predict future events with similar accuracy to human crowd wisdom.\u00a0 As LLMs are not purpose-built for event forecasting, the team built a forecasting system on top of GPT-4 using a novel approach called retrieval-augmented reasoning. This multi-step process involved training GPT-4 to search for pertinent information, assess its relevance, and integrate it into its reasoning process before making a prediction.\u00a0 Here\u2019s how it works: Retrieval: The AI system uses GPT-4 to generate search queries based on the forecasting question and sub-questions, retrieving a broad set of potentially\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/it\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-04-03T14:29:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-03-15.16.51-Craft-a-landscape-image-that-focuses-more-on-the-concept-of-graphs-and-forecasting-rather-than-buildings-set-against-a-futuristic-night-sky.-Imagine-.webp\" \/>\n\t<meta property=\"og:image:width\" 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