{"id":1278,"date":"2023-06-05T17:43:13","date_gmt":"2023-06-05T17:43:13","guid":{"rendered":"https:\/\/dailyai.com\/?p=1278"},"modified":"2023-06-05T17:43:13","modified_gmt":"2023-06-05T17:43:13","slug":"new-mit-self-learning-language-models-can-beat-other-llms","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2023\/06\/new-mit-self-learning-language-models-can-beat-other-llms\/","title":{"rendered":"I nuovi modelli linguistici ad autoapprendimento del MIT possono battere gli altri LLM"},"content":{"rendered":"<p><span style=\"font-weight: 400\">Negli ultimi sei mesi, abbiamo assistito a un boom rivoluzionario di <\/span><a href=\"https:\/\/dailyai.com\/it\/2023\/05\/ai-powered-technology-slows-the-pacific-salmon-invasion-of-europe\/\"><span style=\"font-weight: 400\">Alimentazione con intelligenza artificiale<\/span><\/a><span style=\"font-weight: 400\"> Gli LLM (Large Language Models) sono al centro della scena. Ma \u00e8 sempre necessario che un prodotto o un servizio di IA si basi su LLM? Secondo un documento, i nuovi modelli linguistici ad autoapprendimento del MIT non si basano su LLM e possono superare alcuni dei grandi sistemi di IA che attualmente guidano il settore.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Un gruppo di ricercatori del MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) ha sviluppato un nuovo modo di approcciare i modelli linguistici dell'intelligenza artificiale.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Si tratta di un risultato innovativo che enfatizza i modelli linguistici pi\u00f9 piccoli e la loro capacit\u00e0 di risolvere i problemi di inefficienza e di privacy legati allo sviluppo di grandi modelli di intelligenza artificiale basati su dati testuali.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Con l'emergere di <\/span><a href=\"https:\/\/dailyai.com\/it\/2023\/06\/openai-unveils-1m-cybersecurity-grant-program\/\"><span style=\"font-weight: 400\">OpenAI<\/span><\/a><span style=\"font-weight: 400\"> ChatGPT basato sui modelli linguistici GPT-3 e GPT-4, molte aziende si sono unite alla corsa all'intelligenza artificiale, tra cui Google Bard e altre aziende. <\/span><a href=\"https:\/\/dailyai.com\/it\/2023\/06\/snapchat-generative-ai-my-ai-is-it-worth-the-hype\/\"><span style=\"font-weight: 400\">IA generativa<\/span><\/a><span style=\"font-weight: 400\"> sistemi che consentono di generare testo, immagini e persino video.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Tuttavia, per generare risultati di qualit\u00e0 impeccabile, questi sistemi si basano su una grande quantit\u00e0 di dati che sono costosi da elaborare computazionalmente. Molti di questi sistemi importano dati per l'addestramento tramite API, il che comporta rischi quali la fuga di dati e altri problemi di privacy.<\/span><\/p>\n<h2><b>Integrazione testuale<\/b><\/h2>\n<p><span style=\"font-weight: 400\">Secondo un nuovo documento intitolato<\/span><a href=\"https:\/\/arxiv.org\/pdf\/2305.17197.pdf\"> <span style=\"font-weight: 400\">Entailment come autoapprendimento robusto<\/span><\/a><span style=\"font-weight: 400\"> pubblicato in questi giorni sul repository online di preprinting arXiv, i ricercatori notano che i nuovi modelli linguistici ad autoapprendimento del MIT possono risolvere il problema della comprensione di alcuni compiti linguistici che i modelli linguistici di grandi dimensioni hanno. Questo risultato innovativo viene definito \"textual entailment\".<\/span><\/p>\n<p><span style=\"font-weight: 400\">I modelli si basano sul concetto che se ci sono due frasi - una premessa e un'ipotesi - nel caso in cui una premessa della prima frase sia vera, \u00e8 probabile che sia vera anche l'ipotesi.<\/span><\/p>\n<p><span style=\"font-weight: 400\">In un<\/span><a href=\"https:\/\/www.csail.mit.edu\/news\/mit-researchers-make-language-models-scalable-self-learners\"> <span style=\"font-weight: 400\">dichiarazione<\/span><\/a><span style=\"font-weight: 400\"> pubblicato sul blog del MIT CSAIL, un esempio di questa struttura sarebbe che se \"tutti i gatti hanno la coda\" l'ipotesi \"un gatto soriano ha la coda\" \u00e8 probabilmente vera. Questo approccio porta a una minore distorsione nella <\/span><a href=\"https:\/\/dailyai.com\/it\/2023\/05\/researchers-create-custom-tooth-crowns-using-3d-ai-model\/\"><span style=\"font-weight: 400\">Modelli di intelligenza artificiale<\/span><\/a><span style=\"font-weight: 400\">che fa s\u00ec che i nuovi modelli linguistici ad autoapprendimento del MIT superino i modelli linguistici pi\u00f9 grandi, secondo la dichiarazione.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\"I nostri modelli di entailment autoaddestrati, con 350 milioni di parametri, senza etichette generate dall'uomo, superano i modelli linguistici supervisionati con 137-175 miliardi di parametri\", ha dichiarato in un comunicato Hongyin Luo, associato al CSAIL del MIT e autore principale. \"<\/span><\/p>\n<p><span style=\"font-weight: 400\">Ha inoltre aggiunto che questo approccio potrebbe essere molto vantaggioso per gli attuali sistemi di IA e rimodellare i sistemi di apprendimento automatico in modo da renderli pi\u00f9 scalabili, affidabili ed economici quando si lavora con modelli linguistici.<\/span><\/p>\n<h2>I nuovi modelli linguistici ad autoapprendimento del MIT sono ancora limitati<\/h2>\n<p><span style=\"font-weight: 400\">Anche se i nuovi modelli linguistici di autoapprendimento del MIT promettono molto quando si tratta di risolvere problemi di classificazione binaria, sono ancora limitati a risolvere problemi di classificazione multiclasse. Ci\u00f2 significa che l'entailment testuale non funziona altrettanto bene quando al modello vengono presentate pi\u00f9 scelte.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Secondo James Glass, professore del MIT e ricercatore principale del CSAIL, che \u00e8 anche l'autore dell'articolo, questa ricerca potrebbe far luce su metodi efficienti ed efficaci per addestrare i LLM a comprendere i problemi di implicazione contestuale.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\"Mentre il campo dei LLM sta subendo rapidi e drammatici cambiamenti, questa ricerca dimostra che \u00e8 possibile produrre modelli linguistici relativamente compatti che ottengono risultati molto buoni nei compiti di comprensione dei benchmark rispetto ai loro colleghi di dimensioni pi\u00f9 o meno uguali, o anche a modelli linguistici molto pi\u00f9 grandi\", ha dichiarato.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Questa ricerca \u00e8 solo l'inizio di future tecnologie di intelligenza artificiale che potrebbero imparare da sole ed essere pi\u00f9 efficaci, sostenibili e attente alla privacy dei dati. Il documento sui nuovi modelli linguistici ad autoapprendimento del MIT sar\u00e0 presentato a luglio alla riunione dell'Association for Computational Linguistics di Toronto. Il progetto \u00e8 sostenuto anche dal<\/span><a href=\"https:\/\/www.innohk.gov.hk\/en\/r-d-centres\/air-innohk\/centre-for-artificial-intelligence-and-robotics-hong-kong-institute-of-science-innovation-chinese-academy-of-sciences\/\"> <span style=\"font-weight: 400\">Programma di innovazione AI di Hong Kong<\/span><\/a><span style=\"font-weight: 400\">.<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>Negli ultimi sei mesi abbiamo assistito a un boom rivoluzionario di LLM (Large Language Models) alimentati dall'intelligenza artificiale. Ma \u00e8 sempre necessario che un prodotto o un servizio di IA sia basato su LLM? Secondo un documento, i nuovi modelli linguistici ad autoapprendimento del MIT non sono basati su LLM e possono superare alcuni degli altri grandi sistemi di IA che attualmente guidano il settore. Un gruppo di ricercatori del MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) ha sviluppato un nuovo modo di approcciare i modelli linguistici di IA. Si tratta di un risultato innovativo che pone l'accento su modelli linguistici pi\u00f9 piccoli e sulla loro capacit\u00e0 di affrontare<\/p>","protected":false},"author":4,"featured_media":1280,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118,117],"class_list":["post-1278","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms","tag-mit"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New MIT self-learning language models can beat other LLMs | DailyAI<\/title>\n<meta name=\"description\" content=\"MIT self-learning language models are not based on LLMs and can outperform some of the other large AI systems that currently lead the industry\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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