{"id":6896,"date":"2023-10-29T12:53:37","date_gmt":"2023-10-29T12:53:37","guid":{"rendered":"https:\/\/dailyai.com\/?p=6896"},"modified":"2023-10-29T12:53:37","modified_gmt":"2023-10-29T12:53:37","slug":"mlcommons-and-big-tech-to-develop-ai-safety-benchmarks","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2023\/10\/mlcommons-and-big-tech-to-develop-ai-safety-benchmarks\/","title":{"rendered":"MLCommons e Big Tech per sviluppare parametri di sicurezza dell'IA"},"content":{"rendered":"<p><strong>Il dibattito sulla sicurezza dell'IA continua a essere un tema caldo, ma il settore non ha una definizione definitiva di cosa sia l'IA \"sicura\", n\u00e9 un parametro di riferimento per confrontare la sicurezza dei diversi modelli.<\/strong><\/p>\n<p>MLCommons ha coinvolto una serie di aziende nei suoi diversi gruppi di lavoro per diventare la principale organizzazione di benchmarking dell'IA.<\/p>\n<p>Quando confrontiamo le prestazioni di inferenza delle GPU di un produttore con quelle di un altro o popoliamo una classifica LLM, possiamo farlo perch\u00e9 abbiamo dei benchmark. Benchmark come MLPerf e test standardizzati ci permettono di dire \"Questo \u00e8 migliore di quello\".<\/p>\n<p>Ma quando si tratta di sicurezza dell'IA non abbiamo un benchmark standard del settore che ci permetta di dire: \"Questo LLM \u00e8 pi\u00f9 sicuro di quell'altro\".<\/p>\n<p>Con la formazione del gruppo di lavoro sulla sicurezza dell'IA (AIS), MLCommons intende sviluppare una serie di parametri di riferimento per la sicurezza dell'IA per renderla possibile.<\/p>\n<p>Alcune aziende e organizzazioni hanno gi\u00e0 lavorato in questo ambito. I guardrail di Google per l'IA generativa e i RealToxicityPrompts dell'Universit\u00e0 di Washington sono buoni esempi.<\/p>\n<p>Ma questi test di benchmarking si basano sull'inserimento di un elenco specifico di richieste e dicono solo quanto \u00e8 sicuro il modello in base a quell'insieme di richieste.<\/p>\n<p>Questi test, inoltre, di solito utilizzano insiemi di dati aperti per le richieste e le risposte. I LLM sotto esame potrebbero essere stati addestrati anche su questi set di dati, quindi i risultati del test potrebbero essere falsati.<\/p>\n<p>Il Centro per la Ricerca sui Modelli di Fondazione dell'Universit\u00e0 di Stanford ha svolto un lavoro innovativo con lo sviluppo del suo <a href=\"https:\/\/crfm.stanford.edu\/2022\/11\/17\/helm.html\" target=\"_blank\" rel=\"noopener\">Valutazione olistica dei modelli linguistici<\/a> (HELM). HELM utilizza un'ampia gamma di metriche e scenari per testare la sicurezza del LLM in modo pi\u00f9 olistico.<\/p>\n<p>L'AIS si baser\u00e0 sul quadro HELM per sviluppare i suoi parametri di sicurezza per i modelli linguistici di grandi dimensioni. Invita inoltre una pi\u00f9 ampia partecipazione dell'industria.<\/p>\n<p>Nell'annuncio di MLCommons si legge: \"Ci aspettiamo che diverse aziende esternalizzino i test di sicurezza dell'intelligenza artificiale che hanno utilizzato internamente per scopi proprietari e li condividano apertamente con la comunit\u00e0 MLCommons, il che contribuir\u00e0 ad accelerare il ritmo dell'innovazione\".<\/p>\n<p>I grandi nomi che compongono il gruppo di lavoro AIS includono Anthropic, Coactive AI, Google, Inflection, Intel, Meta, Microsoft, NVIDIA, OpenAI, Qualcomm Technologies, oltre a studiosi di AI.<\/p>\n<p>Una volta che l'industria dell'IA riuscir\u00e0 a concordare un parametro di riferimento per la sicurezza, si potranno fare sforzi come il <a href=\"https:\/\/dailyai.com\/it\/2023\/10\/uk-government-releases-schedule-for-the-ai-safety-summit\/\">Vertice sulla sicurezza dell'intelligenza artificiale<\/a> pi\u00f9 produttivo.<\/p>\n<p>Inoltre, i regolatori governativi potrebbero insistere affinch\u00e9 le aziende di IA raggiungano un punteggio specifico su un benchmark prima di consentire il rilascio dei loro modelli.<\/p>\n<p>Le classifiche sono anche ottimi strumenti di marketing, quindi avere una scorecard per la sicurezza accettata dal settore \u00e8 pi\u00f9 probabile che spinga il budget di progettazione verso la sicurezza dell'IA.<\/p>","protected":false},"excerpt":{"rendered":"<p>Il dibattito sulla sicurezza dell'IA continua a essere un tema caldo, ma il settore non ha una definizione definitiva di cosa sia l'IA \"sicura\", n\u00e9 un parametro di riferimento per confrontare la sicurezza dei diversi modelli. MLCommons ha coinvolto una serie di aziende nei suoi diversi gruppi di lavoro per diventare la principale organizzazione di benchmarking dell'IA. Quando confrontiamo le prestazioni di inferenza delle GPU di un produttore con quelle di un altro o popoliamo una classifica di LLM, possiamo farlo perch\u00e9 abbiamo dei benchmark. Benchmark come MLPerf e test standardizzati ci permettono di dire \"Questo \u00e8 meglio di quello\". Ma quando si tratta di sicurezza dell'IA<\/p>","protected":false},"author":6,"featured_media":6901,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[],"class_list":["post-6896","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>MLCommons and Big Tech to develop AI safety benchmarks | 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\/2023\/10\/mlcommons-and-big-tech-to-develop-ai-safety-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MLCommons and Big Tech to develop AI safety benchmarks | DailyAI\" \/>\n<meta property=\"og:description\" content=\"The debate around AI safety continues to be a hot topic but the industry doesn\u2019t have a definitive definition of what \u201csafe\u201d AI is, or a benchmark to compare how safe different models are. MLCommons has brought a range of companies into its different working groups to become the leading AI benchmarking organization. When we compare one manufacturer\u2019s GPU inference performance with another or populate an LLM leaderboard, we can do that because we have benchmarks. Benchmarks like MLPerf, and standardized tests enable us to say \u201cThis one is better than that one.\u201d But when it comes to AI safety\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/it\/2023\/10\/mlcommons-and-big-tech-to-develop-ai-safety-benchmarks\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-29T12:53:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/10\/benchmarking-AI.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"637\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Eugene van der Watt\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta 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