{"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\/de\/2023\/10\/mlcommons-and-big-tech-to-develop-ai-safety-benchmarks\/","title":{"rendered":"MLCommons und Big Tech entwickeln Benchmarks f\u00fcr KI-Sicherheit"},"content":{"rendered":"<p><strong>Die Debatte \u00fcber die Sicherheit von KI ist nach wie vor ein hei\u00dfes Thema, aber die Branche hat keine endg\u00fcltige Definition daf\u00fcr, was \"sichere\" KI ist, oder einen Ma\u00dfstab, um zu vergleichen, wie sicher verschiedene Modelle sind.<\/strong><\/p>\n<p>MLCommons hat eine Reihe von Unternehmen in seine verschiedenen Arbeitsgruppen aufgenommen, um die f\u00fchrende KI-Benchmarking-Organisation zu werden.<\/p>\n<p>Wenn wir die GPU-Inferenzleistung eines Herstellers mit der eines anderen vergleichen oder eine LLM-Bestenliste aufstellen, k\u00f6nnen wir das tun, weil wir Benchmarks haben. Benchmarks wie MLPerf und standardisierte Tests erm\u00f6glichen es uns, zu sagen: \"Diese ist besser als jene\".<\/p>\n<p>Aber wenn es um die Sicherheit von KI geht, haben wir nicht wirklich einen Industriestandard, der es uns erlaubt zu sagen: \"Dieses LLM ist sicherer als jenes\".<\/p>\n<p>Mit der Gr\u00fcndung der Arbeitsgruppe f\u00fcr KI-Sicherheit (AIS) will MLCommons eine Reihe von KI-Sicherheitsma\u00dfst\u00e4ben entwickeln, um dies zu erm\u00f6glichen.<\/p>\n<p>Einige Unternehmen und Organisationen haben bereits einige Arbeiten in diesem Bereich durchgef\u00fchrt. Die Leitplanken von Google f\u00fcr generative KI und die RealToxicityPrompts der University of Washington sind gute Beispiele.<\/p>\n<p>Diese Benchmarking-Tests beruhen jedoch auf der Eingabe einer bestimmten Liste von Eingabeaufforderungen und sagen nur aus, wie sicher das Modell auf der Grundlage dieses Testaufforderungssatzes ist.<\/p>\n<p>Diese Tests verwenden in der Regel auch offene Datens\u00e4tze f\u00fcr die Eingabeaufforderungen und Antworten. Die zu pr\u00fcfenden LLMs k\u00f6nnen durchaus auch auf diesen Datens\u00e4tzen trainiert worden sein, so dass die Testergebnisse verzerrt sein k\u00f6nnten.<\/p>\n<p>Das Center for Research on Foundation Models der Stanford University leistete bahnbrechende Arbeit mit der Entwicklung seines <a href=\"https:\/\/crfm.stanford.edu\/2022\/11\/17\/helm.html\" target=\"_blank\" rel=\"noopener\">Ganzheitliche Bewertung von Sprachmodellen<\/a> (HELM). HELM verwendet eine breite Palette von Metriken und Szenarien, um die Sicherheit von LLM auf eine ganzheitlichere Weise zu testen.<\/p>\n<p>Die AIS wird auf dem HELM-Rahmen aufbauen, um ihre Sicherheitsbenchmarks f\u00fcr gro\u00dfe Sprachmodelle zu entwickeln. Es l\u00e4dt auch eine breitere Industrie zur Teilnahme ein.<\/p>\n<p>In der Ank\u00fcndigung von MLCommons hei\u00dft es: \"Wir erwarten, dass mehrere Unternehmen KI-Sicherheitstests, die sie intern f\u00fcr propriet\u00e4re Zwecke verwendet haben, externalisieren und offen mit der MLCommons-Community teilen, was zu einer Beschleunigung der Innovation beitragen wird.\"<\/p>\n<p>Zu den gro\u00dfen Namen, die die AIS-Arbeitsgruppe bilden, geh\u00f6ren Anthropic, Coactive AI, Google, Inflection, Intel, Meta, Microsoft, NVIDIA, OpenAI, Qualcomm Technologies sowie KI-Wissenschaftler.<\/p>\n<p>Sobald sich die KI-Branche auf einen Sicherheitsma\u00dfstab einigen kann, wird sie Anstrengungen wie das <a href=\"https:\/\/dailyai.com\/de\/2023\/10\/uk-government-releases-schedule-for-the-ai-safety-summit\/\">AI-Sicherheitsgipfel<\/a> produktiver.<\/p>\n<p>Au\u00dferdem k\u00f6nnten staatliche Regulierungsbeh\u00f6rden darauf bestehen, dass KI-Unternehmen eine bestimmte Punktzahl in einem Benchmark erreichen, bevor sie ihre Modelle freigeben d\u00fcrfen.<\/p>\n<p>Leaderboards sind auch ein hervorragendes Marketinginstrument, so dass eine branchenweit anerkannte Scorecard f\u00fcr Sicherheit die Wahrscheinlichkeit erh\u00f6ht, dass das Entwicklungsbudget in die KI-Sicherheit flie\u00dft.<\/p>","protected":false},"excerpt":{"rendered":"<p>Die Debatte \u00fcber die Sicherheit von KI ist nach wie vor ein hei\u00dfes Thema, aber die Branche verf\u00fcgt weder \u00fcber eine endg\u00fcltige Definition dessen, was \"sichere\" KI ist, noch \u00fcber einen Ma\u00dfstab, um zu vergleichen, wie sicher verschiedene Modelle sind. MLCommons hat eine Reihe von Unternehmen in seine verschiedenen Arbeitsgruppen aufgenommen, um die f\u00fchrende KI-Benchmarking-Organisation zu werden. Wenn wir die GPU-Inferenzleistung eines Herstellers mit der eines anderen vergleichen oder eine LLM-Bestenliste aufstellen, k\u00f6nnen wir das tun, weil wir Benchmarks haben. Benchmarks wie MLPerf und standardisierte Tests erm\u00f6glichen es uns, zu sagen: \"Dieser ist besser als jener\". Aber wenn es um KI-Sicherheit geht<\/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\/de\/2023\/10\/mlcommons-and-big-tech-to-develop-ai-safety-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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\/de\/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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