{"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\/es\/2023\/10\/mlcommons-and-big-tech-to-develop-ai-safety-benchmarks\/","title":{"rendered":"MLCommons y Big Tech desarrollar\u00e1n par\u00e1metros de seguridad para la IA"},"content":{"rendered":"<p><strong>El debate en torno a la seguridad de la IA sigue siendo un tema candente, pero el sector no dispone de una definici\u00f3n definitiva de lo que es una IA \"segura\", ni de una referencia para comparar la seguridad de los distintos modelos.<\/strong><\/p>\n<p>MLCommons ha reunido a diversas empresas en sus diferentes grupos de trabajo para convertirse en la principal organizaci\u00f3n de evaluaci\u00f3n comparativa de la IA.<\/p>\n<p>Cuando comparamos el rendimiento de inferencia de la GPU de un fabricante con el de otro o rellenamos una tabla de clasificaci\u00f3n de LLM, podemos hacerlo porque disponemos de puntos de referencia. Benchmarks como MLPerf y pruebas estandarizadas nos permiten decir \"Este es mejor que aquel\".<\/p>\n<p>Pero en lo que respecta a la seguridad de la IA no tenemos realmente un punto de referencia est\u00e1ndar en el sector que nos permita decir: \"Este LLM es m\u00e1s seguro que aquel\".<\/p>\n<p>Con la formaci\u00f3n del Grupo de Trabajo sobre Seguridad de la IA (AIS), MLCommons quiere desarrollar un conjunto de puntos de referencia sobre seguridad de la IA para hacerlo posible.<\/p>\n<p>Algunas empresas y organizaciones ya han trabajado en este \u00e1mbito. Los guardrails de Google para la IA generativa y los RealToxicityPrompts de la Universidad de Washington son buenos ejemplos.<\/p>\n<p>Pero estas pruebas de evaluaci\u00f3n comparativa se basan en la introducci\u00f3n de una lista espec\u00edfica de instrucciones y s\u00f3lo indican realmente el grado de seguridad del modelo en funci\u00f3n de ese conjunto de instrucciones de prueba.<\/p>\n<p>Estas pruebas tambi\u00e9n suelen utilizar conjuntos de datos abiertos para las preguntas y las respuestas. Los LLM sometidos a prueba tambi\u00e9n pueden haber sido entrenados con estos conjuntos de datos, por lo que los resultados de las pruebas podr\u00edan estar sesgados.<\/p>\n<p>El Centro de Investigaci\u00f3n sobre Modelos de Fundaciones de la Universidad de Stanford realiz\u00f3 un trabajo pionero con el desarrollo de su <a href=\"https:\/\/crfm.stanford.edu\/2022\/11\/17\/helm.html\" target=\"_blank\" rel=\"noopener\">Evaluaci\u00f3n hol\u00edstica de modelos ling\u00fc\u00edsticos<\/a> (HELM). HELM utiliza una amplia gama de m\u00e9tricas y escenarios para probar la seguridad de los LLM de una forma m\u00e1s hol\u00edstica.<\/p>\n<p>AIS se basar\u00e1 en el marco HELM para desarrollar sus par\u00e1metros de seguridad para grandes modelos ling\u00fc\u00edsticos. Tambi\u00e9n est\u00e1 invitando a una mayor participaci\u00f3n de la industria.<\/p>\n<p>Seg\u00fan el anuncio de MLCommons, \"esperamos que varias empresas externalicen las pruebas de seguridad de IA que han utilizado internamente con fines de propiedad y las compartan abiertamente con la comunidad de MLCommons, lo que contribuir\u00e1 a acelerar el ritmo de la innovaci\u00f3n.\"<\/p>\n<p>Entre los grandes nombres que integran el Grupo de Trabajo AIS figuran Anthropic, Coactive AI, Google, Inflection, Intel, Meta, Microsoft, NVIDIA, OpenAI, Qualcomm Technologies, as\u00ed como acad\u00e9micos especializados en IA.<\/p>\n<p>Una vez que la industria de la IA pueda ponerse de acuerdo sobre un punto de referencia de seguridad, realizar\u00e1 esfuerzos como el <a href=\"https:\/\/dailyai.com\/es\/2023\/10\/uk-government-releases-schedule-for-the-ai-safety-summit\/\">Cumbre sobre seguridad de la IA<\/a> m\u00e1s productivo.<\/p>\n<p>Adem\u00e1s, los reguladores gubernamentales podr\u00edan insistir en que las empresas de IA alcancen una puntuaci\u00f3n espec\u00edfica en un par\u00e1metro de referencia antes de permitir que sus modelos salgan a la venta.<\/p>\n<p>Las tablas de clasificaci\u00f3n tambi\u00e9n son excelentes herramientas de marketing, por lo que contar con un sistema de puntuaci\u00f3n de la seguridad aceptado por el sector tiene m\u00e1s probabilidades de dirigir el presupuesto de ingenier\u00eda hacia la seguridad de la IA.<\/p>","protected":false},"excerpt":{"rendered":"<p>El debate en torno a la seguridad de la IA sigue siendo un tema candente, pero el sector carece de una definici\u00f3n definitiva de lo que es una IA \"segura\" o de un punto de referencia para comparar la seguridad de los distintos modelos. MLCommons ha reunido a diversas empresas en sus diferentes grupos de trabajo para convertirse en la principal organizaci\u00f3n de evaluaci\u00f3n comparativa de la IA. Cuando comparamos el rendimiento de inferencia de la GPU de un fabricante con el de otro o rellenamos una tabla de clasificaci\u00f3n de LLM, podemos hacerlo porque disponemos de puntos de referencia. Puntos de referencia como MLPerf y pruebas estandarizadas nos permiten decir \"Este es mejor que aquel\". Pero cuando se trata de la seguridad de la 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\/es\/2023\/10\/mlcommons-and-big-tech-to-develop-ai-safety-benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\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\/es\/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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