{"id":10786,"date":"2024-03-18T09:35:31","date_gmt":"2024-03-18T09:35:31","guid":{"rendered":"https:\/\/dailyai.com\/?p=10786"},"modified":"2024-03-28T09:35:17","modified_gmt":"2024-03-28T09:35:17","slug":"apple-reveals-mm1-its-first-family-of-multimodal-llms","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/03\/apple-reveals-mm1-its-first-family-of-multimodal-llms\/","title":{"rendered":"Apple revela MM1, su primera familia de LLM multimodales"},"content":{"rendered":"

Apple a\u00fan no ha lanzado oficialmente un modelo de IA, pero un nuevo trabajo de investigaci\u00f3n da una idea de los avances de la compa\u00f1\u00eda en el desarrollo de modelos con capacidades multimodales de \u00faltima generaci\u00f3n.<\/strong><\/p>\n

El peri\u00f3dico<\/a>titulado \"MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training\", presenta la familia de MLLM de Apple denominada MM1.<\/p>\n

MM1 muestra unas capacidades impresionantes en el subtitulado de im\u00e1genes, la respuesta a preguntas visuales (VQA) y la inferencia de lenguaje natural. Los investigadores explican que la cuidadosa elecci\u00f3n de los pares imagen-titular les permiti\u00f3 obtener resultados superiores, especialmente en escenarios de aprendizaje con pocas im\u00e1genes.<\/p>\n

Lo que diferencia al MM1 de otros MLLM es su capacidad superior para seguir instrucciones a trav\u00e9s de m\u00faltiples im\u00e1genes y para razonar sobre las complejas escenas que se le presentan.<\/p>\n

Los modelos MM1 contienen hasta 30B de par\u00e1metros, el triple que GPT-4V, el componente que dota a GPT-4 de OpenAI de sus capacidades de visi\u00f3n.<\/p>\n

Estos son algunos ejemplos de las capacidades VQA de MM1.<\/p>\n

\"\"
Probando la capacidad de MM1 para razonar a trav\u00e9s de im\u00e1genes y textos. Fuente: arXiv<\/figcaption><\/figure>\n

MM1 se someti\u00f3 a un preentrenamiento multimodal a gran escala sobre \"un conjunto de datos de 500M documentos imagen-texto intercalados, que conten\u00edan 1B im\u00e1genes y 500B tokens de texto\".<\/p>\n

La escala y la diversidad de su preentrenamiento permiten a MM1 realizar impresionantes predicciones en contexto y seguir un formato personalizado con un peque\u00f1o n\u00famero de ejemplos de pocas tomas. A continuaci\u00f3n se muestran ejemplos de c\u00f3mo MM1 aprende la salida y el formato deseados a partir de solo 3 ejemplos.<\/p>\n

\"\"
MM1 puede contar objetos, realizar OCR en \u00e1reas espec\u00edficas de una imagen, aplicar razonamientos de sentido com\u00fan a objetos y realizar funciones matem\u00e1ticas b\u00e1sicas. Fuente: arXiv<\/figcaption><\/figure>\n

Para crear modelos de IA que puedan \"ver\" y razonar se necesita un conector de visi\u00f3n-lenguaje que traduzca las im\u00e1genes y el lenguaje en una representaci\u00f3n unificada que el modelo pueda utilizar para su posterior procesamiento.<\/p>\n

Los investigadores descubrieron que el dise\u00f1o del conector visi\u00f3n-lenguaje era un factor menos determinante en el rendimiento de MM1. Curiosamente, fueron la resoluci\u00f3n de la imagen y el n\u00famero de tokens de imagen los que tuvieron un mayor impacto.<\/p>\n

Es interesante ver lo abierta que ha sido Apple a la hora de compartir sus investigaciones con la comunidad de la IA en general. Los investigadores afirman que \"en este art\u00edculo, documentamos el proceso de construcci\u00f3n de MLLM e intentamos formular lecciones de dise\u00f1o, que esperamos sean de utilidad para la comunidad.\"<\/p>\n

Es probable que los resultados publicados orienten a otros desarrolladores de MMLM en la elecci\u00f3n de la arquitectura y los datos de preentrenamiento.<\/p>\n

Queda por ver c\u00f3mo se implementar\u00e1n exactamente los modelos MM1 en los productos de Apple. Los ejemplos publicados de las capacidades de MM1 apuntan a que Siri ser\u00e1 mucho m\u00e1s inteligente cuando aprenda a ver.<\/p>","protected":false},"excerpt":{"rendered":"

Apple a\u00fan no ha lanzado oficialmente un modelo de IA, pero un nuevo trabajo de investigaci\u00f3n da una idea de los avances de la empresa en el desarrollo de modelos con capacidades multimodales de \u00faltima generaci\u00f3n. El art\u00edculo, titulado \"MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training\", presenta la familia de MLLM de Apple denominada MM1. MM1 muestra impresionantes capacidades en subtitulado de im\u00e1genes, respuesta a preguntas visuales (VQA) e inferencia de lenguaje natural. Los investigadores explican que la cuidadosa elecci\u00f3n de los pares imagen-titular les permiti\u00f3 obtener resultados superiores, especialmente en escenarios de aprendizaje con pocas im\u00e1genes. Lo que distingue a MM1 de otros MLLM es su capacidad superior para seguir instrucciones a trav\u00e9s de varias im\u00e1genes y de un lenguaje natural.<\/p>","protected":false},"author":6,"featured_media":10790,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[126,166],"class_list":["post-10786","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-apple","tag-computer-vision"],"yoast_head":"\nApple reveals MM1, its first family of multimodal LLMs | 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\/2024\/03\/apple-reveals-mm1-its-first-family-of-multimodal-llms\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Apple reveals MM1, its first family of multimodal LLMs | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Apple is yet to officially release an AI model, but a new research paper gives an insight into the company\u2019s progress in developing models with state-of-the-art multimodal capabilities. 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