{"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\/es\/2023\/06\/new-mit-self-learning-language-models-can-beat-other-llms\/","title":{"rendered":"Los nuevos modelos ling\u00fc\u00edsticos de aprendizaje autom\u00e1tico del MIT pueden superar a otros LLM"},"content":{"rendered":"<p><span style=\"font-weight: 400\">En los \u00faltimos seis meses, hemos asistido a un boom revolucionario de <\/span><a href=\"https:\/\/dailyai.com\/es\/2023\/05\/ai-powered-technology-slows-the-pacific-salmon-invasion-of-europe\/\"><span style=\"font-weight: 400\">Inteligencia artificial<\/span><\/a><span style=\"font-weight: 400\"> Los LLM (Large Language Models) ocupan un lugar central. Pero, \u00bfes siempre necesario que un producto o servicio de IA se base en LLM? Seg\u00fan un art\u00edculo, los nuevos modelos ling\u00fc\u00edsticos de autoaprendizaje del MIT no se basan en LLM y pueden superar a algunos de los otros grandes sistemas de IA que actualmente lideran el sector.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Un grupo de investigadores del Laboratorio de Ciencias de la Computaci\u00f3n e Inteligencia Artificial (CSAIL) del MIT ha desarrollado una nueva forma de abordar los modelos ling\u00fc\u00edsticos de la IA.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Se trata de un logro pionero que hace hincapi\u00e9 en los modelos ling\u00fc\u00edsticos m\u00e1s peque\u00f1os y en su capacidad para resolver problemas de ineficacia, as\u00ed como en las preocupaciones por la privacidad relacionadas con el desarrollo de grandes modelos de IA basados en datos textuales.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Con la aparici\u00f3n de <\/span><a href=\"https:\/\/dailyai.com\/es\/2023\/06\/openai-unveils-1m-cybersecurity-grant-program\/\"><span style=\"font-weight: 400\">OpenAI<\/span><\/a><span style=\"font-weight: 400\"> ChatGPT basado en los modelos ling\u00fc\u00edsticos GPT-3 y GPT-4, muchas empresas se sumaron a la carrera de la IA, entre ellas Google Bard, y otras <\/span><a href=\"https:\/\/dailyai.com\/es\/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\"> sistemas que permiten generar textos, im\u00e1genes e incluso v\u00eddeos.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Sin embargo, para generar resultados de calidad impecable, estos sistemas se basan en una gran cantidad de datos cuyo procesamiento inform\u00e1tico es costoso. Muchos de estos sistemas importan datos para el entrenamiento a trav\u00e9s de API, lo que conlleva sus propios riesgos, como fugas de datos y otros problemas de privacidad.<\/span><\/p>\n<h2><b>Vinculaci\u00f3n textual<\/b><\/h2>\n<p><span style=\"font-weight: 400\">Seg\u00fan un nuevo documento titulado<\/span><a href=\"https:\/\/arxiv.org\/pdf\/2305.17197.pdf\"> <span style=\"font-weight: 400\">La vinculaci\u00f3n como autoaprendizaje robusto<\/span><\/a><span style=\"font-weight: 400\"> publicado actualmente en el repositorio de preimpresi\u00f3n en l\u00ednea arXiv, los investigadores se\u00f1alan que los nuevos modelos ling\u00fc\u00edsticos de aprendizaje autom\u00e1tico del MIT pueden resolver el problema de comprensi\u00f3n de ciertas tareas ling\u00fc\u00edsticas que tienen los grandes modelos ling\u00fc\u00edsticos. A este logro pionero lo denominan vinculaci\u00f3n textual.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Los modelos se basan en el concepto de que si hay dos frases -una premisa y una hip\u00f3tesis-, en el caso de que una premisa de la primera frase sea cierta, es probable que la hip\u00f3tesis tambi\u00e9n lo sea.<\/span><\/p>\n<p><span style=\"font-weight: 400\">En un<\/span><a href=\"https:\/\/www.csail.mit.edu\/news\/mit-researchers-make-language-models-scalable-self-learners\"> <span style=\"font-weight: 400\">declaraci\u00f3n<\/span><\/a><span style=\"font-weight: 400\"> publicado en el blog CSAIL del MIT, un ejemplo de esta estructura ser\u00eda que si \"todos los gatos tienen cola\" es probable que la hip\u00f3tesis \"un gato atigrado tiene cola\" sea cierta. Este enfoque conduce a un menor sesgo en <\/span><a href=\"https:\/\/dailyai.com\/es\/2023\/05\/researchers-create-custom-tooth-crowns-using-3d-ai-model\/\"><span style=\"font-weight: 400\">Modelos de IA<\/span><\/a><span style=\"font-weight: 400\">, lo que hace que los nuevos modelos ling\u00fc\u00edsticos de aprendizaje autom\u00e1tico del MIT superen a los modelos ling\u00fc\u00edsticos de mayor tama\u00f1o, seg\u00fan el comunicado.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\"Nuestros modelos de vinculaci\u00f3n autoentrenados, con 350 millones de par\u00e1metros y sin etiquetas generadas por humanos, superan a los modelos ling\u00fc\u00edsticos supervisados con entre 137.000 y 175.000 millones de par\u00e1metros\", afirma en un comunicado Hongyin Luo, postdoctorando del MIT CSAIL y autor principal. \"<\/span><\/p>\n<p><span style=\"font-weight: 400\">Tambi\u00e9n a\u00f1adi\u00f3 que este enfoque podr\u00eda ser muy beneficioso para los actuales sistemas de IA y remodelar los sistemas de aprendizaje autom\u00e1tico de forma que sean m\u00e1s escalables, fiables y rentables cuando trabajen con modelos ling\u00fc\u00edsticos.<\/span><\/p>\n<h2>Los nuevos modelos ling\u00fc\u00edsticos de aprendizaje autom\u00e1tico del MIT siguen siendo limitados<\/h2>\n<p><span style=\"font-weight: 400\">Aunque los nuevos modelos ling\u00fc\u00edsticos de aprendizaje autom\u00e1tico del MIT prometen mucho a la hora de resolver problemas de clasificaci\u00f3n binaria, siguen estando limitados a la hora de resolver problemas de clasificaci\u00f3n multiclase. Eso significa que la vinculaci\u00f3n textual no funciona tan bien cuando al modelo se le presentan m\u00faltiples opciones.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Seg\u00fan James Glass, profesor del MIT e investigador principal del CSAIL, autor tambi\u00e9n del art\u00edculo, esta investigaci\u00f3n podr\u00eda arrojar luz sobre m\u00e9todos eficientes y eficaces para entrenar a los LLM en la comprensi\u00f3n de problemas de vinculaci\u00f3n contextual.<\/span><\/p>\n<p><span style=\"font-weight: 400\">\"Aunque el campo de los LLM est\u00e1 experimentando cambios r\u00e1pidos y espectaculares, esta investigaci\u00f3n demuestra que es posible producir modelos ling\u00fc\u00edsticos relativamente compactos que rinden muy bien en tareas de comprensi\u00f3n de referencia en comparaci\u00f3n con sus hom\u00f3logos de aproximadamente el mismo tama\u00f1o, o incluso con modelos ling\u00fc\u00edsticos mucho m\u00e1s grandes\", afirm\u00f3.\"<\/span><\/p>\n<p><span style=\"font-weight: 400\">Esta investigaci\u00f3n es s\u00f3lo el principio de futuras tecnolog\u00edas de IA que podr\u00edan aprender por s\u00ed solas y ser m\u00e1s eficaces, sostenibles y centradas en la privacidad de los datos. El trabajo sobre los nuevos modelos ling\u00fc\u00edsticos de autoaprendizaje del MIT se presentar\u00e1 en julio en la reuni\u00f3n de la Asociaci\u00f3n de Ling\u00fc\u00edstica Computacional en Toronto. El proyecto tambi\u00e9n cuenta con el respaldo del<\/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\">Programa Hong Kong Innovation AI<\/span><\/a><span style=\"font-weight: 400\">.<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>En los \u00faltimos seis meses, hemos asistido a un auge revolucionario de los LLM (Large Language Models) basados en IA. Pero, \u00bfes siempre necesario que un producto o servicio de IA se base en LLM? Seg\u00fan un art\u00edculo, los nuevos modelos ling\u00fc\u00edsticos de aprendizaje autom\u00e1tico del MIT no se basan en LLM y pueden superar a algunos de los otros grandes sistemas de IA que actualmente lideran el sector. Un grupo de investigadores del Laboratorio de Ciencias de la Computaci\u00f3n e Inteligencia Artificial (CSAIL) del MIT ha desarrollado una nueva forma de abordar los modelos ling\u00fc\u00edsticos de IA. Se trata de un logro pionero que hace hincapi\u00e9 en modelos ling\u00fc\u00edsticos m\u00e1s peque\u00f1os y en su capacidad para abordar<\/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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