{"id":11235,"date":"2024-04-03T14:29:19","date_gmt":"2024-04-03T14:29:19","guid":{"rendered":"https:\/\/dailyai.com\/?p=11235"},"modified":"2024-04-03T14:29:19","modified_gmt":"2024-04-03T14:29:19","slug":"berkeley-researchers-build-ai-forecasting-system-that-bests-humans","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/","title":{"rendered":"Investigadores de Berkeley crean un sistema de previsi\u00f3n artificial que supera a los humanos"},"content":{"rendered":"

Investigadores de la Universidad de Berkeley (California) desarrollaron un sistema de previsi\u00f3n artificial para predecir acontecimientos futuros con una exactitud similar a la de la sabidur\u00eda popular humana.\u00a0<\/b><\/p>\n

Dado que los LLM no est\u00e1n dise\u00f1ados espec\u00edficamente para la predicci\u00f3n de sucesos, el equipo cre\u00f3 un sistema de predicci\u00f3n basado en GPT-4 utilizando un m\u00e9todo novedoso denominado razonamiento de recuperaci\u00f3n aumentada.<\/p>\n

Este proceso de varios pasos implicaba entrenar a GPT-4 para que buscara informaci\u00f3n pertinente, evaluara su relevancia y la integrara en su proceso de razonamiento antes de realizar una predicci\u00f3n.\u00a0<\/span><\/p>\n

As\u00ed es como funciona:<\/span><\/p>\n

    \n
  1. Recuperaci\u00f3n<\/b>: El sistema de IA utiliza GPT-4 para generar consultas de b\u00fasqueda basadas en la pregunta de previsi\u00f3n y las subpreguntas, recuperando un amplio conjunto de art\u00edculos de noticias potencialmente relevantes.<\/span><\/li>\n
  2. Evaluaci\u00f3n de la pertinencia<\/b>: GPT-4 eval\u00faa la pertinencia de cada art\u00edculo recuperado, descartando los art\u00edculos de baja puntuaci\u00f3n para reducir el conjunto de informaci\u00f3n.<\/span><\/li>\n
  3. Resumen<\/b>: GPT-4 reduce cada art\u00edculo a sus puntos clave, centr\u00e1ndose en los detalles relacionados con la pregunta de previsi\u00f3n.<\/span><\/li>\n
  4. Razonamiento<\/b>: GPT-4 analiza los art\u00edculos resumidos y elabora un pron\u00f3stico detallado con una explicaci\u00f3n razonada. Estas indicaciones gu\u00edan el proceso de razonamiento del modelo, fomentando un enfoque sistem\u00e1tico del razonamiento.<\/span><\/li>\n<\/ol>\n

    A continuaci\u00f3n, el equipo de Berkeley llev\u00f3 el sistema un paso m\u00e1s all\u00e1 con un ajuste fino autosupervisado.\u00a0<\/span><\/p>\n

    Generaron un gran n\u00famero de pron\u00f3sticos de IA sobre preguntas anteriores con respuestas conocidas y seleccionaron ejemplos en los que la IA hab\u00eda superado la \"sabidur\u00eda de la multitud\", definida como las predicciones agregadas de los pronosticadores humanos.<\/span><\/p>\n

    Al ajustar GPT-4 a estos ejemplos, los investigadores ense\u00f1aron al modelo a emular los patrones de razonamiento que generaban las mejores previsiones.<\/span><\/p>\n

    Resultados<\/h2>\n

    Cuando se someti\u00f3 a pruebas de previsi\u00f3n a partir de junio de 2023, la IA obtuvo una puntuaci\u00f3n Brier de 0,179, frente a la puntuaci\u00f3n de 0,149 del pronosticador humano.\u00a0<\/span><\/p>\n

    La IA funcion\u00f3 especialmente bien en preguntas con una elevada incertidumbre humana al principio del proceso de previsi\u00f3n y cuando tuvo acceso a suficientes art\u00edculos relevantes sobre un tema concreto.\u00a0<\/span><\/p>\n

    \"Previsi\u00f3n
    (a) El sistema obtiene mejores resultados que un grupo de personas cuando tiene de 0 a 10 art\u00edculos relevantes.
    (b) Cuando las personas no est\u00e1n seguras de sus predicciones (niveles de confianza entre 0,3 y 0,7), el sistema obtiene mejores resultados, con una puntuaci\u00f3n Brier de 0,199 frente a la suya de 0,246. Sin embargo, cuando las personas est\u00e1n muy seguras (predicciones por debajo de 0,05), obtienen mejores resultados que nuestro sistema. Sin embargo, cuando las personas est\u00e1n muy seguras (predicciones por debajo de 0,05), obtienen mejores resultados que nuestro sistema.
    (c) La precisi\u00f3n del sistema es mayor al principio de la recogida de informaci\u00f3n. Fuente: ArXiv<\/a> (acceso abierto).<\/figcaption><\/figure>\n

    Los autores escriben en el <\/span>estudiar<\/span><\/a>\"Que sepamos, es el primer sistema automatizado con capacidad de previsi\u00f3n que se acerca al nivel de la multitud humana, que suele ser m\u00e1s fuerte que los pronosticadores humanos individuales\".<\/span><\/p>\n

    Hubo una peque\u00f1a peculiaridad, ya que el sistema pareci\u00f3 empeorar con m\u00e1s art\u00edculos a partir de los cuales trabajar y, por tanto, mayor certeza sobre la previsi\u00f3n. Esto puede deberse a que el modelo \"cubre\" sus predicciones.<\/p>\n

    Los investigadores lo describen de la siguiente manera: \"Nuestra hip\u00f3tesis es que esto se debe a la tendencia de nuestro modelo a cubrir las predicciones debido a su entrenamiento en seguridad\".<\/p>\n

    Implicaciones<\/h2>\n

    Seg\u00fan los investigadores, los responsables pol\u00edticos, las empresas y los funcionarios de salud p\u00fablica podr\u00edan beneficiarse de esta forma de previsi\u00f3n de la IA basada en el lenguaje.<\/span><\/p>\n

    \"En el futuro, los responsables pol\u00edticos podr\u00e1n consultar a las IA sobre qu\u00e9 acciones tendr\u00edan m\u00e1s probabilidades de producir los resultados deseados\", afirma Dan Hendrycks, del Centro para la Seguridad de la IA de California.<\/p>\n

    Hendrycks propone modelos de predicci\u00f3n para hacer frente a los riesgos que plantea la IA. \"Los robots de predicci\u00f3n nos ayudar\u00edan a anticipar y evitar estos riesgos\", afirma Hendrycks. dijo a New Scientist<\/a>.<\/p>\n

    Se han hecho otros intentos de predecir acontecimientos vitales complejos con IA, incluido un modelo entrenado por investigadores daneses para <\/span>predecir los riesgos de muerte prematura<\/span><\/a>.\u00a0<\/span><\/p>\n

    Aprovechar la IA para aplicaciones predictivas que afectan a la vida de las personas plantea cuestiones \u00e9ticas, como garantizar que estos sistemas sean transparentes, imparciales y tengan un fundamento \u00e9tico. <\/span><\/p>\n

    Este nuevo estudio de Berkeley esboza c\u00f3mo la IA puede hacer previsiones eficaces, pero no podemos calibrar con qu\u00e9 precisi\u00f3n llega a sus decisiones.<\/span><\/p>\n

    El uso de la IA para predecir grandes acontecimientos sociales e individuales puede parecer un concepto dist\u00f3pico, pero ya es una pr\u00e1ctica extendida en muchas partes del mundo.<\/span><\/p>\n

    En varios pa\u00edses democr\u00e1ticos, como EE.UU., el Reino Unido, Brasil, Australia y los Pa\u00edses Bajos, la IA se utiliza para vigilancia policial<\/a>vigilancia y toma de decisiones en materia de bienestar<\/a>.\u00a0<\/span><\/p>\n

    \u00bfPodr\u00eda una IA estar prediciendo aspectos de tu futuro ahora mismo? Sin duda, es posible.<\/p>","protected":false},"excerpt":{"rendered":"

    Investigadores de la Universidad de Berkeley (California) desarrollaron un sistema de previsi\u00f3n artificial para predecir acontecimientos futuros con una precisi\u00f3n similar a la de la sabidur\u00eda popular humana. Dado que los LLM no est\u00e1n dise\u00f1ados espec\u00edficamente para predecir acontecimientos, el equipo cre\u00f3 un sistema de predicci\u00f3n basado en GPT-4 utilizando un m\u00e9todo novedoso denominado razonamiento aumentado por recuperaci\u00f3n. Este proceso de varios pasos consiste en entrenar a GPT-4 para que busque informaci\u00f3n pertinente, eval\u00fae su relevancia y la integre en su proceso de razonamiento antes de realizar una predicci\u00f3n. El funcionamiento es el siguiente Recuperaci\u00f3n: El sistema de IA utiliza GPT-4 para generar consultas de b\u00fasqueda basadas en la pregunta y las subpreguntas de la predicci\u00f3n, recuperando un amplio conjunto de informaci\u00f3n potencialmente relevante.<\/p>","protected":false},"author":2,"featured_media":11237,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[105],"class_list":["post-11235","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-machine-learning"],"yoast_head":"\nBerkeley researchers build AI forecasting system that bests humans | 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\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Berkeley researchers build AI forecasting system that bests humans | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at the University of Berkeley, California, developed an AI forecasting system to predict future events with similar accuracy to human crowd wisdom.\u00a0 As LLMs are not purpose-built for event forecasting, the team built a forecasting system on top of GPT-4 using a novel approach called retrieval-augmented reasoning. This multi-step process involved training GPT-4 to search for pertinent information, assess its relevance, and integrate it into its reasoning process before making a prediction.\u00a0 Here\u2019s how it works: Retrieval: The AI system uses GPT-4 to generate search queries based on the forecasting question and sub-questions, retrieving a broad set of potentially\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/es\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-04-03T14:29:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-03-15.16.51-Craft-a-landscape-image-that-focuses-more-on-the-concept-of-graphs-and-forecasting-rather-than-buildings-set-against-a-futuristic-night-sky.-Imagine-.webp\" \/>\n\t<meta property=\"og:image:width\" 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