{"id":103181,"date":"2022-02-03T09:36:41","date_gmt":"2022-02-03T07:36:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/deepmind-predstavil-sistemu-mashinnogo-obucheniya-dlya-generaczii-koda-po-tekstovomu-opisaniyu-zadachi"},"modified":"2022-02-03T09:36:41","modified_gmt":"2022-02-03T07:36:42","slug":"deepmind-predstavil-sistemu-mashinnogo-obucheniya-dlya-generaczii-koda-po-tekstovomu-opisaniyu-zadachi","status":"publish","type":"post","link":"https:\/\/prohoster.info\/es\/blog\/news\/deepmind-predstavil-sistemu-mashinnogo-obucheniya-dlya-generaczii-koda-po-tekstovomu-opisaniyu-zadachi","title":{"rendered":"DeepMind ha presentado un sistema de aprendizaje autom\u00e1tico para generar c\u00f3digo a partir de descripciones textuales de tareas.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

La empresa DeepMind, conocida por sus desarrollos en inteligencia artificial y la construcci\u00f3n de redes neuronales capaces de jugar videojuegos y juegos de mesa a nivel humano, present\u00f3 el proyecto AlphaCode, que desarrolla un sistema de aprendizaje autom\u00e1tico para la generaci\u00f3n de c\u00f3digo, capaz de participar en competiciones de programaci\u00f3n en la plataforma Codeforces y mostrar resultados promedio. Una de las caracter\u00edsticas clave del desarrollo es la capacidad de generar c\u00f3digo en lenguajes Python o C++, tomando como entrada texto en ingl\u00e9s que describe el problema. <\/p>\n

Para probar el sistema, se seleccionaron 10 nuevas competiciones de Codeforces con m\u00e1s de 5000 participantes, realizadas despu\u00e9s de que finalizara el entrenamiento del modelo de aprendizaje autom\u00e1tico. Los resultados de la resoluci\u00f3n de problemas permitieron que AlphaCode se ubicara aproximadamente a mitad de la tabla en dichas competiciones (54.3%). La calificaci\u00f3n total pronosticada para AlphaCode fue de 1238 puntos, lo que lo posiciona en el Top 28% de todos los participantes de Codeforces que han competido al menos una vez en los \u00faltimos 6 meses. Se observa que el proyecto a\u00fan se encuentra en una fase inicial de desarrollo y se planea mejorar la calidad del c\u00f3digo generado, as\u00ed como desarrollar AlphaCode hacia sistemas que ayuden en la escritura de c\u00f3digo o herramientas de desarrollo de aplicaciones que puedan ser utilizadas por personas sin habilidades de programaci\u00f3n. <\/p>\n

En el proyecto se utiliza la arquitectura de red neuronal \u00abTransformer\u00bb junto con m\u00e9todos de muestreo y filtrado que permiten generar diferentes variantes de c\u00f3digo impredecible, alineadas con el texto en lenguaje natural. Despu\u00e9s de la filtraci\u00f3n, la clustering y el ranking, se selecciona el c\u00f3digo de trabajo m\u00e1s \u00f3ptimo del flujo de variantes generado, el cual luego se verifica para asegurar que produce el resultado correcto (en cada tarea de la competici\u00f3n se proporciona un ejemplo de datos de entrada y el resultado correspondiente que debe ser obtenido tras la ejecuci\u00f3n del programa).

\"DeepMind<\/center> <\/p>\n

Para el entrenamiento inicial del sistema de aprendizaje autom\u00e1tico se utiliz\u00f3 una base de c\u00f3digo disponible en repositorios p\u00fablicos de GitHub. Despu\u00e9s de preparar el modelo inicial, se llev\u00f3 a cabo una fase de optimizaci\u00f3n, implementada sobre la base de una colecci\u00f3n de c\u00f3digo con ejemplos de problemas y soluciones ofrecidos a los participantes de competiciones como Codeforces, CodeChef, HackerEarth, AtCoder y Aizu. En total, se utilizaron 715 GB de c\u00f3digo de GitHub y m\u00e1s de un mill\u00f3n de ejemplos de soluciones a problemas t\u00edpicos de competiciones. Antes de pasar a la generaci\u00f3n del c\u00f3digo, el texto del problema pas\u00f3 por una fase de normalizaci\u00f3n, en la que se elimin\u00f3 todo lo innecesario y se conservaron solo las partes significativas.

\"DeepMind<\/center>
\n
Fuente: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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[…]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":103182,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-103181","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47DeepMind present\u00f3 un sistema de aprendizaje autom\u00e1tico para la generaci\u00f3n de c\u00f3digo a partir de la descripci\u00f3n textual del problema | ProHoster","description":"La empresa DeepMind, conocida por sus desarrollos en el campo de la inteligencia artificial y la construcci\u00f3n de redes neuronales capaces de jugar videojuegos y juegos de mesa a nivel humano, present\u00f3 un 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