{"id":11187,"date":"2024-04-02T16:26:13","date_gmt":"2024-04-02T16:26:13","guid":{"rendered":"https:\/\/dailyai.com\/?p=11187"},"modified":"2024-04-04T05:59:53","modified_gmt":"2024-04-04T05:59:53","slug":"deepmind-develop-a-foundation-model-for-building-2d-game-environments","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/04\/deepmind-develop-a-foundation-model-for-building-2d-game-environments\/","title":{"rendered":"DeepMind desarrolla un modelo b\u00e1sico para construir entornos de juego en 2D"},"content":{"rendered":"

Genie, de Google DeepMind, es un modelo generativo que traduce simples im\u00e1genes o mensajes de texto en mundos din\u00e1micos e interactivos.\u00a0<\/b><\/p>\n

Genie se entren\u00f3 con un extenso conjunto de datos de m\u00e1s de 200.000 horas de secuencias de v\u00eddeo de juegos, incluidos juegos de plataformas en 2D e interacciones rob\u00f3ticas en el mundo real.\u00a0<\/span><\/p>\n

Este vasto conjunto de datos permiti\u00f3 a Genie comprender y generar la f\u00edsica, la din\u00e1mica y la est\u00e9tica de numerosos entornos y objetos.<\/span><\/p>\n

El modelo final, documentado en un <\/span>trabajo de investigaci\u00f3n<\/span><\/a>contiene 11.000 millones de par\u00e1metros para generar mundos virtuales interactivos a partir de im\u00e1genes en m\u00faltiples formatos o mensajes de texto.\u00a0<\/span><\/p>\n

As\u00ed, puedes dar a Genie una imagen de tu sal\u00f3n o jard\u00edn y convertirla en un nivel de plataformas 2D jugable.<\/p>\n

O garabatear un entorno 2D en un trozo de papel y convertirlo en un entorno jugable.<\/p>\n

\"DeepMind
Genie puede funcionar como un entorno interactivo, aceptando diversas indicaciones, como im\u00e1genes generadas o bocetos dibujados a mano. Los usuarios pueden guiar el resultado del modelo proporcionando acciones latentes en cada paso temporal, que Genie utiliza para generar el siguiente fotograma de la secuencia a 1 FPS. Fuente: DeepMind v\u00eda ArXiv (acceso abierto).<\/figcaption><\/figure>\n

Lo que diferencia a Genie de otros modelos de mundo es su capacidad para permitir a los usuarios interactuar con los entornos generados fotograma a fotograma.<\/span><\/p>\n

Por ejemplo, a continuaci\u00f3n puedes ver c\u00f3mo Genie toma fotograf\u00edas de entornos del mundo real y las convierte en niveles de juego en 2D.<\/p>\n

\"DeepMind
Genie puede crear niveles de juego a partir de a) otros niveles de juego, b) bocetos dibujados a mano y c) fotograf\u00edas de entornos del mundo real. Mira los niveles de juego (fila inferior) generados a partir de im\u00e1genes del mundo real (fila superior). Fuente: DeepMind.<\/figcaption><\/figure>\n

C\u00f3mo funciona Genie<\/h2>\n

Genie es un \"modelo de mundo fundacional\" con tres componentes clave: un tokenizador espaciotemporal de v\u00eddeo, un modelo de din\u00e1mica autorregresiva y un modelo de acci\u00f3n latente (LAM) sencillo y escalable.<\/span><\/p>\n

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

    \n
  1. Transformadores espaciotemporales<\/b>: Un elemento central de Genie son los transformadores espaciotemporales (ST), que procesan secuencias de fotogramas de v\u00eddeo. A diferencia de los transformadores tradicionales que manejan texto o im\u00e1genes est\u00e1ticas, los transformadores ST est\u00e1n dise\u00f1ados para comprender la progresi\u00f3n de los datos visuales a lo largo del tiempo, lo que los hace ideales para la generaci\u00f3n de v\u00eddeo y entornos din\u00e1micos.<\/span><\/li>\n
  2. Modelo de acci\u00f3n latente (MLA)<\/b>: Genie comprende y predice acciones dentro de sus mundos generados a trav\u00e9s del LAM. Este infiere las acciones potenciales que podr\u00edan ocurrir entre los fotogramas de un v\u00eddeo, aprendiendo un conjunto de \"acciones latentes\" directamente de los datos visuales. Esto permite a Genie controlar la progresi\u00f3n de los acontecimientos en entornos interactivos, a pesar de la ausencia de etiquetas de acci\u00f3n expl\u00edcitas en los datos de entrenamiento.<\/span><\/li>\n
  3. Tokenizador de v\u00eddeo y modelo din\u00e1mico<\/b>: Para gestionar los datos de v\u00eddeo, Genie emplea un tokenizador de v\u00eddeo que comprime los fotogramas de v\u00eddeo en bruto en un formato m\u00e1s manejable de tokens discretos. Tras la tokenizaci\u00f3n, el modelo din\u00e1mico predice el siguiente conjunto de tokens de fotogramas, generando fotogramas posteriores en el entorno interactivo.<\/span><\/li>\n<\/ol>\n

    El equipo de DeepMind explic\u00f3 lo siguiente sobre Genie: \"Genie podr\u00eda permitir a un gran n\u00famero de personas generar sus propias experiencias de juego.<\/span> Esto podr\u00eda ser positivo para quienes deseen expresar su creatividad de una forma nueva, por ejemplo, los ni\u00f1os, que podr\u00edan dise\u00f1ar y adentrarse en sus propios mundos imaginados\".<\/span><\/p>\n

    En un experimento paralelo, cuando se le presentaron v\u00eddeos de brazos rob\u00f3ticos reales interactuando con objetos del mundo real, Genie demostr\u00f3 una asombrosa capacidad para descifrar las acciones que estos brazos pod\u00edan realizar. Esto demuestra su potencial para la investigaci\u00f3n rob\u00f3tica.\u00a0<\/span><\/p>\n

    Tim Rockt\u00e4schel, del equipo de Genie, describi\u00f3 el potencial abierto de Genie: \"Es dif\u00edcil predecir qu\u00e9 casos de uso ser\u00e1n posibles. <\/span>Esperamos que proyectos como Genie acaben proporcionando a la gente nuevas herramientas para expresar su creatividad\".\u00a0<\/span><\/p>\n

    DeepMind era consciente de los riesgos de divulgar este modelo de cimentaci\u00f3n, por lo que afirmaba en el documento: \"Hemos optado por no divulgar los puntos de control del modelo entrenado, el conjunto de datos de entrenamiento del modelo o ejemplos de esos datos para acompa\u00f1ar este documento o el sitio web.\"<\/p>\n

    \"Nos gustar\u00eda tener la oportunidad de seguir colaborando con la comunidad investigadora (y de videojuegos) y garantizar que cualquier futuro lanzamiento de este tipo sea respetuoso, seguro y responsable\".<\/p>\n

    Utilizar juegos para simular aplicaciones del mundo real<\/h2>\n

    DeepMind ha utilizado videojuegos para varios proyectos de aprendizaje autom\u00e1tico.\u00a0<\/span><\/p>\n

    Por ejemplo, en 2021, DeepMind construy\u00f3 XLand<\/a>un campo de juego virtual para probar enfoques de aprendizaje por refuerzo (RL) para agentes de IA generalistas. Aqu\u00ed, los modelos de IA dominaban la cooperaci\u00f3n y la resoluci\u00f3n de problemas realizando tareas como mover obst\u00e1culos en entornos de juego abiertos.\u00a0<\/span><\/p>\n

    Luego, el mes pasado, SIMA<\/a> (Scalable, Instructable, Multiworld Agent) fue dise\u00f1ado para comprender y ejecutar instrucciones en lenguaje humano a trav\u00e9s de diferentes juegos y escenarios.\u00a0<\/span><\/p>\n

    SIMA se entren\u00f3 con nueve videojuegos que requer\u00edan distintas habilidades, desde la navegaci\u00f3n b\u00e1sica hasta el pilotaje de veh\u00edculos.\u00a0<\/span><\/p>\n

    Los entornos de juego ofrecen una caja de arena controlable y escalable para entrenar y probar modelos de IA.<\/p>\n

    La experiencia de DeepMind en juegos se remonta a 2014-2015, cuando desarrollaron un algoritmo para derrotar a humanos en juegos como Pong y Space Invaders, por no hablar de AlphaGo, que derrot\u00f3 al jugador profesional Fan Hui en un tablero de 19\u00d719 a tama\u00f1o real.<\/p>","protected":false},"excerpt":{"rendered":"

    Genie, de Google DeepMind, es un modelo generativo que traduce im\u00e1genes sencillas o mensajes de texto en mundos din\u00e1micos e interactivos. Genie se entren\u00f3 con un extenso conjunto de datos de m\u00e1s de 200.000 horas de secuencias de v\u00eddeo de videojuegos, incluidos juegos de plataformas en 2D e interacciones rob\u00f3ticas en el mundo real. Este vasto conjunto de datos permiti\u00f3 a Genie comprender y generar la f\u00edsica, la din\u00e1mica y la est\u00e9tica de numerosos entornos y objetos. El modelo final, documentado en un trabajo de investigaci\u00f3n, contiene 11.000 millones de par\u00e1metros para generar mundos virtuales interactivos a partir de im\u00e1genes en m\u00faltiples formatos o de mensajes de texto. Por ejemplo, Genie puede mostrarle una imagen de su sal\u00f3n o de su jard\u00edn.<\/p>","protected":false},"author":2,"featured_media":11190,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,169],"class_list":["post-11187","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-robotics"],"yoast_head":"\nDeepMind develop a foundation model for building 2D game environments | DailyAI<\/title>\n<meta name=\"description\" content=\"Google DeepMind's Genie is a generative model that translates simple images or text prompts into dynamic, interactive worlds.\u00a0\" \/>\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\/deepmind-develop-a-foundation-model-for-building-2d-game-environments\/\" 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