{"id":7939,"date":"2024-07-02T09:16:57","date_gmt":"2024-07-02T07:16:57","guid":{"rendered":"http:\/\/nextbrain.ai\/?p=7939"},"modified":"2024-07-02T09:16:59","modified_gmt":"2024-07-02T07:16:59","slug":"the-future-of-ai-liquid-networks-merging-artificial-intelligence-and-robotics","status":"publish","type":"post","link":"https:\/\/nextbrain.ai\/es\/blog\/the-future-of-ai-liquid-networks-merging-artificial-intelligence-and-robotics","title":{"rendered":"El futuro de la IA: redes l\u00edquidas que fusionan la inteligencia artificial y la rob\u00f3tica"},"content":{"rendered":"
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Hoy en d\u00eda, la inteligencia artificial y la rob\u00f3tica operan en gran medida como campos separados.<\/p>

Aunque la IA nos ha impresionado con sus capacidades de toma de decisiones y aprendizaje, sigue estando dentro de las computadoras.<\/p>

Los robots, por otro lado, tienen una presencia tangible y pueden ejecutar tareas preprogramadas, pero carecen de inteligencia.<\/p>

Sin embargo, esta divisi\u00f3n se est\u00e1 disolviendo gradualmente. La IA est\u00e1 pasando del mundo digital al din\u00e1mico espacio f\u00edsico 3D, lo que conduce a un nuevo paradigma conocido como inteligencia f\u00edsica<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Entendiendo la inteligencia f\u00edsica<\/h3>

La inteligencia f\u00edsica integra la capacidad de la IA para comprender texto, im\u00e1genes y otros datos en l\u00ednea con m\u00e1quinas del mundo real, aumentando su rendimiento. Esta evoluci\u00f3n significa que la IA ya no se quedar\u00e1 en nuestras computadoras, sino que interactuar\u00e1 con nosotros en el mundo f\u00edsico de diferentes maneras. Imagina un supermercado donde los robots te ayudan con diversas tareas, como llevar cajas pesadas. Lograr esto requiere repensar la cognici\u00f3n, el dise\u00f1o y los procesos de aprendizaje de las m\u00e1quinas.<\/p>

El papel de las redes l\u00edquidas en la IA<\/h3>

Para que la inteligencia f\u00edsica sea efectiva, la IA debe operar en computadoras lo suficientemente peque\u00f1as como para caber en un robot. Los sistemas de IA actuales pueden cometer errores; por lo tanto, hay una necesidad de sistemas m\u00e1s confiables y compactos. Daniela Rus,<\/a> profesora en el Laboratorio de Ciencias de la Computaci\u00f3n y AI del MIT, junto con su equipo, ha sido pionera en un nuevo enfoque de IA llamado \u201credes l\u00edquidas.\u201d<\/strong><\/p>

Considera un coche aut\u00f3nomo, que utiliza decenas de miles de neuronas artificiales para navegar. Es un desaf\u00edo vincular las actividades de estos neuronas con el comportamiento del coche. Por otro lado, una soluci\u00f3n de red l\u00edquida utiliza solo 19 neuronas, enfoc\u00e1ndose en la carretera y los alrededores. Esta complejidad reducida permite a los investigadores entender mejor los procesos de toma de decisiones. Los sistemas de IA tradicionales utilizan neuronas artificiales como unidades b\u00e1sicas de encendido\/apagado, mientras que las neuronas l\u00edquidas emplean ecuaciones diferenciales para modelar los c\u00e1lculos neuronales de manera m\u00e1s precisa.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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La Ventaja de las Redes L\u00edquidas<\/h3>

Las redes l\u00edquidas no son est\u00e1ticas; contin\u00faan adapt\u00e1ndose despu\u00e9s de la implementaci\u00f3n en funci\u00f3n de nuevas entradas. Esta adaptabilidad, basada en modelos f\u00edsicos, ofrece una nueva forma de pensar para las m\u00e1quinas. Estas redes pueden funcionar en varios dispositivos, desde tel\u00e9fonos inteligentes hasta robots y computadoras empresariales, abriendo nuevas posibilidades para el dise\u00f1o y la funcionalidad de las m\u00e1quinas.<\/p>

Innovando con Texto-a-Robot y Imagen-a-Robot<\/h3>

El segundo aspecto cr\u00edtico de la inteligencia f\u00edsica implica transformar texto e im\u00e1genes en acciones rob\u00f3ticas. Las soluciones de IA actuales, que dependen de modelos estad\u00edsticos, no son suficientes en este sentido. Sin embargo, las redes l\u00edquidas permiten convertir texto e im\u00e1genes en robots funcionales, reduciendo dr\u00e1sticamente el tiempo y los recursos necesarios para la creaci\u00f3n de prototipos y la innovaci\u00f3n.<\/p>

Aprendizaje de Humano a Robot<\/h3>

La tercera dimensi\u00f3n de la inteligencia f\u00edsica es permitir que las m\u00e1quinas aprendan del comportamiento humano. Daniela Rus y su equipo han desarrollado un sistema donde los humanos est\u00e1n instrumentados con sensores en un entorno de cocina. Al recopilar datos completos sobre las tareas humanas\u2014como el movimiento muscular, la postura y la mirada\u2014entrenan a la IA para ense\u00f1ar a los robots estas tareas. Este enfoque conduce a robots que pueden moverse con agilidad y adaptarse a diversas tareas, desde la preparaci\u00f3n de alimentos hasta la limpieza.<\/p>

Conclusi\u00f3n<\/h3>

La fusi\u00f3n de la IA y la rob\u00f3tica a trav\u00e9s de redes l\u00edquidas conduce a enormes avances en la inteligencia f\u00edsica.<\/p>

Esta tecnolog\u00eda promete dar vida a nuestras ideas m\u00e1s imaginativas, creando m\u00e1quinas inteligentes que pueden interactuar sin problemas con el mundo f\u00edsico.<\/p>

En NextBrain IA<\/a>, seguimos las \u00faltimas innovaciones en IA para ayudarte a extraer valiosos conocimientos empresariales y estrat\u00e9gicos de tus datos. Programa una demostraci\u00f3n hoy<\/a> para ver el poder de primera mano.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Nowadays artificial intelligence and robotics operate as largely separate fields. While AI has impressed us with its decision-making and learning capabilities, it remains within computers. 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