{"id":36249,"date":"2019-10-31T22:10:29","date_gmt":"2019-10-31T19:10:29","guid":{"rendered":"https:\/\/prohoster.info\/blog\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry\/"},"modified":"2019-10-31T22:10:29","modified_gmt":"2019-10-31T19:10:29","slug":"nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","status":"publish","type":"post","link":"https:\/\/prohoster.info\/es\/blog\/news\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","title":{"rendered":"Red neuronal en vidrio. No requiere alimentaci\u00f3n el\u00e9ctrica, reconoce n\u00fameros","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Red
\n
\nTodos estamos familiarizados con la capacidad de las redes neuronales para reconocer escritura a mano. Los fundamentos de esta tecnolog\u00eda existen desde hace muchos a\u00f1os, pero solo recientemente, un avance en la potencia inform\u00e1tica y el procesamiento paralelo de datos ha permitido transformar esta tecnolog\u00eda en una soluci\u00f3n muy pr\u00e1ctica. Sin embargo, esta soluci\u00f3n pr\u00e1ctica, en esencia, ser\u00e1 presentada en forma de una computadora digital que cambia repetidamente bits, al igual que cuando se ejecuta cualquier otro programa. Pero en el caso de la red neuronal desarrollada por investigadores de las universidades de Wisconsin, MIT y Columbia, la situaci\u00f3n es diferente. Ellos crearon un panel de vidrio que no requiere alimentaci\u00f3n el\u00e9ctrica propia, pero que puede reconocer n\u00fameros escritos a mano.<\/a><\/noindex>.
\n<\/a><\/noindex>
\nEste vidrio contiene inclusiones colocadas con precisi\u00f3n, como burbujas de aire, impurezas de grafeno y otros materiales. Cuando la luz incide en el vidrio, se generan patrones de ondas complejos, lo que resulta en que en una de diez \u00e1reas la luz se vuelve m\u00e1s intensa. Cada una de estas \u00e1reas corresponde a un n\u00famero. Por ejemplo, a continuaci\u00f3n se muestran dos ejemplos que ilustran c\u00f3mo se propaga la luz al reconocer el n\u00famero \"dos\".<\/p>\n

\"Red
\n
\nCon un conjunto de entrenamiento de 5000 im\u00e1genes, la red neuronal puede reconocer correctamente el 79% de 1000 im\u00e1genes de entrada. El equipo cree que podr\u00edan mejorar el resultado si pudieran sortear las limitaciones impuestas por el proceso de fabricaci\u00f3n del vidrio. Comenzaron con un dise\u00f1o de dispositivo muy limitado para obtener un prototipo funcional. Luego planean continuar explorando diferentes formas de mejorar la calidad del reconocimiento, asegur\u00e1ndose de no complicar demasiado la tecnolog\u00eda, para que luego pueda ser utilizada en la producci\u00f3n. El equipo tambi\u00e9n tiene planes de crear una red neuronal tridimensional dentro del vidrio.
\n
Fuente: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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