{"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\/pt\/blog\/news\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","title":{"rendered":"Rede neural em vidro. N\u00e3o requer alimenta\u00e7\u00e3o el\u00e9trica, reconhece n\u00fameros.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Rede
\n
\nTodos n\u00f3s estamos familiarizados com a habilidade das redes neurais de reconhecer texto manuscrito. Os fundamentos dessa tecnologia existem h\u00e1 muitos anos, mas, apenas recentemente, um salto nas capacidades computacionais e no processamento paralelo de dados permitiu tornar essa tecnologia uma solu\u00e7\u00e3o muito pr\u00e1tica. No entanto, essa solu\u00e7\u00e3o pr\u00e1tica ser\u00e1, em sua ess\u00eancia, apresentada na forma de um computador digital que altera bits repetidamente, da mesma forma que ao executar qualquer outro programa. Mas, no caso da rede neural desenvolvida por pesquisadores das universidades de Wisconsin, MIT e Columbia, a situa\u00e7\u00e3o \u00e9 diferente. Eles criaram um painel de vidro que n\u00e3o requer fonte de alimenta\u00e7\u00e3o pr\u00f3pria, mas que \u00e9 capaz de reconhecer n\u00fameros manuscritos<\/a><\/noindex>.
\n<\/a><\/noindex>
\nEsse vidro cont\u00e9m inclus\u00f5es precisamente posicionadas, como bolhas de ar, impurezas de grafeno e outros materiais. Quando a luz incide sobre o vidro, padr\u00f5es de onda complexos se formam, resultando em uma das dez \u00e1reas se tornando mais intensa. Cada uma dessas \u00e1reas corresponde a um n\u00famero. Por exemplo, abaixo est\u00e3o dois exemplos mostrando como a luz se propaga ao reconhecer o n\u00famero \"dois\".<\/p>\n

\"Rede
\n
\nCom um conjunto de treinamento de 5000 imagens, a rede neural \u00e9 capaz de reconhecer corretamente 79% das 1000 imagens de entrada. A equipe acredita que poderia melhorar o resultado se conseguisse contornar as limita\u00e7\u00f5es impostas pelo processo de fabrica\u00e7\u00e3o do vidro. Eles come\u00e7aram com um design de dispositivo muito limitado para obter um prot\u00f3tipo funcional. Em seguida, planejam continuar explorando v\u00e1rias maneiras de aumentar a precis\u00e3o de reconhecimento, ao mesmo tempo em que tentam n\u00e3o complicar excessivamente a tecnologia para que ela possa ser posteriormente utilizada na produ\u00e7\u00e3o. A equipe tamb\u00e9m tem planos de criar uma rede neural tridimensional no vidro.
\n
Fonte: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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