{"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\/it\/blog\/novosti-interneta\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","title":{"rendered":"Rete neurale in vetro. Non richiede alimentazione, riconosce i numeri.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Rete
\n
\nTutti siamo familiari con la capacit\u00e0 delle reti neurali di riconoscere il testo scritto a mano. I fondamenti di questa tecnologia esistono da molti anni, ma solo di recente, un salto nelle capacit\u00e0 di calcolo e nell'elaborazione parallela dei dati ha reso possibile trasformare questa tecnologia in una soluzione molto pratica. Tuttavia, questa soluzione pratica sar\u00e0 essenzialmente rappresentata da un computer digitale che modifica continuamente i bit, proprio come per l'esecuzione di qualsiasi altro programma. Ma nel caso della rete neurale sviluppata dai ricercatori delle universit\u00e0 del Wisconsin, MIT e Columbia, la situazione \u00e8 diversa. Essi hanno creato un pannello di vetro che non richiede alimentazione propria, ma \u00e8 in grado di riconoscere i numeri scritti a mano.<\/a><\/noindex>.
\n<\/a><\/noindex>
\nQuesto vetro contiene inclusioni accuratamente posizionate, come bolle d'aria, impurit\u00e0 di grafene e altri materiali. Quando la luce colpisce il vetro, si generano complessi schemi d'onda, a seguito dei quali in una delle dieci aree la luce diventa pi\u00f9 intensa. Ognuna di queste aree corrisponde a un numero specifico. Ad esempio, qui ci sono due esempi che mostrano come si propaga la luce nel riconoscere il numero \"due\".<\/p>\n

\"Rete
\n
\nCon un set di addestramento di 5000 immagini, la rete neurale \u00e8 in grado di riconoscere correttamente il 79% delle 1000 immagini in ingresso. Il team crede di poter migliorare il risultato se riuscissero a superare i limiti imposti dal processo di produzione del vetro. Hanno iniziato con un design molto limitato del dispositivo per ottenere un prototipo funzionante. Successivamente, pianificano di continuare a esplorare vari modi per migliorare la qualit\u00e0 del riconoscimento, cercando di non complicare eccessivamente la tecnologia per poterla utilizzare in produzione. Il team ha anche in programma di creare una rete neurale tridimensionale nel vetro.
\n
Fonte: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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