{"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\/pl\/blog\/news\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","title":{"rendered":"Sztuczna inteligencja w szkle. Nie wymaga zasilania, rozpoznaje cyfry","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Sztuczna
\n
\nWszyscy znamy zdolno\u015b\u0107 sieci neuronowych do rozpoznawania pisma r\u0119cznego. Podstawy tej technologii istniej\u0105 ju\u017c od wielu lat, jednak dopiero niedawno skok w obszarze mocy obliczeniowej i r\u00f3wnoleg\u0142ego przetwarzania danych pozwoli\u0142 przekszta\u0142ci\u0107 t\u0119 technologi\u0119 w bardzo praktyczne rozwi\u0105zanie. Niemniej jednak, to praktyczne rozwi\u0105zanie jest w zasadzie reprezentowane przez cyfrowy komputer, kt\u00f3ry wielokrotnie zmienia bity, dok\u0142adnie tak samo, jak w przypadku ka\u017cdej innej aplikacji. Jednak w przypadku sieci neuronowej opracowanej przez badaczy z uniwersytet\u00f3w Wisconsin, MIT i Columbia, sytuacja wygl\u0105da inaczej. Oni stworzyli szklan\u0105 p\u0142yt\u0119, kt\u00f3ra nie wymaga zasilania, a mimo to potrafi rozpoznawa\u0107 pisemne cyfry.<\/a><\/noindex>.
\n<\/a><\/noindex>
\nTo szk\u0142o zawiera precyzyjnie umiejscowione wtr\u0105cenia, takie jak p\u0119cherzyki powietrza, domieszki grafenu i innych materia\u0142\u00f3w. Gdy \u015bwiat\u0142o pada na szk\u0142o, powstaj\u0105 z\u0142o\u017cone wzory falowe, w wyniku kt\u00f3rych w jednej z dziesi\u0119ciu stref \u015bwiat\u0142o staje si\u0119 bardziej intensywne. Ka\u017cda z tych stref odpowiada okre\u015blonemu numerowi. Na przyk\u0142ad poni\u017cej przedstawiono dwa przyk\u0142ady, kt\u00f3re pokazuj\u0105, jak \u015bwiat\u0142o rozchodzi si\u0119 podczas rozpoznawania liczby \u201edwa\u201d.<\/p>\n

\"Sztuczna
\n
\nPrzy zestawie szkoleniowym sk\u0142adaj\u0105cym si\u0119 z 5000 obraz\u00f3w, sie\u0107 neuronowa jest w stanie poprawnie rozpozna\u0107 79% z 1000 wej\u015bciowych obraz\u00f3w. Zesp\u00f3\u0142 uwa\u017ca, \u017ce mogliby poprawi\u0107 wyniki, gdyby uda\u0142o im si\u0119 obej\u015b\u0107 ograniczenia wynikaj\u0105ce z procesu produkcji szk\u0142a. Zacz\u0119li od bardzo ograniczonego projektu urz\u0105dzenia, aby uzyska\u0107 dzia\u0142aj\u0105cy prototyp. Nast\u0119pnie planuj\u0105 kontynuowa\u0107 badania nad r\u00f3\u017cnymi sposobami poprawy jako\u015bci rozpoznawania, staraj\u0105c si\u0119 jednocze\u015bnie nie komplikowa\u0107 nadmiernie technologii, aby mo\u017cna j\u0105 by\u0142o wykorzysta\u0107 w produkcji. Zesp\u00f3\u0142 ma tak\u017ce plany dotycz\u0105ce stworzenia tr\u00f3jwymiarowej sieci neuronowej w szkle.
\n
\u0179r\u00f3d\u0142o: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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