{"id":103181,"date":"2022-02-03T09:36:41","date_gmt":"2022-02-03T07:36:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/deepmind-predstavil-sistemu-mashinnogo-obucheniya-dlya-generaczii-koda-po-tekstovomu-opisaniyu-zadachi"},"modified":"2022-02-03T09:36:41","modified_gmt":"2022-02-03T07:36:42","slug":"deepmind-predstavil-sistemu-mashinnogo-obucheniya-dlya-generaczii-koda-po-tekstovomu-opisaniyu-zadachi","status":"publish","type":"post","link":"https:\/\/prohoster.info\/da\/blog\/news\/deepmind-predstavil-sistemu-mashinnogo-obucheniya-dlya-generaczii-koda-po-tekstovomu-opisaniyu-zadachi","title":{"rendered":"DeepMind har pr\u00e6senteret et maskinl\u00e6ringssystem til generering af kode baseret p\u00e5 tekstbeskrivelser af opgaver.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

DeepMind, der har opn\u00e5et ber\u00f8mmelse for sine udviklinger inden for kunstig intelligens og opbygning af neurale netv\u00e6rk, der kan spille computer- og br\u00e6tspil p\u00e5 menneskeligt niveau, har pr\u00e6senteret projektet AlphaCode, som udvikler et maskinl\u00e6ringssystem til generering af kode, der kan deltage i programmeringskonkurrencer p\u00e5 platformen Codeforces og vise gennemsnitsresultater. En n\u00f8glefunktion ved udviklingen er evnen til at generere kode i Python eller C++, idet den modtager en opgavebeskrivelse p\u00e5 engelsk som input. <\/p>\n

Til testning af systemet blev 10 nye Codeforces-konkurrencer udvalgt med over 5000 deltagere, gennemf\u00f8rt efter afslutningen af tr\u00e6ningen af maskinl\u00e6ringsmodellen. Resultaterne af opgavernes udf\u00f8relse gjorde det muligt for AlphaCode at komme ind omkring midten af ranglisten for de n\u00e6vnte konkurrencer (54,3%). Den forudsigte samlede rating for AlphaCode var 1238 point, hvilket sikrer en placering i Top 28% blandt alle Codeforces-deltagere, der har deltaget i konkurrencer i l\u00f8bet af de sidste 6 m\u00e5neder. Det bem\u00e6rkes, at projektet stadig er i en tidlig udviklingsfase, og at der i fremtiden planl\u00e6gges at forbedre kvaliteten af den genererede kode samt udvikle AlphaCode i retning af systemer, der hj\u00e6lper med kodeudvikling, eller udviklingsv\u00e6rkt\u00f8jer, som personer uden programmeringsf\u00e6rdigheder kan bruge. <\/p>\n

Projektet anvender arkitekturen af neuralnetv\u00e6rket «Transformer» i kombination med sampling- og filtreringsteknikker, som g\u00f8r det muligt at generere forskellige uforudsigelige kodestrukturer, der svarer til teksten p\u00e5 naturligt sprog. Efter filtrering, klyngedannelse og rangordning sorteres den mest optimale arbejdskode fra det dannede str\u00f8mmodeller af muligheder, som derefter verificeres for at sikre, at det korrekte resultat opn\u00e5s (i hvert konkurrencem\u00e6ssigt opgave er der angivet et eksempel p\u00e5 inddata og det tilsvarende resultat, der skal opn\u00e5s efter programmets udf\u00f8relse).

\"DeepMind<\/center> <\/p>\n

Til grov tr\u00e6ning af maskinl\u00e6ringssystemet blev der anvendt en kodebase, der er tilg\u00e6ngelig i offentlige GitHub-repositorier. Efter forberedelsen af den indledende model blev der gennemf\u00f8rt en optimeringsfase baseret p\u00e5 en samling kode med eksempler p\u00e5 opgaver og l\u00f8sninger, der tilbydes til deltagerne i konkurrencerne Codeforces, CodeChef, HackerEarth, AtCoder og Aizu. I alt blev 715 GB kode fra GitHub og over en million eksempler p\u00e5 l\u00f8sninger af standardopgaver fra konkurrencerne anvendt til tr\u00e6ningen. F\u00f8r overgangen til kodegenerering gennemgik opgaveteksten en normaliseringsfase, hvor alt un\u00f8dvendigt blev udelukket, og kun betydningsfulde dele blev tilbageholdt.

\"DeepMind<\/center>
\n
Kilde: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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[…]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":103182,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-103181","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47DeepMind pr\u00e6senterede et maskinl\u00e6ringssystem til generering af kode baseret p\u00e5 en tekstbeskrivelse af opgaven | ProHoster","description":"Virksomheden DeepMind, der er blevet kendt for sine udviklinger inden for kunstig intelligens og opbygning af neurale netv\u00e6rk, der kan spille computer- og br\u00e6tspil p\u00e5 menneskelig niveau, har pr\u00e6senteret et 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