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Li, Yujia; Choi, David; Chung, Junyoung; Kushman, Nate; Schrittwieser, Julian; Leblond, Rémi; Eccles, Tom; Keeling, James; Gimeno, Felix; Dal Lago, Agustin; Hubert, Thomas; Choy, Peter; de Masson d'Autume, Cyprien; Babuschkin, Igor; Chen, Xinyun; Huang, Po-Sen; Welbl, Johannes; Gowal, Sven; Cherepanov, Alexey; Molloy, James; Mankowitz, Daniel J; Sutherland Robson, Esme; Kohli, Pushmeet; de Freitas, Nando; Kavukcuoglu, Koray; Vinyals, Oriol
Science (American Association for the Advancement of Science), 12/2022, Letnik: 378, Številka: 6624Journal Article
Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform poorly on more complex tasks requiring problem-solving skills, such as competitive programming problems. Here, we introduce AlphaCode, a system for code generation that achieved an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform. AlphaCode solves problems by generating millions of diverse programs using specially trained transformer-based networks and then filtering and clustering those programs to a maximum of just 10 submissions. This result marks the first time an artificial intelligence system has performed competitively in programming competitions.
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Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Vir: Osebne bibliografije
in: SICRIS
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