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Se robotarna spela mot varandra

Google Deepmind tränar sina robotar – med hjälp av pingis

Forskare på Google Deepmind har utvecklat ett system där robotar lär sig spela bordtennis genom att utmana varandra, helt utan mänsklig inblandning. Det skriver Google Deepminds Pannag Sanketi i en gästartikel i teknikmagasinet IEEE Spectrum.

Syftet är att skapa robotar som själva kan förbättra sina färdigheter i komplexa miljöer, något som enligt Sanketi krävs för att de ska kunna användas i verkliga miljöer som fabriker eller hem.

Robotarna tränas först i att samarbeta i lugna bollar, men genom att sedan spela mot varandra tvingas de utveckla nya strategier. Målet är att robotar på sikt ska kunna lära sig nya färdigheter utan att behöva programmeras manuellt varje gång.

 
Google Deepmind
Wikipedia (en)
DeepMind Technologies Limited, trading as Google DeepMind or simply DeepMind, is a British–American artificial intelligence research laboratory which serves as a subsidiary of Alphabet Inc. Founded in the UK in 2010, it was acquired by Google in 2014 and merged with Google AI's Google Brain division to become Google DeepMind in April 2023. The company is headquartered in London, with research centres in the United States, Canada, France, Germany, and Switzerland. In 2014, DeepMind introduced neural Turing machines (neural networks that can access external memory like a conventional Turing machine). The company has created many neural network models trained with reinforcement learning to play video games and board games. It made headlines in 2016 after its AlphaGo program beat Lee Sedol, a Go world champion, in a five-game match, which was later featured in the documentary AlphaGo. A more general program, AlphaZero, beat the most powerful programs playing go, chess and shogi (Japanese chess) after a few days of play against itself using reinforcement learning. DeepMind has since trained models for game-playing (MuZero, AlphaStar), for geometry (AlphaGeometry), and for algorithm discovery (AlphaEvolve, AlphaDev, AlphaTensor). In 2020, DeepMind made significant advances in the problem of protein folding with AlphaFold, which achieved state of the art records on benchmark tests for protein folding prediction. In July 2022, it was announced that over 200 million predicted protein structures, representing virtually all known proteins, would be released on the AlphaFold database. Google DeepMind has become responsible for the development of Gemini (Google's family of large language models) and other generative AI tools, such as the text-to-image model Imagen and the text-to-video model Veo.
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