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Prior to the development of AlphaTensor, one of the main challenges in discovering new algorithms was the vast number of possibilities to consider & there are often an enormous number of potential algorithms that could be developed to solve a given problem. This makes it difficult for humans to identify the most efficient or effective algorithms through traditional methods of exploration and trial and error.

How does the use of a self-played game by AlphaTensor, an artificial intelligence system developed by DeepMind, enable the discovery of novel and efficient algorithms for matrix multiplication and how does the AI's performance in the game, as measured by the number of steps taken to zero out a 3D tensor, correspond to the efficiency of the generated algorithm?

DeepMind paper on AlphaTensor: Discovering faster matrix multiplication algorithms with reinforcement learning

nbro
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Faizy
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  • Given that you're linking to the paper, I suppose that you're looking for an answer that gives you an overview of the idea presented in the paper. Your second question seems to be more specific, so it seems that you already read the paper or are particularly confused about that part. – nbro Dec 19 '22 at 09:50

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