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by Inufu
1947 days ago
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The author keeps referring to the "PSPACE" complexity of chess and Go in the context of AlphaGo; this is incorrect - these games are only PSPACE for arbitrary large board size N, at fixed board size as actually used by humans and current AI they are just constant O(1), complexity class is not relevant for this. The article was also written before the best evidence we currently have for this was published: scaling laws for natural language understanding (https://arxiv.org/abs/2001.08361), performance of RL algorithms with respect to data (eg AlphaGo Elo vs training time), image model accuracies, etc all show that exponentially increasing amounts of data/computation are required for linear improvements in performance. I posted some graphs with more details here: http://www.furidamu.org/blog/2020/05/03/the-case-against-the... tl;dr: current evidence suggests AI performance scales with log of data or computation |
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