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Complexity no Bar to AI · Gwern.net

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Critics of AI risk suggest diminishing returns to computing (formalized asymptotically) means AI will be weak; this argument relies on a large number of questionable premises and ignoring additional resources, constant factors, and nonlinear returns to small intelligence advantages, and is highly unlikely. Com­pu­ta­tional com­plex­ity the­ory de­scribes the steep in­crease in com­put­ing power re­quired for many al­go­rithms to solve larger prob­lems; fre­quently, the in­crease is large enough to ren­der prob­lems a few times larger to­tally in­tractable. Many of these al­go­rithms are used in AI-relevant con­texts. It has been ar­gued that this im­plies that AIs will fun­da­men­tally be lim­ited in ac­com­plish­ing real-world tasks bet­ter than hu­mans be­cause they will run into the same com­pu­ta­tional com­plex­ity limit as hu­mans, and so the con­se­quences of de­vel­op­ing AI will be small, as it is im­pos­si­ble for there to be any large fast global changes due to human or supe

--- title: Complexity no Bar to AI description: Critics of AI risk suggest diminishing returns to computing (formalized asymptotically) means AI will be weak; this argument relies on a large number of questionable premises and ignoring additional resources, constant factors, and nonlinear returns to small intelligence advantages, and is highly unlikely. created: 2014-06-01 modified: 2019-06-09 status: finished confidence: likely importance: 10 css-extension: dropcaps-kanzlei ... > Computational complexity theory describes the steep increase in computing power required for many algorithms to so

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