AI-complete

Describing a problem conjectured to be as hard as AI itself: a system able to solve it would need general, human-level intelligence, so solving it would amount to solving AI as a whole. The label borrows the shape of NP-completeNP-complete, adj.: Describes problems, such as solving giant Sudokus, where a proposed answer is quick to check but no known method finds one quickly; a fast method for any one of them could be turned into one for all. from complexity theory, but only informally — no accepted reduction shows that solving one such problem solves the others, though formalizations based on reductions to a human oracle have been proposed. Problems put forward as examples include open-ended natural-language understandingnatural language processing, n.: The field that teaches computers to handle human language — to read, interpret, translate, summarize and produce text and speech. It is one of the oldest and largest areas of artificial intelligence., human-quality machine translation, computer visioncomputer vision, n.: The part of AI that lets computers make sense of images and video: recognizing faces, reading signs, spotting tumors on scans or judging how far away a car is. in unconstrained scenes and the Winograd schema challengeWinograd schema challenge, prop. n.: A test of machine common sense built from pairs of sentences in which changing one word changes what a pronoun refers to — something people resolve instantly using everyday knowledge.. Its predictions have aged unevenly: language modelslarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code. now perform well on several tasks once labeled AI-complete, which some read as evidence of growing generality and others as evidence that the tasks were easier than claimed. CAPTCHAs deliberately exploit problems believed to be hard for machines.