The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence.
Author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was chorus of critics to say, 'that's not thinking'." AI researcher Rodney Brooks complains "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.'"
As soon as AI successfully solves a problem, the problem is no longer a part of AI.
Pamela McCorduck calls it an "odd paradox," that "practical AI successes, computational programs that actually achieved intelligent behavior, were soon assimilated into whatever application domain they were found to be useful in, and became silent partners alongside other problem-solving approaches, which left AI researchers to deal only with the "failures," the tough nuts that couldn't yet be cracked."
When IBM's chess playing computer Deep Blue succeeded in defeating Garry Kasparov in 1997, people complained that it had only used "brute force methods" and it wasn't real intelligence.Fred Reed writes "A problem that proponents of AI regularly face is this: When we know how a machine does something 'intelligent,' it ceases to be regarded as intelligent. If I beat the world's chess champion, I'd be regarded as highly bright."
Douglas Hofstadter expresses the AI effect concisely by quoting Tesler's Theorem: "AI is whatever hasn't been done yet."
When problems have not yet been formalised, they can still be characterised by a model of computation that includes human computation. The computational burden of a problem is split between a computer and a human: one part is solved by computer and the other part solved by human. This formalisation is referred to as human-assisted Turing machine.