AI Effect

:''For the magnitude of effect of a pesticide, see Pesticide application. Of change in farming practices, see Agricultural intensification.''

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 a chorus of critics to say, 'that's not thinking'." 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.'"

"The AI effect" is that line of thinking, the tendency to redefine AI to mean: "AI is anything that has not been done yet." This is the common public misperception, that as soon as AI successfully solves a problem, that solution method is no longer within the domain 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."

is:

"AI is whatever hasn't been done yet."

Larry Tesler

Douglas Hofstadter quotes this as do many other commentators.

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 a human. This formalisation is referred to as human-assisted Turing machine.

AI applications become mainstream

Software and algorithms developed by AI researchers are now integrated into many applications throughout the world, without really being called AI. This underappreciation is known from such diverse fields as computer chess, marketing, agricultural automation and hospitality.

Michael Swaine reports "AI advances are not trumpeted as artificial intelligence so much these days, but are often seen as advances in some other field". "AI has become more important as it has become less conspicuous", Patrick Winston says. "These days, it is hard to find a big system that does not work, in part, because of ideas developed or matured in the AI world."

According to Stottler Henke, "The great practical benefits of AI applications and even the existence of AI in many software products go largely unnoticed by many despite the already widespread use of AI techniques in software. This is the AI effect. Many marketing people don't use the term 'artificial intelligence' even when their company's products rely on some AI techniques. Why not?"

Marvin Minsky writes "This paradox resulted from the fact that whenever an AI research project made a useful new discovery, that product usually quickly spun off to form a new scientific or commercial specialty with its own distinctive name. These changes in name led outsiders to ask, Why do we see so little progress in the central field of artificial intelligence?"

Nick Bostrom observes that "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labelled AI anymore."

The AI effect on decision-making in supply chain risk management is a severely understudied area.

To avoid the AI effect problem, the editors of a special issue of ''IEEE Software'' on AI and software engineering recommend not overselling not hyping the real achievable results to start with.

Legacy of the AI winter

Many AI researchers find that they can get more funding and sell more software if they avoid the bad name of "artificial intelligence" and instead pretend their work has nothing to do with intelligence at all. This was especially true in the early 1990s, during the second "AI winter

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