The AI effect occurs when onlookers discount the behavior of an artificial intelligence program as not "real" intelligence.
The 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.'"
Definition
"The AI effect" refers to a phenomenon where either the definition of AI or the concept of intelligence is adjusted to exclude capabilities that AI systems have mastered. This often manifests as tasks that AI can now perform successfully no longer being considered part of AI, or as the notion of intelligence itself being redefined to exclude AI achievements. Edward Geist credits John McCarthy for coining the term "AI effect" to describe this phenomenon.
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." It is an example of moving the goalposts.
Tesler's Theorem is:
AI is whatever hasn't been done yet.
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 a human-assisted Turing machine.
AI applications become mainstream
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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.
The Bulletin of the Atomic Scientists organization views the AI effect as a worldwide strategic military threat. They point out that it obscures the fact that applications of AI had already found their way into both US and Soviet militaries during the Cold War. AI tools to advise humans regarding weapons deployment were developed by both sides and received very limited usage during that time. They believe this constantly shifting failure to recognise AI continues to undermine human recognition of security threats in the present day.
Some experts think that the AI effect will continue, with advances in AI continually producing objections and redefinitions of public expectations. Some also believe that the AI effect will expand to include the dismissal of specialised artificial intelligences.
Legacy of the AI winter
In the early 1990s, during the second "AI winter" many AI researchers found that they could get more funding and sell more software if they avoided the bad name of "artificial intelligence" and instead pretended their work had nothing to do with intelligence at all.
Patty Tascarella wrote in 2006: "Some believe the word 'robotics' actually carries a stigma that hurts a company's chances at funding."
Saving a place for humanity at the top of the chain of being
Michael Kearns suggests that "people subconsciously are trying to preserve for themselves some special role in the universe". By discounting artificial intelligence people can continue to feel unique and special. Kearns argues that the change in perception known as the AI effect can be traced to the mystery being removed from the system. In being able to trace the cause of events implies that it's a form of automation rather than intelligence.
A related effect has been noted in the history of animal cognition and in consciousness studies, where every time a capacity formerly thought of as uniquely human is discovered in animals (e.g. the ability to make tools, or passing the mirror test), the overall importance of that capacity is deprecated.
Herbert A. Simon, when asked about the lack of AI's press coverage at the time, said, "What made AI different was that the very idea of it arouses a real fear and hostility in some human breasts. So you are getting very strong emotional reactions. But that's okay. We'll live with that."
Mueller 1987 proposed comparing AI to human intelligence, coining the standard of Human-Level Machine Intelligence. This nonetheless suffers from the AI effect however when different humans are used as the standard.
Deep Blue defeats Kasparov
When IBM's chess-playing computer Deep Blue succeeded in defeating Garry Kasparov in 1997, public perception of chess playing shifted from a difficult mental task to a routine operation.
The public complained that Deep Blue had only used "brute force methods" and it wasn't real intelligence. Notably, John McCarthy, an AI pioneer and founder of the term "artificial intelligence", was disappointed by Deep Blue. He described it as a mere brute force machine that did not have any deep understanding of the game. McCarthy would also criticize how widespread the AI effect is ("As soon as it works, no one calls it AI anymore" ), but in this case did not think that Deep Blue was a good example.
On the other side, Fred A. 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.
