The Turing test has been proposed as a measure of a machine's "ability to think" or its "intelligence". This proposal has received criticism from both philosophers and computer scientists. It assumes that an interrogator can determine if a machine is "thinking" by comparing its behavior with human behavior. Every element of this assumption has been questioned: the reliability of the interrogator's judgement, the value of comparing only behavior and the value of comparing the machine with a human.
Human intelligence vs
intelligence in general
The Turing test does not directly test whether the computer behaves intelligently - it tests only whether the computer behaves like a human being. Since human behavior and intelligent behavior are not exactly the same thing, the test can fail to accurately measure intelligence in two ways:
The Turing test does not directly test whether the computer behaves intelligently - it tests only whether the computer behaves like a human being. Since human behavior and intelligent behavior are not exactly the same thing, the test can fail to accurately measure intelligence in two ways:
Some human behavior is unintelligent
The Turing test requires that the machine be able to execute all human behaviors, regardless of whether they are intelligent. It even tests for behaviors that we may not consider intelligent at all, such as the susceptibility to insults, the temptation to lie or, simply, a high frequency of typing mistakes. If a machine cannot imitate these unintelligent behaviors in detail it fails the test.
The Turing test requires that the machine be able to execute all human behaviors, regardless of whether they are intelligent. It even tests for behaviors that we may not consider intelligent at all, such as the susceptibility to insults, the temptation to lie or, simply, a high frequency of typing mistakes. If a machine cannot imitate these unintelligent behaviors in detail it fails the test.
Some intelligent behavior is inhuman
The Turing test does not test for highly intelligent behaviors, such as the ability to solve difficult problems or come up with original insights. In fact, it specifically requires deception on the part of the machine: if the machine is more intelligent than a human being it must deliberately avoid appearing too intelligent. If it were to solve a computational problem that is practically impossible for a human to solve, then the interrogator would know the program is not human, and the machine would fail the test.
The Turing test does not test for highly intelligent behaviors, such as the ability to solve difficult problems or come up with original insights. In fact, it specifically requires deception on the part of the machine: if the machine is more intelligent than a human being it must deliberately avoid appearing too intelligent. If it were to solve a computational problem that is practically impossible for a human to solve, then the interrogator would know the program is not human, and the machine would fail the test.
Real intelligence vs simulated
intelligence
The Turing test is concerned strictly with how the subject acts — the external behavior of the machine. In this regard, it takes a behaviorist or functionalist approach to the study of intelligence. The example of ELIZA suggests that a machine passing the test may be able to simulate human conversational behavior by following a simple (but large) list of mechanical rules, without thinking or having a mind at all.
The Turing test is concerned strictly with how the subject acts — the external behavior of the machine. In this regard, it takes a behaviorist or functionalist approach to the study of intelligence. The example of ELIZA suggests that a machine passing the test may be able to simulate human conversational behavior by following a simple (but large) list of mechanical rules, without thinking or having a mind at all.
John Searle has argued that external behavior cannot be used to determine if a machine is "actually" thinking or merely "simulating thinking." His Chinese room argument is intended to show that, even if the Turing test is a good operational definition of intelligence, it may not indicate that the machine has a mind, consciousness, or intentionality.
Naivete of interrogators and the
anthropomorphic fallacy
In practice, the test's results can easily be dominated not by the computer's intelligence, but by the attitudes, skill or naivete of the questioner.
In practice, the test's results can easily be dominated not by the computer's intelligence, but by the attitudes, skill or naivete of the questioner.
Chatterbot programs such as ELIZA have repeatedly fooled unsuspecting people into believing that they are communicating with human beings. In these cases, the "interrogator" is not even aware of the possibility that they are interacting with a computer. To successfully appear human, there is no need for the machine to have any intelligence whatsoever and only a superficial resemblance to human behavior is required.
Michael Shermer points out that human beings consistently choose to consider non-human objects as human whenever they are allowed the chance, a mistake called the anthropomorphic fallacy: They talk to their cars, ascribe desire and intentions to natural forces (e.g., "nature abhors a vacuum"), and worship the sun as a human-like being with intelligence. If the Turing test is applied to religious objects, Shermer argues, then, that inanimate statues, rocks, and places have consistently passed the test throughout history. This human tendency towards anthropomorphism effectively lowers the bar for the Turing test, unless interrogators are specifically trained to avoid it.
Impracticality and irrelevance:
the Turing test and AI research
Mainstream AI researchers argue
that trying to pass the Turing Test is merely a distraction from more fruitful
research. Indeed, the Turing test is not an active focus of much academic or commercial
effort. There are several reasons.
First, there are easier ways to test their programs. Most current research in AI-related fields is aimed at modest and specific goals, such as automated scheduling, object recognition, or logistics.
Second, creating lifelike simulations of human beings is a difficult problem on its own that does not need to be solved to achieve the basic goals of AI research.
Turing, for his part, never intended his test to be used as a practical, day-to-day measure of the intelligence of AI programs.
First, there are easier ways to test their programs. Most current research in AI-related fields is aimed at modest and specific goals, such as automated scheduling, object recognition, or logistics.
Second, creating lifelike simulations of human beings is a difficult problem on its own that does not need to be solved to achieve the basic goals of AI research.
Turing, for his part, never intended his test to be used as a practical, day-to-day measure of the intelligence of AI programs.