Post date: 27-Feb-2012 06:33:50
Intelligence is a tricky concept to define explicitly. There would appear to be various types of intelligence and various levels of intelligence.
It seems remarkable then, that there is no generally agreed definition of intelligence (Legg & Hutter). Certainly, there has been a wide range of proposals. One obvious approach has been to describe intelligence by way of a list of features which comprise intelligence. These might include the likes of memory, problem solving, reasoning, planning, language, analogy, adaptation, classification, learning, discrimination, judgment, and the capacity to inhibit instinct, all to enable success in an environment. This approach ends up looking like a grab bag of features rather than a clean definition.
A common feature of many definitions of intelligence is the notion of a purposeful adaptive behavioral response to the demands of the environment. However, Intelligence involves a value judgment on the part of the observer about the merits of the behavior observed (Butler & Hodos 2005).
The famous Turing Test (Turing 1950) recognizes the ambiguities involved with questions such as “Can machines think?” and takes an indirect approach. Turing proposed an 'imitation game' in which a human interrogator must determine the gender of two unseen respondents, a man and a woman. The interrogator may pose any number of questions to the respondents but must rely entirely on typewritten responses. The objective of the male respondent is to cause the interrogator to err while that of the female respondent is to assist the interrogator. Turing then asks what will happen when a machine takes the part of the male respondent in this game. Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman?
Turing's indirect approach of allowing that a machine is merely imitating a thinking human rather than genuinely thinking, allows the discussion to proceed on relatively 'safe' ground. There is no controversy that humans can think, and the idea that a machine might one day deliver a successful imitation, while certainly ambitious, is not unduly confrontational. The modern day term “Artificial Intelligence” seems to follow along similar lines. The development of artificially intelligent systems is a challenging task without being so bold, or so rash, as to claim the development of genuine, non-human intelligence.
The Turing Test thus investigates the theoretical capacity of a machine to imitate the deceitful responses of a human male to a degree that is indistinguishable to the human interrogator. While Turing did not define 'thinking', he clearly adopted the assumption that the human male respondent is able to think, and that this ability contributes to his success in deceiving the interrogator. Turing, went on to argue that a digital computer is capable of passing the test, in principle, if not yet in practice. There has been much written on the topic since.
Whether or not a successful machine is actually thinking (whatever that may mean), it is certainly able to imitate a thinking device (a human male) to a considerable extent. The deceptive capacity of each individual human male will of course vary, as will the interrogator's skill in teasing out the truth of the matter. Human performances also clearly vary from one day to the next. So, the Turing Test maps out a region of convincing performance, but the boundaries are distinctly fuzzy. This highlights that a machine operating within this region may well exceed the deceptive capabilities of some human males. One approach that a machine could take would be to closely imitate a particular individual, but this probably would not work so well if the interrogator keeps coming up with new tactics. Providing 'typical' responses may represent an easier route to success for a machine, but it does introduce the notion of a more 'general' capability – a step away from pure imitation and perhaps towards what we might consider intelligence.
Turing's imitation game pits the intellectual ability of the interrogator against that of the deceptive male respondent. Both contestants will certainly be thinking quite hard about their strategies to win the game. This is interesting, because when a machine takes place of the deceptive male respondent, the intelligence of the interrogator remains as the gauge of the machine's success. Turing chose to use the thinking ability of a human interrogator to discriminate a successful imitation of thinking. This is a curiously circular approach, where thinking is being employed to recognize thinking.
In any case, there is a conceivable, loosely-defined set of machines that pass the Turing Test by imitating a human male attempting to deceive an interrogator, by masquerading as a female.
There is a profoundly tragic irony here. Alan Turing was a brilliant man who was instrumental in breaking German ciphers, including the Enigna machine, in the second world war. Turing appears to have committed suicide after accepting treatment with female hormones as an alternative to imprisonment for homosexuality. (Hodges 1996)
Turing's test drags together two apparently opposite extremes; animate homo-sapiens who unquestionably think; and inanimate machines. This leaves rather a large expanse of middle ground inviting questions such as, “Can animals other than humans think” or “Can infant humans think?”. Such questions lead into progressively murkier territory with the likes of, “Which animals can think, and at what age?”. These questions are difficult because they challenge our intuition about what it means to think, and challenge our ability to test for intelligence in animals that do not have human language skills.
Turing's test deftly avoids considerable controversy by simply comparing the machine to the best and most accepted example of thinking that we have - a human. Unfortunately for our ego, mammals do not always have the most sophisticated brain systems and even primates are not necessarily the most complex (Buter & Hodos 2005). For example, many mammals have superior olfaction to that of primates, bats and marine mammals have superior audition, birds have superior vision, and frogs have faster tongues. Even when the ratio of the cortex surface area to brain volume is examined, humans are average rather than exceptional.
It is all to easy to fall into the trap of measuring non-human animal cognition by the gold-standard of human cognition. Indeed, it is difficult to even imagine how animals think and perceive the world with such foreign sense organs as the sonar of bats or the lateral line of fish. Most intelligence tests are designed around quantifying one human cognitive function or another (Butler Hodos 2005) without regard for the differences in animal cognition. For example, rats perform as well in an olfactory version of a test as other primates in visual versions. Butler and Hodos caution against viewing non-human intelligence as a scaled down version of human intelligence as it overlooks the special adaptations of both human and non-humans.
Butler, A.B. & Hodos, W. (2005), Comparative Vertebrate Neuroanatomy - Evolution and Adaption. Wiley.
Legg,S. & Hutter,M. (2006), A Collection of Definitions of Intelligence.
Russell,S.J. and Norvig, P. (2003), Artificial Intelligence: A Modern Approach. (2nd Edition) Prentice Hall.
Turing, A.M. (1950) Computing Machinery and Intelligence. Mind 49: 433-460.