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\author{David Golombek}
\title{The Turing Test}
\date{\today}
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\maketitle  
	The Turing Test is intended to provide a quick method of determining whether a computer can think.  It presents a simple test, in which a human asks questions of two test subjects, each of which is located out of his sight.  All communication take place via some method that prevents him from making any unfair judgments from it.  He then asks questions of each subject and tries to determine which is human and which computer.  Any computer that can fool the tester into believing it is human is said to pass the test, and thus be advanced enough to be deemed able to think.  Despite the Turing Test's rather simple, non-scientific appearance and presentation, this test is all that is needed to answer the question of whether or not computers can think.  This is because the simple test that Turing describes turns out not to be so easy after all.  Any computer program that can fool a human into believing that it too is a human must be able to think, because this is the only way it will be able to pass the test.  I shall attempt to show both why passing the Turing test is so difficult, and why it is impossible to pass it without thinking.

	When most people think about computers trying to pass the Turing Test, they begin by imagining a base of a sentence parser.  This is a program that reads in a sentence and breaks it down into grammatical pieces such as nouns, verbs, and so on.  This is obviously going to be the base of any artificial intelligence program, because all such programs need some way of reading the input they are receiving.  The traditional next step has been to limit the realm of conversation topics, and then begin to build a table of relations between these verbs, nouns, and adjectives.  The limiting of subject has been due to time and machine capacity, both of which will be solved eventually.  Thus, the machines that have been built in this pattern demonstrate nicely the problems with this approach.  For perfectly worded sentences, with no ambiguity or unusual sentence structures, dealing only with the specified subject, these machines do fine.  In fact, the famous Eliza program, written in 1969, was exactly of this type.  It handled sentences such as ``I'm angry with my mother'' perfectly fine, responding with ``Tell me more about your mother''.  But when any irregularities arose in the sentence, the parser broke down, and failed to respond coherently.  At the time, it was assumed that a better parser simply needed to be built, and people attempted to do so.  However, in the twenty-five years since this attempt began, very little progress has been made.  Researchers are slowly finding that making a better sentence parser is very difficult, and have begun to research alternate methods of processing sentences.

	The true problem traditional sentence parsers have here is not about limited memory, which will eventually be remedied, but rather a lack of true understanding of sentences, at least with the traditional methods of writing artificial intelligence programs.  While computers can build up long lists of association between the information they accumulate, what happens when there are two associations that both seem to fit?  Or if there are new multiple connotations that fit, or if a person uses non-standard English?  While humans are very good at adapting to slight changes in the rules of English, it is impossible to program this into a non-thinking computer.  This is because a non-thinking computer doesn't comprehend the sentences it is passed.  A program such as this must use the rules of language, and anyone breaking these rules will also break the program.  

	In a conversation between two humans, each speaker assumes a tremendous amount of shared information with his listener.  This shared information consists of the knowledge and understanding a  human being acquires as he is growing up, through interaction with the world around him.  He directly experiences the sights, sounds, and tastes that he will later use in conversation.  Thus, he not only knows the definition of these things, but also knows their context and connotation.  Humans have a very powerful memory when it comes to associating these senses, one that is no where near understood.  However, one thing that is certain is that the way humans handle association is non-linear.  The tests that have been performed so far on the subject of human thought have merely confused researchers more.  To summarize, there appears to be no ordered method of association, in that some words are linked to memories, while others are linked by definition, while still others are linked by grammatical use.  This is natural to humans, and certainly works.  Artificial Intelligence generally succeeds by imitating humans.  Recent work with a method called neural networking appears to be coming close as an approximation of human memory, but still has far to go.  How can anything but a thinking program hope to imitate this apparently random association scheme?  It is impossible to integrate a linear program (one that doesn't think, a non-linear process) and a non-linear memory.

	Besides this shared knowledge, there are the idiosyncrasies involved in every language.  Any person uses his native language on a day to day basis, and thus has tremendous experience in dealing with these inconsistencies.  Consider all the non-standard verb conjugations, multiple word meanings, and colloquial expressions that come up in everyday English conversation.  Humans have encountered these confusing word combinations many times in their lives, at least for the more common language complications.  However, it is with the rarer word combinations that only true comprehension of the words can resolve.  There are sentences that aren't perfectly clear and can't be resolved thorough any list of rules, only by comprehension of the sentence.  Consider a sentence such as ``This essay killed me.''  It is obvious to any English speaking human that the essay did not really kill me, and that the essay actually did not do anything at all.  Note that here I am assuming someone with a moderate command of the English language, for it is unfair to make comparisons with poor speakers, as theses people couldn't pass the Turing Test themselves.  Most people will go beyond this first realization, and figure out that I really mean that I had trouble writing this paper, taking their clues from the fact that ``killed me''  might really stand for causing pain, humans typically write papers, and the only way an essay could cause pain was through having trouble writing it.  

	Now consider a complex sentence parser, as mentioned before.  A parser passed ``This essay killed me'' would encounter several difficulties.  It's first response would be to assume that I was dead.  After all, it's plainly stated in the sentence that I was killed.  It would assign blame to the essay, and be done with the matter.  A far more advanced parser would realize that essays are not usually associated with killing, and this first answer was incorrect.  However, there is no dictionary listing that associates killing with ``having a problem writing.''  Realizing that I had trouble with the essay is by far the hardest step.  This jump would require that the sentence parser know that humans write essays, they turn in essays to be graded, writing essays can be difficult, earning poor scores on essays causes bad feelings, and that under all these circumstances, killing can be a symbolic meaning of having a hard time.  Clearly, this is a lot of obtuse background information to be connected in, information that is not written in any grammar book.  The sentence itself is quite simple, and most sentence parsers wouldn't see the need to bring in these connections.  

	Considering the examples already mentioned, and the many others that can be devised in a short period of time, one can easily see why researchers have had trouble creating an improved sentence parser that can handle them.  Simply stated, humans don't follow normal grammatical rules all the time, and a sentence parser is based on these rules.  Thus, any sentence parser that is going to be able to handle {\it any} sentence must be able to comprehend what is being said.  That is, it must be able to think.

	How one can get a computer to think is an entirely different matter, one that no one has any real idea about.  If I were able to explain it, I would instead write a program that could pass the Turing Test, and become fabulously rich.  Instead, we must leave that problem aside for several more years, and much more research.  However, as shown above, in order to pass the Turing Test, a computer must be able to think, and this is why Turing devised the test as he did.  Although it seems simple, creating a computer program that can converse like a human requires that the computer be thinking.

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