Artificial Intelligence Essay Research Paper Artificial Intelligence — страница 2

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from the same deep structure. Computational linguists and AI researchers saw that these rules, once understood, could be applied, or mechanized, with a formal mathematical system. Here, “natural languages were strings of symbols constructed to different conventions, which needed to be converted to a universal human ‘machine code.’” From a computational viewpoint, language is an abstract system for manipulating symbols; the universal grammar could be purified in the sense of mathematics, in other words, being independent of physical reality. Semantics in this view would just be an application of the abstract syntax onto the real world. Chomskyan linguistics, as we shall see further on, does not acknowledge any application of syntax outside the internal realm of mind,

semantics being one of the components of syntax. The primary difficulty in AI work, and that which binds it so closely with philosophy, cognitive science, psychology, and computational and natural linguistics, is that in order to build a mind, we must understand that which we are building. While we understand the external functions which are carried out by the brain/mind (age old mind/body problem), we do not understand the mind itself. Therefore we could (though this is exceedingly difficult and has not yet been done fully) imitate the mind (or language) but not simulate it. That is not to say that this is impossible in the future, but rather that the current paradigm must be transcended and an entirely new way of understanding the mind and machines must be put forth. A computer

imitating intelligence would be like an actor who plays someone smarter than himself, whereas “simulation is only possible where there is a mathematical model, a virtual machine, representing the system being simulated.” Research with the goal of imitation is called “weak AI” and that with the goal of simulation is called “strong AI”. And so, as set forth by Chomsky, it is the goal of computational linguistics to create a mathematical model of a native speaker’s understanding of his language, as it is the goal of AI to create a mathematical model of the mind as a whole. This analogy is imbalanced in that computational linguistics is not a separate discipline, but rather could very well be the key to AI. In addition, the relationships between computational

linguistics and linguistics, or of AI and cognitive psychology (or philosophy of mind) are not of dependence of one upon the other, but of interdependence. If AI researchers were to create a functional model of the human mind in a machine, this would provide (perhaps all-encompassing) insight into the nature of the human mind, just as a complete understanding of the human mind would allow for computational modeling. The understanding of the interrelatedness of these fields is essential because in the end it will most likely be through a synthesis of work in the various fields that progress will be made. To return to the specifics of computational linguistics, we see that while Chomsky’s work was vastly responsible for spawning the modern field, the idea of natural language

“understanding” (more on this below) has been intricately tied to AI since Alan Turing posed his “Turing Test” in 1950 (which, incidentally, he predicted would be passed by the year 2000) . This test, which would supposedly determine that a machine had attained “intelligence,” is essentially that a computer would be able to converse in a natural language well enough to convince an interrogator he was talking to a human being. Yet, as we discussed above, there is a great difference between a computer so extensively programmed as to be able to imitate linguistic ability (which in itself has thus far proven extremely difficult if not impossible) or another conscious cognitive function, and one which simulates it. For example, a computer voice recognition system (one far

more perfected than those available in the present day) which has advanced pattern-recognition abilities and can respond to any natural language vocal command with the proper action, still would not be said to understand language. The true sign of AI would be a computer who possessed a generative grammar, the ability to learn and to use language creatively. This possibility may not actually be possible, and Chomsky would be the first to argue that it wouldn’t, yet an examination into his more recent work in his minimalist program shows some strands of thought whose implications are far outside of his rationalist heritage, and which could be important to AI in the future. Attempts at language understanding in computers before Chomsky were limited to trials like the