Artificial Intelligence

Artificial Intelligence As we get closer to the twenty-first century, we find that soon a computer will be another common household appliance. As the television was introduced in the 1950’s, it soon became an essential part of everyday life. It is now found in every home and an importance source for entertainment and for gaining information. In the next couple of years, the same will be said for computer. It is fast becoming as essential part of our everyday life.

With the Internet becoming an important resource for gaining information with the touch of a button. Yet, this is just the beginning of the computer age. We now use components from computers to run other household appliances such as: microwave ovens, phones, alarm clocks, VCRs, and even television themselves have change to incorporate computer components. We even have cars with computers installed within them. Soon everything in home will be run, in some way by a computer.

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Yet, with the advancement in computers, engineers are still trying to find a way to create Artificial Intelligence. This would truly take the computer to the next level, but creating something of this magnitude is extremely difficult. Lets first take a look at what we have now. As we look into businesses, we find that robotics has become as important asset for companies to stay in business. Robots can produce products more rapidly and more efficient than the human work force. Though robots cannot totally replace people in all work fields, they help in limiting mistakes, and boosting productivity.

Still, robots have their limitations. To look at these limitations we first must know how computers work. Perhaps more than anything else, are the multi-purpose functionally and its ability to perform hundreds of very different tasks, which makes computers an essential part of our lives. Every computer has five functions: input, output, processing, information holding, and control. Before we get into the five functions, lets first take a quick look at the computer language.

Computer language is binary number, basic 0 and 1. To look at this simply lets look at a light switch. A light switch, you could say, is also binary. On, will make the bulb receive electricity, which would make it illuminate. Off, will stop the electricity from getting to the bulb which would make it stop illuminating.

This is a simple way of describing the binary language of a computer, but of course theres more to it than that. Though it uses binary language, it must also be able to understand human language. Therefore, computers must convert the alphabets and numbers into binary language. To understand how computers convert the alphabets and numbers into binary language, we must explore what is called bytes. A bit is either 0 or 1, which is as I explained earlier the same as binary language.

A byte is a series of eight bits, which converts the alphabets and the number series into binary language. For example, lets take the letter A, notice I use capital a, because both uppercase and lowercase has its own binary code. The computer knows the letter A as 01000001. Therefore, when you type the letter A in the computer, the computer is sent this code in place of the key that you typed. Converts this code into A, and displays the A on your monitor. This is just to give you a simple example of what the computer read when you type on the keyboard.

Keep in mind that each letter of the alphabet (both upper and lowercases) has its own binary code, as do the numbers from 0 to 9, and symbols such as +, -, and &, to name a few. To get back to the five functions of the computer, lets explore each one. First we explore input. Input is a set of instructions, which tells the computer what to do. Such instructions are known as the programs. Input is also anything you type on the keyboard, click with your mouse, or scan on your scanner. However, without the program, anything you type on the keyboard is useless, and the same goes with any other input device.

When you type on your keyboard, the program in the computer instructs the computer to convert these keyboard inputs into binary codes. For example, if you type the letter A, the program in the computer tells the computer that A equals 01000001. This way the computer understands what you are saying. In return, the computer sends out an output. An out is displayed on your monitor.

Output is information, which is run through the computer and displays the result on your monitor. When the computer is running this calculation this is called, processing information. So when you press the key A, the computer receives 01000001, checks to see what is in the space 01000001, retrieves whats in that space and places it on the monitor. This is input, output, and processing. So far, we covered three of the five functions of the computer.

Now lets look at the information holding. This is a very important aspect of your computer. Without this, your computer would not be able to perform its tasks. There are basically two main information-holding devises, which are RAM and ROM; read-only memory is your programs. The place in your CPU where your instructions are held.

This tells your computer what to do. RAM, random-access memory is basically a place to store information for a short period of time. RAM, is where your input is stored until the computer finds out what to do with it. When you type in A the controller goes store the information in RAM, then goes into ROM for instructions. Once ROM tells the computer where A is located, (that key is located in 01000001) the computer retrieves the information and then goes to RAM, (this is what you looking for) and displays it on the monitor.

This is a very simple explanation of how your computer works. It is just to give an idea of the functions of the computer. Now that we have a basic idea of how computers work, let us look into robotics. Robots are used for many tasks; some which are dangerous, some that people cannot accomplish easily, and some, which are just tedious, work. Still these tasks are important to a company to stay in business. Lets us look at an auto manufacturer for example. Let us say you needed to install seats into a car.

You have an employee doing the tasks. The seats of a car are heavy. It would take a person much time to get the seat, bring them to the car, and place them in the appropriate place before they weld them to the body of the car. With a robotic arm, the tasks could be done in less than half the time it takes the individual to do it. Cutting down not only time but also the amount of money it took to build the car.

With the cut down in time, more cars could be built in a day with the robot than with people alone. Now take into account the windshields, the different body part of the car such as doors, hoods, well you get the picture. You now get an idea of how important robots are to a car manufacturer. Car manufacturers, but also TV manufacturers, the Post Office, and the list go on. Once you get an idea of how robots are programmed, you will see how difficult it is to create Artificial Intelligence. First we must explore how robots are programmed.

Earlier you got a brief explanation of how programming works. Of course programming a robot is much more complicated. More complicated than you could imagine, because machines are stupid. For example, if you wanted a robot to open a door. If you were talking to a person, you would say open the door please, and the person would open the door.

A robot would not know what open the door meant. You would have to explain the process to the robot through its program. Something like this: Approach the door. Stop twelve inches from the door. Lift up your left hand and place it on the doorknob. Grip the doorknob.

Turn the doorknob 45o radius. Stop. Pull the doorknob. Take one step back. Pull the doorknob. Release the doorknob.

Etc. Etc. Well you get the picture. Even these instructions might be a little vague fro a robot to understand. If now the robot knows how to open the door, it wouldnt know how to close the door.

You would have to go through the same programming process of how to close a door. In other words every tasks which is done by a robot is do to its programming. A robot cannot deviate from its program. Some bosses would love to have employees like this, but like the saying goes Be careful what you wish for. When we speak about Artificial Intelligence, we are talking about computers that act and think like people.

You might say that should be simple, but its not. Until now computers do as there program instructs them to do. If you program a computer to add 1 + 1 equal 3, thats what the answer will equal to all the time for as long as the program stays the same. Not until you change the program will 1 + 1 not equal 3. The computer cannot go and find the information for itself.

Computers cannot do more than one thing at a time. It could have hundreds of programs to do different task, but it could only do one task at a time. Unlike the human brain which could process more than one thing at a time. In addition, computers cannot learn on their own. As I said before computers only do what its program allows it to do.

When engineers try to design Artificial Intelligence, I believe that this is where the break through is beginning. There is an attractive similarity between computers and humans. It is almost impossible to resist the temptation to compare a CPU and memory to the human brain and I/O devices to our senses. Information flows into our memory through sight, sound, touch, taste, and smell. Our brain remembers the information, decides to take action, and send commands to our muscles so that we speak or move around. This analogy is the origin of the term electronic brain. Assuming that things are alike because they look alike is a common error. In this case, although there are similarities in structure, computers and humans operate in fundamentally different ways.

The human brain, though operates similar to the CPU, it is in many ways different. Unlike the computer, when the brain receives input from different places of the body, different parts of a brain process these inputs. This is the major difference between the brain and a CPU. We have a brain, which can perform many different tasks at one time. Not until engineers create a CPU with the capability to perform multiple tasks, and learn on its own, will they be able to create an Artificial Intelligent being that could be compared to people.

Computers and Internet.

Artificial Intelligence

Artificial Intelligence is based in the view that the only way to prove you know
the mind’s causal properties is to build it. In its purest form, AI research
seeks to create an automaton possessing human intellectual capabilities and
eventually, consciousness. There is no current theory of human consciousness
which is widely accepted, yet AI pioneers like Hans Moravec enthusiastically
postulate that in the next century, machines will either surpass human
intelligence, or human beings will become machines themselves (through a process
of scanning the brain into a computer). Those such as Moravec, who see the
eventual result as “the universe extending to a single thinking
entity” as the post-biological human race expands to the stars, base their
views in the idea that the key to human consciousness is contained entirely in
the physical entity of the brain. While Moravec (who is head of Robotics at
Carnegie Mellon University) often sounds like a New Age psychedelic guru
professing the next stage of evolution, most AI (that which will concern this
paper) is expressed by Roger Schank, in that “the question is not ‘can
machines think?’ but rather, can people think well enough about how people think
to be able to explain that process to machines?” This paper will explore
the relation of linguistics, specifically the views of Noam Chomsky, to the
study of Artificial Intelligence. It will begin by showing the general
implications of Chomsky’s linguistic breakthrough as they relate to machine
understanding of natural language. Secondly, we will see that the theory of
syntax based on Chomsky’s own minimalist program, which takes semantics as a
form of syntax, has potential implications on the field of AI. Therefore, the
goal is to show the interconnectedness of language with any attempt to model the
mind, and in the process explain Chomsky’s influence on the beginnings of the
field, and lastly his potential influence on current or future research. Chomsky
essentially founded modern linguistics in seeking out a systematic, testable
theory of natural language. He hypothesized the existence of a “language
organ” within the brain, wired with a “deep structured” universal
grammar that is transmitted genetically and underlies the superficial structures
of all human languages. Chomsky asserted that underlying meaning was carried in
the universal grammar of deep structures and transformed by a series of
operations that he termed “transformational rules” into the less
abstract “surface structures” that was the spoken form of the various
natural languages. He showed also that mental activities in general can and
should be investigated independently of behavior and cognitive underpinnings.

This “idealization” of the linguistic capability of a native speaker
brought Chomsky to his nativist, internalist, and constructivist philosophical
views of language and mind. This concept of generative grammar could be seen as
“a ‘machine’, in the abstract Turing sense, that can be used to generate
all the grammatical sentences in a given language.” Chomsky was searching
for a formal method of describing the possible grammatical sentences of a
language, as the Turing machine (more below) was used to specify what was
possible in the language of mathematics. Chomsky’s transformational generative
grammar (TGG) possessed the most influence on AI in that it was a specification
for a machine that went beyond the syntax of a language, to their semantics, or
the ways that meanings are generated. An ambiguous sentence like “I like
her cooking” or “flying planes can be dangerous” could have a
single surface structure from multiple deep structures, just as semantically
equivalent sentences involving a transformation from active to passive voice or
the like, could have different surface structures emerging 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.

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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 military-funded effort
of Warren Weaver, who saw Russian as English coded in some “strange
symbols.” His method of computer translation relied on automatic dictionary
and grammar reference to rearrange the word equivalents. But, as Chomsky made
very clear, language syntax is much more than lexicon and grammatical word
order, and Weaver’s translations were profoundly inaccurate. Contrary to their
original speculations in the dawn of the AI age (50’s-60’s), the most complex
human capabilities have proven simple for machines, while the simplest things
human children do almost mindlessly, such as tying shoes, acquiring language, or
learning itself, prove the most difficult (if not impossible). Numerous computer
language modeling programs have been created, the details of which are not
essential to the topic of this paper and will not be delved into, yet none as of
yet can approach the Turing Test. Much difficulty arises from linguistic
anomalies like the ambiguities mentioned above, as in the old AI adage
“time flies like an arrow; fruit flies like a banana.” The early
language programs, like Joseph Weizenbaum’s ELIZA (which was able to convince
adult human beings that they were receiving genuine psychotherapy through a
cleverly designed Rogerian system of asking “leading questions” and
rephrasing important bits of entered data) had nothing to do with modeling of
language. Rather, these were programs which were programmed to respond to input
with a variable output of designed speech with no generative grammatical or
lexical capability. Early attempts at computational linguistics, under Chomsky’s
influence, attempted to model sentences by syntax alone, hoping that if this
worked, the semantics could be worked out subsequently, and only once, for the
deep structure. However, as Chomsky showed much later on, semantics is part of
syntax (the most important part), and thereby could not be dealt with
post-syntactically. Not unsurprisingly, the only linguistic area where computers
thus far have shown considerable ability is the area that humans find the most
difficult, whereas the simplest human linguistic abilities remain elusive.

Sentences known as recursive, or left or right-branching such as The monkey that
the lion who had eaten the zebra wouldn’t eat ate the banana, have an infinite
capacity for embeddings, allowing for the vastly superior memory of the computer
to be more effective in parsing them. Understanding that Chomsky’s original
breakthroughs (those of Syntactic Structures and his 60’s work) had profound
impact on Artificial Intelligence, the remainder of this paper will speculate on
the potential impact of his minimalist program and the nature of what I will
call the “syntactic mind.” The premise of the argument is presented by
SUNY Professor William Rapaport in his essay “How to Pass a Turing Test:
Syntactic Semantics, Natural Language Understanding, and First Person
Cognition,” as a rebuttal to John Searle’s Chinese Room argument, which
Rapaport describes as: “1) Computer programs are purely syntactic. 2)
Cognition is semantic. 3) Syntax alone is not sufficient for semantics. 4)
Therefore, no purely syntactic computer program can exhibit semantic
cognition.” Rapaport responds by saying that syntax is sufficient for
semantics, and if you accept that, then you discover that a purely syntactic
computer program can exhibit semantic cognition; in other words, if semantics
can be incorporated into syntax, then the computer program can simulate the
cognitive mind. This is a bold statement, so let’s see how it is derived from
Chomsky’s work. Syntax is defined as the relations among a set of markers (Rapaport
refrains from calling them symbols as “symbol” implies an inherent
connection to an external object), and semantics is the relations between the
system of markers and “other things,” (their meanings). His argument
claims that if the set of markers is merged with the set of meanings, then the
resulting set is a new set of markers, a sort of meta-syntax. The mechanism that
the symbol-user (native speaker) uses to understand the relation between the old
and new markers is a syntactic one. The simplest way to put all this would be
that semantics must be understood syntactically, and is therefore a form of
syntax. The crux of the argument is that a word (for example tree) does not
signify an actual external tree-object, but rather signifies the internal
representation tree found in the mind. This idea goes to back to Chomsky’s
Lectures on Government and Binding where he introduces “Relation R,”
elucidated by James McGilvray as “reference, but without the idea that
reference relates an LF Logical Form, or SEM, semantic form that stands
between elements of an LF and these stipulated semantic values that serve to
‘interpret it’. This relation places both terms of Relation R, LF’s and their
semantic values, entirely within the domain of syntax, broadly conceived;. .

.They are in the head.” Chomsky’s internalism goes back to the Cartesian
view that all sensory input is subjective and therefore nothing can be known
outside of the mind. Therefore language cannot refer to external objects, but
rather, either to its internal representations of them based on sensory input,
or to concepts (like Unicorns) which have no external source to represent. So
Chomsky’s internalism and nativism allow for the syntactic phrase in its
semantic interface “an internally constituted perspective that can play a
role in individuating, and even constructing the things of a world.” The
implications for AI lie in that the purely syntactic symbol manipulation of a
computational system’s knowledge base suffices for it to understand natural
language. The end-pursuit of “strong” AI is to model or simulate human
consciousness. If syntax exists only inside a larger mental meta-syntax (rather
than semantics) then the human consciousness is a world of signifiers, our
mental reality suffers a permanent disengagement from the signified. “It is
not really the world which is known but the idea or symbol. . ., while that
which it symbolizes, the great wide world, gradually vanishes into Kant’s
unknowable noumena.” If we take the Chomsky/McGilvray idea of “broad
syntax” one step farther, philosophically, we find that the labyrinth of
signifiers which is the syntactic mind exists in a world in which there is no
concept outside the mechanisms of representation. Strangely, the post-structuralist
Jacques Derrida, who Chomsky despises, says the same thing. At the origin of
language “in the absence of a center of origin, everything became
discourse. . .that is to say, when everything became a system where the central
signified, the original or transcendental signified, is never absolutely present
outside a system of differences. The absence of the transcendental signified
extends the domain and the interplay of signification ad infinitum.” What
Derrida is talking about by a transcendental signified is the semantic, external
reality to which syntax refers. It is transcendental in that it transcends
syntactic representation, it transcends the syntactic mind. The internalist view
does not deny the existence of the external world, rather, when McGilvray refers
to “constructing the things of the world” through language, it is the
world of human consciousness to which he refers. In this theory, it is through
Chomsky’s I-language, through syntax, that we construct our world. This is the
essence of Chomsky’s constructivism. So we see that if we are to construct a
thinking machine (or for that matter, representations in our mind of a thinking
machine) this broad syntax does significantly clarify how to go about designing
a computer which can take discourse as input, remember and learn, etc. . .If we
realize however the syntactic nature of the minds which create the machine, we
can see that it is possible for a machine to think syntactically, or at least
that Searle’s Chinese Room argument does not stand up, because cognition is not
dependent on semantics. Thus, a thinking machine would be “a purely
syntactic system” of symbols (a neural network) and algorithms for
manipulating them. So we have seen that Chomsky (despite his own description of
AI as “natural stupidity) has had profound influence upon linguistics, and
thereby upon AI, as computational linguistics are central to past and future
attempts to simulate the human mind. Artificial Intelligence is based in the
view that the only way to prove you know the mind’s causal properties is to
build it. In its purest form, AI research seeks to create an automaton
possessing human intellectual capabilities and eventually, consciousness. There
is no current theory of human consciousness which is widely accepted, yet AI
pioneers like Hans Moravec enthusiastically postulate that in the next century,
machines will either surpass human intelligence, or human beings will become
machines themselves (through a process of scanning the brain into a computer).

Those such as Moravec, who see the eventual result as “the universe
extending to a single thinking entity” as the post-biological human race
expands to the stars, base their views in the idea that the key to human
consciousness is contained entirely in the physical entity of the brain. While
Moravec (who is head of Robotics at Carnegie Mellon University) often sounds
like a New Age psychedelic guru professing the next stage of evolution, most AI
(that which will concern this paper) is expressed by Roger Schank, in that
“the question is not ‘can machines think?’ but rather, can people think
well enough about how people think to be able to explain that process to
machines?” This paper will explore the relation of linguistics,
specifically the views of Noam Chomsky, to the study of Artificial Intelligence.

It will begin by showing the general implications of Chomsky’s linguistic
breakthrough as they relate to machine understanding of natural language.

Secondly, we will see that the theory of syntax based on Chomsky’s own
minimalist program, which takes semantics as a form of syntax, has potential
implications on the field of AI. Therefore, the goal is to show the
interconnectedness of language with any attempt to model the mind, and in the
process explain Chomsky’s influence on the beginnings of the field, and lastly
his potential influence on current or future research. Chomsky essentially
founded modern linguistics in seeking out a systematic, testable theory of
natural language. He hypothesized the existence of a “language organ”
within the brain, wired with a “deep structured” universal grammar
that is transmitted genetically and underlies the superficial structures of all
human languages. Chomsky asserted that underlying meaning was carried in the
universal grammar of deep structures and transformed by a series of operations
that he termed “transformational rules” into the less abstract
“surface structures” that was the spoken form of the various natural
languages. He showed also that mental activities in general can and should be
investigated independently of behavior and cognitive underpinnings. This
“idealization” of the linguistic capability of a native speaker
brought Chomsky to his nativist, internalist, and constructivist philosophical
views of language and mind. This concept of generative grammar could be seen as
“a ‘machine’, in the abstract Turing sense, that can be used to generate
all the grammatical sentences in a given language.” Chomsky was searching
for a formal method of describing the possible grammatical sentences of a
language, as the Turing machine (more below) was used to specify what was
possible in the language of mathematics. Chomsky’s transformational generative
grammar (TGG) possessed the most influence on AI in that it was a specification
for a machine that went beyond the syntax of a language, to their semantics, or
the ways that meanings are generated. An ambiguous sentence like “I like
her cooking” or “flying planes can be dangerous” could have a
single surface structure from multiple deep structures, just as semantically
equivalent sentences involving a transformation from active to passive voice or
the like, could have different surface structures emerging 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 military-funded effort
of Warren Weaver, who saw Russian as English coded in some “strange
symbols.” His method of computer translation relied on automatic dictionary
and grammar reference to rearrange the word equivalents. But, as Chomsky made
very clear, language syntax is much more than lexicon and grammatical word
order, and Weaver’s translations were profoundly inaccurate. Contrary to their
original speculations in the dawn of the AI age (50’s-60’s), the most complex
human capabilities have proven simple for machines, while the simplest things
human children do almost mindlessly, such as tying shoes, acquiring language, or
learning itself, prove the most difficult (if not impossible). Numerous computer
language modeling programs have been created, the details of which are not
essential to the topic of this paper and will not be delved into, yet none as of
yet can approach the Turing Test. Much difficulty arises from linguistic
anomalies like the ambiguities mentioned above, as in the old AI adage
“time flies like an arrow; fruit flies like a banana.” The early
language programs, like Joseph Weizenbaum’s ELIZA (which was able to convince
adult human beings that they were receiving genuine psychotherapy through a
cleverly designed Rogerian system of asking “leading questions” and
rephrasing important bits of entered data) had nothing to do with modeling of
language. Rather, these were programs which were programmed to respond to input
with a variable output of designed speech with no generative grammatical or
lexical capability. Early attempts at computational linguistics, under Chomsky’s
influence, attempted to model sentences by syntax alone, hoping that if this
worked, the semantics could be worked out subsequently, and only once, for the
deep structure. However, as Chomsky showed much later on, semantics is part of
syntax (the most important part), and thereby could not be dealt with
post-syntactically. Not unsurprisingly, the only linguistic area where computers
thus far have shown considerable ability is the area that humans find the most
difficult, whereas the simplest human linguistic abilities remain elusive.

Sentences known as recursive, or left or right-branching such as The monkey that
the lion who had eaten the zebra wouldn’t eat ate the banana, have an infinite
capacity for embeddings, allowing for the vastly superior memory of the computer
to be more effective in parsing them. Understanding that Chomsky’s original
breakthroughs (those of Syntactic Structures and his 60’s work) had profound
impact on Artificial Intelligence, the remainder of this paper will speculate on
the potential impact of his minimalist program and the nature of what I will
call the “syntactic mind.” The premise of the argument is presented by
SUNY Professor William Rapaport in his essay “How to Pass a Turing Test:
Syntactic Semantics, Natural Language Understanding, and First Person
Cognition,” as a rebuttal to John Searle’s Chinese Room argument, which
Rapaport describes as: “1) Computer programs are purely syntactic. 2)
Cognition is semantic. 3) Syntax alone is not sufficient for semantics. 4)
Therefore, no purely syntactic computer program can exhibit semantic
cognition.” Rapaport responds by saying that syntax is sufficient for
semantics, and if you accept that, then you discover that a purely syntactic
computer program can exhibit semantic cognition; in other words, if semantics
can be incorporated into syntax, then the computer program can simulate the
cognitive mind. This is a bold statement, so let’s see how it is derived from
Chomsky’s work. Syntax is defined as the relations among a set of markers (Rapaport
refrains from calling them symbols as “symbol” implies an inherent
connection to an external object), and semantics is the relations between the
system of markers and “other things,” (their meanings). His argument
claims that if the set of markers is merged with the set of meanings, then the
resulting set is a new set of markers, a sort of meta-syntax. The mechanism that
the symbol-user (native speaker) uses to understand the relation between the old
and new markers is a syntactic one. The simplest way to put all this would be
that semantics must be understood syntactically, and is therefore a form of
syntax. The crux of the argument is that a word (for example tree) does not
signify an actual external tree-object, but rather signifies the internal
representation tree found in the mind
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He gives no bibliographical information, but presents the article as the premise
for a forthcoming book entitled “Understanding Understanding: Semantics,
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