FREE ESSAY ON ARTIFICIAL INTELLIGENCE

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. 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
Bibliography
Moravec, Hans. (1988) Mind Children: The Future of Robot and Human Intelligence
(Cambridge MA, Harvard University Press.
Schank interviewed in: Kurzweil, Ray. (1987) The Age of Intelligent Machines
[videorecording] / written and narrated by Ray Kurzweil. Waltham MA: Kurzweil Foundation;
Cambridge MA : distributed by MIT Press
Wooley (1992) pg. 106
Hogan, James P. (1997) Mind Matters: Exploring the World of Artificial Intelligence. 
New York: Ballantine.
Wooley, Benjamin. (1992) Virtual Worlds. Cambridge, MA: Blackwell Publishers. pg. 119 
Turing, Alan M. (1950) Computing Machinery and Intelligence, Mind 59: 433-460
Ibid, pg. 203
Weizenbaum, Joseph. ELIZA - A Computer Program for the Study of Natural Language
Communication between Man and Machine. Communications of the Association for Computing
Machinery 9, no. 1 (January 1965): 36-45
Rapaport, William J. (1999) How to Pass a Turing Test: Syntactic Semantics, Natural
Language Understanding, and First Person Cognition. Posted on Rapaport's WWW page
(http://www.cse.buffalo.edu/~rapaport/papers.html). He gives no bibliographical
information, but presents the article as the premise for a forthcoming book entitled
Understanding Understanding: Semantics, Computation, Cognition
Chomsky, Noam. (1982). Lectures on Government and Binding. Dordrecht: Foris. pg. 324
McGilvray, James. Meanings are Syntactically Individuated and Found in the Head, Mind and
Language 13: 225-80. pg. 268
Ibid, pg. 227
Percy, Walker (1975) The Message in the Bottle: How Queer Man is, How Queer Language is,
and What One Has To Do With the Other. New York: Farrar, Straus and Giroux.
Derrida, Jacques Structure, Sign, and Play in the Discourse of the Human Sciences, The
Strucuralist Controversy: The Languages of Criticism and the Sciences of Man, ed. Richard
Macksey and Eugenio Donato. Baltimore, Md.: Johns Hopkins University Press, 1972 pg.
247-8
Rapaport (1999)