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
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Derrida, Jacques "Structure, Sign, and
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