1,261
11
Essay, 19 pages (5000 words)

Artificial intelligence 3392

Advertising We'll write a high-quality original custom paper on Artificial intelligence 3392 just for you with a 15% discount for the 1st order Use a Discount Use a Discount

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)

Thank's for Your Vote!
Artificial intelligence 3392. Page 1
Artificial intelligence 3392. Page 2
Artificial intelligence 3392. Page 3
Artificial intelligence 3392. Page 4
Artificial intelligence 3392. Page 5
Artificial intelligence 3392. Page 6
Artificial intelligence 3392. Page 7
Artificial intelligence 3392. Page 8
Artificial intelligence 3392. Page 9

This work, titled "Artificial intelligence 3392" was written and willingly shared by a fellow student. This sample can be utilized as a research and reference resource to aid in the writing of your own work. Any use of the work that does not include an appropriate citation is banned.

If you are the owner of this work and don’t want it to be published on AssignBuster, request its removal.

Request Removal

Cite this Essay

References

AssignBuster. (2022) 'Artificial intelligence 3392'. 25 October.

Reference

AssignBuster. (2022, October 25). Artificial intelligence 3392. Retrieved from https://assignbuster.com/artificial-intelligence-3392/

References

AssignBuster. 2022. "Artificial intelligence 3392." October 25, 2022. https://assignbuster.com/artificial-intelligence-3392/.

1. AssignBuster. "Artificial intelligence 3392." October 25, 2022. https://assignbuster.com/artificial-intelligence-3392/.


Bibliography


AssignBuster. "Artificial intelligence 3392." October 25, 2022. https://assignbuster.com/artificial-intelligence-3392/.

Work Cited

"Artificial intelligence 3392." AssignBuster, 25 Oct. 2022, assignbuster.com/artificial-intelligence-3392/.

Get in Touch

Please, let us know if you have any ideas on improving Artificial intelligence 3392, or our service. We will be happy to hear what you think: [email protected]