a.i.general

TJFS: Turkish Journal of Fuzzy Systems (eISSN: 1309–1190)

An Official Journal of Turkish Fuzzy Systems Association

Vol.1, No.1, pp. 55-79, 2010.

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From deterministic world view to

uncertainty and fuzzy logic: a critique of

artificial intelligence and classical logic

Ayten Yılmaz Yalçıner*

University of Sakarya, Faculty of Engineering, Department of Industrial Engineering

54187, Sakarya, Turkey

E-mail: [email protected]

*Corresponding author

Berrin Denizhan

University of Sakarya, Faculty of Engineering, Department of Industrial Engineering

54187, Sakarya, Turkey

E-mail: [email protected]

Harun Taşkın

University of Sakarya, Faculty of Engineering, Department of Industrial Engineering

54187, Sakarya, Turkey

E-mail: [email protected]

Received: March 3, 2010 - Revised: May 24, 2010 – Accepted: June 5, 2010

Abstract

The purpose of this paper is to show paradigm shift about mechanistic and deterministic

world view or thinking that it has been a dominant role over centuries. This view is

based on three columns: First, Aristotelian Logic which it has nearly ruled for twenty

three centuries. Second is Newtonian Mechanics and Cartesian Dualism. Third

Determinism, which has been transformed Uncertainty in the 20th century. The central

theme of this paper is Critique of Classical Logic, Cartesian Dualism and Artificial

Intelligence. Finally we will show that natural thinking and system would be ruled in

the next century: Fuzzy World or Fuzzy Logic.

Keywords: Fuzzy logic, classical logic, Cartesian dualism, artificial intelligence

1. Introduction

This essential idea of this paper is to critise classical logic, mechanistic world or

deterministic world view, Cartesian dualism, machine/artificial intelligence. Our aim is

to prove the profound role of the fuzzy logc. Uncertainity and natural/human

intelligence. The contents of this paper are based on nine sections;

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[1] Introduction

[2] Aristotelian Logic

[3] Cartesian and Newtonian World View:

Mind-Body Problem and Dualism

[4] Mind Intelligence and Artificial Intelligence

[5] Human Intelligence versus Machine/Artificial Intelligence

[6] Critique of Artificial Intelligence

[7] Gödel‘s Theorem: Incompleteness and Human Reasoning

[8] Logic and Artificial Intelligence: Uncertainty and Fuzzy Logic

[9] Epilogue

2. Aristotelian logic

The analysis of logical form, opposition and conversion are combined in syllogistic,

Aristotle‘s the greatest invention in logic Aristotle may also be credited with the

formulation of several metalogical theses, most notably the Law of Noncontradiction,

the principle of the Excluded Middle, and the Law of Bivalence (King and Shapiro,

1995).

Fernand Schwarz, a French anthropologist and philosopher, in his book, was termed the

Aristotelian logic either/ or logic as ―metaphysical calamity or disaster‖ (Schwarz,

1997).

After centuries it was understood that the world and human being can not be represent

with yes-no rules, laws and expressions..When we reach to 20th

century, a new approach

to physical world takes place: Fuzzy World

2.1. Goodbye to the Aristotelian weltanschauung

In contrast to ineffective attempts in the past by different scholars to criticize or refute

one or the other of the Aristotelian principles in isolation, it successfully terminates the

whole Aristotelian paradigm that has been reigning over scientific reasoning and human

culture for the last 2300 years. In this sense, it represents a unique, unprecedented

example of Thomas Kuhn‘s account of scientific change by paradigm shift. The all-

embracing paradigm shift caused by fuzzy theory that we are excitedly witnessing is too

far-reaching to allow any of the Aristotelian foundations to survive. It, thus, exercises

an unfuzzy break with a long-standing and deeply entrenched tradition.

At the highest level of generality we presently encounter, to our surprise, a particular

disciplinary matrix which has been nourishing all sciences and theories for the last 2300

years, i.e. the Aristotelian disciplinary matrix, because it contains the two-valued,

classical logic with which researchers‘ reason and defend their work. What is being

eradicated by fuzzy theory is just this universal disciplinary matrix (Zadeh, 2001).

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L. Zadeh, father of Fuzzy Logic, shows this paradigm shift from bivalent logic to fuzzy

logic is to represent better than Aristotalian (human) reason in fig.1

New Logical Systems by L. A. Zadeh

Figure1. Logical Systems (Zadeh, 2006)

To explain new paradigm shift before we will interpret Cartesian and Newtonian World

View.

3. Cartesian and Newtonian world view

In the sixteenth and seventeenth centuries the medieval worldview, based on

Aristotelian philosophy and Christian theology, changed radically. The notion of an

organic, living, and spiritual universe was replaced by that the world as a machine and

the world became the dominant metaphor of modern era. This radical change was

brought about the new discoveries in physics, astronomy and mathematics known as the

Scientific Revolution and associated with the names of Copernicus, Galileo, Descartes,

Bacon and Newton.

The conceptual framework created by Galileo and Descartes – the world as a perfect

machine governed by exact mathematical laws- was completed triumphantly by Isaac

Newton, whose grand synthesis, Newtonian mechanics, was the crowning achievement

of seventeenth-century science (Capra, 1996).

The mathematical forms of these laws were sufficient to prescribe all of classical

mechanics. It was their rigorous minimalism, more than Newton‘s three principles that

came to characterize the ensuring ―Newtonianism‖.

Somewhat surprisingly one rarely finds the minimalist assumptions behind

Newtonianism spelled out in any detail one exception is by Kempis, Depew and Weber

to formulate the Newtonianism common in terms of fundamental postulates. According

to these authors;

1) Newtonian systems are deterministic

2) Newtonian systems are closed

3) Newtonian systems are reversible

4) Newtonian systems are strongly decomposable or atomistic

bivalent logic

multivalent

logic

fuzzy

logic

truth is bivalent

every proposition is

either true or

false with no intermediate

degree of truth

allowed

truth is

multivalent

almost all

concepts

are bivalent

everything is or

is to allowed to be graduated,

that is, be a

matter of degree

everything is or

is allowed to be

granulated

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5) Newtonian laws are universal (Ulanowicz, 1999)

Descartes and Newton divide human being as body-mind and this view states dualist

view.

3.1. Dualism

Some philosophical problems, but unfortunately not very many, can receive a scientific

solution. We believe that one of these is the problem of consciousness. The central part

of the problem can be stated quite simply. How exactly are conscious states caused by

brain process and how exactly are they released in the brain? The two key phrases here

are ―caused by and realized in‖. In the history of philosophy, this has been the center of

the traditional mind-body problem: how exactly does consciousness relate to the brain

and the rest of the physical world? (Searle, 2007)

3.2. The mind-body problem

The so-called dualist position, as laid out by Rene‘ Descartes in the seventeenth century,

states that there are two separate systems within a human being: a mental thing, the

rescogitians, and a physical thing, the res extansa. Descartes was concerned about how

these two worlds – the mental and physical- talk to one another. His ideas have raised

many deep issues, which together are known as the mind-body problem (Pfeifer,

Bonguard, 2007).

Descartes perceived the mind as mankind‘s heavenly endowment and, in its essence,

distinct from body, the burden of mortality ―The first thing one can know with

certainty‖. Descartes argues that man, ―is a being or substance which is not of all

corporeal, whose nature is solely to think‖, for Descartes, the human intellect was godly

–―doubtless received from God‖. Descartes declared – and was defined by precisely

those characteristics which the human being shared with God.

The body, on the other hand, reflected mankind‘s ―epistemological fallenness‖ rather

than its divinity, and stands ―opposed to reason‖ impediments to pure thought, the

body‘s senses and passions deceive and disturb the intellect. The body is always a

hindrance to the mind in its thinking (Noble, 1999).

3.3. Critique of Descartes’ mind-body dualism

Substance dualism in its most radical form is called Cartesian Dualism, after its most

famous modern preperent, Rene Descartes. Descartes held that the mind (or the self-he

took these to be the same) is an immaterial soul, a substance the essence of which is

consciousness. All of the mind‘s properties are conscious mental states, expressions of

this essence. By contrast all of a material substance‘s properties are physical,

expressions of extension, the essence of bodies, mental and physical substances (and

their properties) are thus radically distinct.

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This is, roughly, the content of Cartesian Dualism –but why believes the view is true?

Descartes was impressed by some of the mental-physical differences listed earlier. But

his most powerful argument for dualism, and the one that has historically received the

most attention, proceeds from the more conceivability of one‘s own disembodied

existence (O‘Conner, Robb, 2003)

Two arguments persuaded Descartes that he could virtually all his normal beliefs. The

first is the argument from dreaming. We believe that we are sitting by the fire with a

piece of paper in my hand. Why? Because my senses tell me so. But could I not be

dreaming? In dreams my senses present me with information of the same kind as I

receive waking. So how do I know that I am not dreaming now?

A similar argument can be mounted for the proposition that I think, which verifies itself

very act of being doubted. Neither ―I think nor‖ I exist expresses a necessary truth: each

might have been false.

We should say that the truth that I exist is self-evident. Descartes wrote rather that it‘s

manifest to the ―natural light‖ of reason (Scruton, 2002).

Descartes has been painted as dividing the mental and the material so clearly that bodily

processes make no real contribution to guiding human behavior. Descartes has been

accused of ‗disdain‘ for the body and seeking ‗liberation‘ from the body ad emotions so

as became a purified or disembodied mind (Hatfield, 2007).

4. Definitions of mind and intelligence

We have some definitions related to Mind, Intelligence and AI related to Dualism and

Mechanistic World View.

4.1. Mind

The mind is a process that emerges from neuronal activity within the brain. The brain is

a machine and the mind is a process that occurs in the brain. It is the mind that sharply

distinguishes the human race from all other species. It is the mind that enables humans

to understand and use language, to manufacture an use tools, to tell stories, to compute

with numbers, and reason with rules of logic (Albus, Meystel, 2001).

Mind Mechanism / Philosophy: By ―mind‖ we mean a system that produces thought,

viewed at a relatively high level of aggregation: say, at or above the level of elementary

process that require 100 milliseconds, or more for their execution. At that level, little or

nothing need be said about the structure or behavior of individual neurons, or even

small assemblages of them. Our units will be larger and more abstract (Simon, 1995).

The Concept of Mind: A Different Approach: To limit the field of mental processes

we must follow the criteria of folk psychology. There are three kinds of mind: human,

animal and mechanical. But the human mind is the paradigm or model of mind

(Martinez-Freire, 2008).

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Philosophy of Mind: David Braddon-Mitchell and Frank Jackson propose that two

approaches are widely used to introduce the philosophy of mind. One approach is

historical: it is given a sketch of the views of some of the great scholars of the past. The

other approach is the one chosen by book. The reader learns about current views and as

a consequence learns only about the concepts that in use nowadays.

The authors argue that we all have an implicit knowledge of the mind, just like we have

an implicit knowledge of grammar of our mother tongue. In the case of grammar, our

implicit knowledge is revealed by our ability to formulate correct sentences and to

recognize incorrect sentences. Our knowledge about mind is revealed by our ability to

predict our own behavior as well as of other people these predictions are stated in terms

of mental states. Hence our commonly used mental terms are a window in to human

mind (Beudewijnse, 2008).

Another review on Dennett’s Book, Writer of Descarte’s Error about Mind:

Dennett is one of the pioneers, is assuming increasing importance in contemporary

cognitive science. A second and equally important aim of the book is to challenge the

Cartesian, anthropomorphic and realist prejudices of the lay public.

Dennett begins, in his deceptively easy style, with a list of questions prompting the

reader to consider which organisms might or might not process minds (Dickins and

Frankish, 1997).

Physics of the Mind: As a physical theory of the mind possible? What kind of physics

would this be? We proceed assuming that the mind and brain refer to the same physical

system at different level of description. This situation is not new to physics. The world

is amenable to understanding at various levels. Understanding searched by physicists is

specific in certain ways: physics is a search for basic laws, a few universal ―first

principles‖ describing a wealth of observed phenomena. Many other ―physics of the

mind‖ and we will attempt to overcome this initial reaction. Some of the reasons for

discomfort are obvious: the mind is perceived as deeply personal, something that no

equation will ever be able to describe, no computer ever be able to simulate. The future

will tell how close a physical theory could come to understand individual minds.

Another reason for skepticism is that the mind the mind is both diverse and

unpredictable, therefore how can it be reduced to few basic laws? Newton nothing

wrong with developing physics of the mind, which he called spiritual substance.

However Newton failed and since then few physicists have dared to approach the

subject. Recently new data, new institutions and new mathematical tools have emerged,

and today we make a new attempt. We seek to identify a few basic principles of the

mind operation formulate these principles mathematically, use them to explain a wealth

of known data, and make predictions that can be tested in the lab (Perlovsky, 2006).

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4.2. Reflections on intelligence: Practical versus theoretical

We define intelligence as the ability of a system to behave appropriately in an uncertain

environment, where appropriate behavior is that which maximizes the likelihood of

success in achieving the system‘s goals. (Albus, Meystel, 2001)

The American Heritage Dictionary defines ―intelligence as the ability to acquire and

apply knowledge‖ (Kugel, 2004).

The goal of creating non-biological intelligence has been with us for a long time, the

nominal 1956 establishment of the field of artificial intelligence by centuries or, under

some definitions, even by millennia. For much of this history it was reasonable to recast

the goal of ―creating‖ intelligence as that of‖ designing‖ intelligence (Spector, 2006).

There are of course risks involved in trying our discussion as closely to the concept of

―Intelligence‖ as is required by our adopted definition of ―technologies‖ as practical

implementation of intelligence. It is worthy surveying the gourd from which we start

when we begin to ask epistemological questions about the kinds of thinking that

technology involves.

The definitions of intelligence are below:

a) Intelligence comes in Degrees and in a variety of styles

b) Intelligence is not unique to Human Beings

c) Intelligence relates to the capacity for Flexible Response

d) Intelligence varies with Speed of Response

e) Intelligence varies with fineness of Discrimination

f) Intelligence varies with Remoteness of Inference

g) Intelligence varies with synaptic Power

h) Intelligence varies with Effectiveness in Achieving Appropriate Goals

i) Intelligence Relates to Appropriateness of Goals themselves.

Practical Intelligence in action when we watch a rat learn the way through a maze: we

acknowledge theoretical Intelligence when we follow someone‘s elegant mathematical

proof. What do these forms of intelligence have in common, and what are their

differences?

One common feature is that both are purposive. The general purpose of practical

intelligence is to survive or thrive: the general purpose of theoretical intelligence is to

know or understand. Other basic similarities consist in the extent to which both practical

and theoretical intelligence are found in degrees of greater and less (Ferre, 1998).

4.3. Artificial intelligence : as an intelligent machine

Mc Charthy, father of AI defines AI and intelligence like that: AI is the science and

engineering of making intelligent machines, especially intelligent programs. It is related

to similar task of using computers to understand human intelligence, but AI does not

have to confine itself to methods that are biologically observable.

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And intelligence is the computational part of the ability to achieve goals in the world.

Varying kind and degrees of intelligence occur in people, many animals and some

machines.

The answer of to ―Isn‘t there a solid definition of intelligence that doesn‘t depend on

relating it to human intelligence?‖ is that according to him: The problem is that we

cannot yet characterize in general what kinds of computational procedures we want to

call intelligence. We understand some of the mechanisms of intelligence and not others

(McCarthy, 2007).

4.4. Artificial intelligence: an empirical science

Herbert Simon’s View on AI: H.A. Simon one of the pioneers of AI and Nobel price

winner defines AI as empirical science.

AI deals with some of the phenomena surrounding computer, hence is a part of

computer science. It is also a part of psychology and cognitive science. It deals, in

particular with phenomena that appear when computers perform tasks that, if performed

by people, would be regarded as requiring intelligence-thinking.

Artificial Intelligence began in 1950s as an inquiry in to the nature of intelligence. It

used computers as a revolutionary tool to simulate, indeed exhibit intelligence, thereby

providing a means for examining it in utmost detail. ―B.C.‖ means before computers,

the only observable examples of intelligence were the minds of living organisms,

especially human being. Now the family of intelligent systems had been joined by a

new genus, intelligent computer programs (Simon, 1995).

Simon‘s work was motivated by the belief that neither the human mind, human

thinking, and decision making nor human creativity need be mysterious. His life work

was devoted to proving this point. His motto was ―Wonderful, but not

incomprehensible‖.

He helped create ―thinking‖ machines that Simon came to understand human intuition

as subconscious pattern recognition.

Intuition is often described by what it not: intuition is a residual concept. The most

common terms used for intuition reveal intuition‘s residual nature: gut feeling, educated

hunch, and sixth sense.

Simon‘s view of thinking affected by AI is that thinking is a form of information-

processing. Both human thinking and information-processing programs perform three

similar operations: they scan data for patterns, they store the patterns in memory and

they apply the patterns to make inferences or extrapolations. AI also led him (human

thinking closely parallel the operations) to conclude that intuition is a subset of

thinking.

Machines who ―think‖: Simon‘s machine think in that recognize patterns and apply ―if-

then‖ in making decisions (Frantz, 2003).

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It is important to specify what we mean by using the expression artificial intelligence

(AI). There are at least two different views about AI the first one quotes AI to the

science of the artificial, or the science of designing and building computer-based

artifacts performing various human tasks (Pomerol , 1997).

Definition of AI by M. Minsky: Artificial Intelligence is the science of making

machines to things that would require intelligence if done by men.

A visitor to our planet might be puzzled about the role of computers in our technology.

On the one hand, he would read and hear all about wonderful ―mechanical brains‖

baffling their creators with prodigious intellectual performance. In he (or it) warned that

these must be restrained, less they overwhelm us by might, persuasion or even by the

revelation of truths too terrible to be borne. On the other hand, our visitor would find

the machines being denounced, on all sides, for their slavish obedience, unimaginative

literal interpretations and in capacity for innovation or initiative, in short, for their

human dullness (Minsky, 1963).

As we known that AI have two approaches of point of capabilities: Strong AI and Weak

AI.

Strong and Weak AI: Can Machines act intelligent: The assertion that machines that

do so actually thinking is called the strong AI hypothesis.

Some philosophers have tried to prove that AI is impossible: that machines cannot

possibly act intelligently some have used their arguments to call-for a-stop to AI

research. (Russell and Norvig, 2003).

Limitation of AI: We know that a computer could have certain capabilities of the

human mind. Naturally, people want to know how far computer systems can go in this

direction. Can a computer be given all mental capabilities, or there are certain

limitations that a computer cannot pass, no matter how it is designed? When Turing

argued for the possibility of the intelligent machines, he did not believe in any such

limitation, and he used a large part of the paper to refute several claims of limitation he

anticipated.

Since then, the debates on the possibility of thinking machines, or the limitations of AI

research, have never stopped.

These debates have been focused on three claims:

An AI system is in principle an axiomatic system.

The problem solving process of an AI system is equivalent to a Turing machine.

An AI system is formal, and only gets meaning according to model theoretic

semantic (Wang 2006).

Contemporary artificial intelligence (AI) can be viewed as assays in the epistemology of

androids, an exploration of the principles underlying the cognitive behavior of any

possible kind of mechanical agents. Occasional hyperpole and flimflam aside, artificial

intelligence is wonderful subject, full of new ideas and possibilities, unfettered by

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tradition or concern (other than inspirational) for the accidents of human constitution,

but disciplined by the limits of mechanical computation. More than other new sciences,

AI and philosophy have things to say to one to another: any attempt to create and

understand minds must be of philosophical interest. In fact, AI is philosophy, conducted

by novel means (Glymour, et al 2006).

Questions Currently Latent in Artificial Intelligence: Here we have identified two

questions which lie beneath the surface of the pluralistic AI of today.

The first question, to rephrase, ask why we do not have a mathematical theory of the

perception-action cycle. Of course there is work on active perception on sensory-motor

coordinate systems and engineering department robotics is full of mathematics. But the

kind of theory I mean is one that is as universally useful for characterizing cyclic

systems as Shannon‘s information theory is characterizing communication channels.

Implicit in this is the second question. What would we want such a post-Shannon

system to do? What quantity should a perception-action cycle system maximize?

A third question was directed at AI researchers by Penrose and by the hostility and

controversy it caused, you know he had hit a weak spot in AI Penrose wondered if the

fact that physical substrate of the world, of which relativity and quantum mechanics are

our best accounts, might be sufficiently different from the digital substrate of computers

that it would render AI impossible. Is there something in the quantum that is necessary

for mind? (Bell, 1999)

May be we will never manage to build real artificial intelligence. The problem could be

too difficult for human brain over to solve (Bostrom, 2003).

Even in case scientists underline advantages of human over machine, human

intelligence is often approached by them unilaterally (Tikhemirov, 1975).

5. Human intelligence versus machine/artificial intelligence

5.1. Machine-human intelligence

We stand on the threshold of the most profound and transformative event in the history

of humanity, the ―singularity‖.

What is the Singularity? From Kurzweil perspective, the singularity is a future period

during which the pace of technological change will be so fast and far-reaching that

human existence on this planet will be irreversibly altered. We will combine our brain

power –the knowledge, skills, and personality quirks that makes us human- with our

computer power in order to think, reason, communicate and create. In ways we can

scarcely even contemplate today.

This merger of man and machine, coupled with the sudden explosion in machine

intelligence and rapid innovation in gene research and nano-technology, will result in a

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world where there is no distinction between biological and mechanical, or between

physical and virtual reality. These technological revolutions will allow us to transcend

our frail bodies with all their limitations (Kurzweil, 2000).

5.2. Definitions of MI (Machine Intelligence)

A fundamental problem in artificial intelligence is that nobody really knows what

intelligence is. The problem is especially acute when we need no consider artificial

systems which are significantly different to humans. Legg and Hutler approach this

problem in the following way: they take a number of well known informal definitions of

human intelligence that have been given by experts and extract their essential features.

These are then mathematically formalized to produce a general measure of intelligence

for arbitrary machines. They believe that this equation formally captures the concept of

machine intelligence in the broadest reasonable sense (Legg and Hutler, 2007).

There are fundamental differences between the human intelligence and today‘s machine

intelligence. Human intelligence is very good in identifying patterns and subjective

matters. However, it is usually not very good in handling large amounts of data and

doing massive computations. Nor can it process and solve complex problems with large

number of constraints. This is specially true when real time processing of data and

information is required. For these types of issues, machine intelligence is an excellent

substitute. In general human thinking is based on pattern recognition (PR), not fast

logical analysis (LA), and we are very good at PR but slow at LA compared to a

machine (Aminzadeh, 2005).

One of the most fundamental problems in artificial intelligence –the ―frame‖ problem.

Traditional artificial intelligence methods are based around logic programming which

retain their rigor on the assumption that logical thoughts are true eternally – this is

theorem proving. Humans use emotional coloring to select courses of action. They are

capable of utilizing a sophisticated language for the communication of complex

concepts (Ellery, 2003).

Mind and Human Intelligence: Cognitive scientist and philosopher, Andy Clark, picks

up this (the idea that a living entity can be hybrid of both organic matter and mechanical

pasts) general theme and presents an empirical and philosophical case for the following

inextricably linked theses:

1. The human mind is naturally disposed to develop and incorporate tools,

2. Human have always been to a greater or lesser degree cyborgs (Marsh, 2005).

Dick‘s argument is simple, but firmly founded in the naturalistic evolutionary

worldview. The over arching argument may be stated as follows: Advanced

intelligence-defined as at least at the level of homosapiens- implies culture indeed some

consider culture part of very definition of advanced intelligence (Dick, 2007).

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5.3. Artificial human nature

Artificial Intelligence (AI) critics repeatedly ask whether human can be placed by

machines: can ―human nature‖ be duplicated by machines and if so are humans than just

a special sort of machine? By examining the present and history of AI criticism it is

possible to identify moments where specific critics have fixated and particular qualities

as the ―essential‖ qualities of ―human nature‖. Reason, perception, emotion and the

body are four qualities that have been championed by AI critics (and proposrents) as

―essential‖ and (un)implementable as hardware or software machinery (Sack, 1997).

Nowadays, it is widely accepted that general purpose of artificial intelligence (AI) is to

develop (1) conceptual models (2) formal rewriting processes of these models and (3)

programming strategies and physical machines to reproduce as efficiently and

thoroughly as possible the most authentic, cognitive, scientific and technical tasks of

biological systems that we have labeled Intelligent (Miran,2008).

Machines as Intelligent as humans should be able to do most of things human can do.

Humans can ―think‖ and so also should any machine processing human-level

intelligence.

Machines exhibitions true human-level intelligence should be able to do many of things

humans are able to do (Nilsson, 2005).

6. Critique of artificial intelligence

No computers actually function like human mind. The human mind does not depend on

formal or logic rules ascribed to computer. Thus, symbolic AI research has falsified the

rationalist assumption that ―the human mind reaches certainty by functioning formally‖

by virtue of its failure to create a thinking machine.

The Immortal Mind: Artificial Intelligence: If intelligent machines were viewed as

vehicles of human transcendence and immortality, they were also understood as having

lives of their own and an ultimate destiny beyond human experience. In the eyes of AI

and visionaries, mind machines represented the next step in evolution, a new species,

Machine sapiens, which would rival and ultimately supersede Homo sapiens as the most

intelligent beings in creation.

―The manifest destiny of mankind is to pass the torch of life and intelligence on to the

computer‖ Rucker proclaimed. Yet three and a half centuries after Descartes first

dreamed of releasing the immortal mind from its mortal mornings. A-Lifers were still

wrestling with the enigma of the Christian soul, the divinity of man new boldly being

passed forward to its mechanical progeny (Noble, 1999).

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6.1. Dreyfus and the critique of artificial intelligence

Hubert Dreyfus, an American follower of Heidegger, has given what is perhaps the

most influential application of the phenomenological approach to technology in his

critique of artificial intelligence. Dreyfus argued that classical AI (Artificial

Intelligence) was based on mistaken assumption about thinking and meaning that were

shared by early modern philosophers such as Descartes and the British empiricists.

Dreyfus argues that the rules at the basis of thinking cannot be fully formalized or made

fully explicit. Behind our capacity to judge and reason are tacit or prereflective

orientation. Deryfus claims that the human rationality involves the ability to apply rules

to particular contexts in a manner that cannot be fully formalized.

According to Dreyfus, thought and intention assume a body, not the mechanical body,

as described by physics and chemistry, but a ―lived body‖, in the sense of Maurice

Merleau-Ponty (1907-61), a follower and a developer of the ideas of later Husserl. The

―lived body‖ is notion different from that of the pure mind as a spiritual or mental

entity, and also different from the body considered as mechanical body of physics

(Dusek, 2006).

Using Heidegger as a guide, Searle began to look for signs that the whole AI research

program was degenerating. I was particularly stuck by the fact that, among other

troubles, researchers were running up against the problem of representing of

significance and relevance – a problem that Heidegger saw was implicit in Descartes‘

understanding of the world as a set of meaningless facts to which the mind assigned

what Descartes values, and John Searle now calls functions.

But Heidegger warned, values are just more meaningless fact (Dreyfus, 2007).

Dreyfus contends that it is impossible to create intelligent computer programs analogous

to the human brain because the workings of human intelligence are entirely different

from that of computing machines. For Dreyfus, the human mind functions intuitively

and not formally. Dreyfus‘s critique on AI proceeds from his critique on rationalist

epistemological assumptions about human intelligence. Dreyfus‘s major attack targets

the rationalist conception that human understanding or intelligence can be ―formalized‖.

Via Descartes and other nationalist or intellectualist thinkers, this was later extended to

the realm of the natural sciences and attained a very high explanatory power. And it is

this success that has in turn reinforced the idea that in any ordently domain there must

be some set of context-free elements and some abstract relations among those elements

that account for the order of that domain and for man‘s ability to act intelligently in the

Dreyfus‘s critique on AI is basically a critique on the rationalist or broadly speaking, the

intellectualist perspective that forms the view that we can formulate human thinking in

terms of proposition or primitives (Kenaw, 2008).

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6.2. Weizenbaum’s critique of artificial intelligence

Joseph Weizenbaum, as a AI pioneer and a critic of AI, ―Computer power and human

reason‖ is a mosaic of well-reasoned analysis and passionate pleading on the nature of

computers and of man, and about the place that computers (read ―technology‖ if you

wish) should have in human affairs.

Weizenbaum‘s may still be too much technocrat: witness his oversimplifications of

social movements as the immediate fruits of technical innovations.

Weizenbaum makes a conscientious effort to distinguish his assertions of faith from the

scientific consensus; but the non-specialist reader will still have to look closely to be

sure. As Weizenbaum insists. There are few things less well understand than human

creative imagination.

His own prophecy is that this will NEVER be emulated to any significant measure by

computing machines. This hypothesis is beyond the range of scientific criticism, short

of tangible advances to much to hope for right away; but his arguments are mainly

repetitious assertions of his personal faith.

The abrogation of human responsibility for moral decision whether it is be out of lazy

delegation to machines, or superstitious deference to super-human abstraction can

indeed once again ignite the holocaust.

Weizenbaum is particularly of the critical of the use of computer in the role of

psychotherapy—doubtless in consternation that the machine‘s patrons believed they

were talking to a sympathetic, understanding ‗person‘.

The abuses might be either ideological or technological. If human intelligence were

more successfully mirrored in the machine, will that not justify treating human beings

as if they were mere machines? (Lederbeg, 1976)

6.3. Penrose’s critique of artificial intelligence

Machine consciousness has also criticized by Penrose, who claims that the processing of

an algorithm is not enough to evoke phenomenal awareness because subtle and largely

unknown physical principles are needed to perform the non-computational actions that

lie at the roots of consciousness. ―Electronic computers have their undoubted

importance in clarifying many of the issues that relate to mental phenomena (perhaps, to

a large extent, by teaching us what genuine mental phenomena are not)… computers,

we conclude, do something very different from what we are doing when we bring our

awareness to bear upon same problem‖ (Gamez, 2008).

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6.4. Other controversies about artificial intelligence

Throughout history, developments in the sciences have caused people to change their

views of man and his place in the universe. The Copernican Revolution placed man on a

planet, adrift in space;

Darwinian revolution changed our view of human origins. Computers, too raise

questions about the nature of man: Can computers think the way we do, and, if so are

we like them- just thinking machines? Of course the question ―can a machine think?‖

has been raised before, as long ago as seventeenth century: by Descartes and Pascal,

who said no; by Hobbes: who said that thought was mechanical: and somewhat later by

La Mettrie, who saw man himself as a machine (Goertzel, 2007).

7. Gödel’s theorem; towards to Incompleteness, uncertainty, logic and human

reasoning

But only in our century, as a result of work in the mathematical sciences, has the

question ―can a machine think?‖ been given widespread and rigorous discussion. Today

computer scientists have devised programs that solve problems which, it solved by

people, would seem to require intelligent thought. This point is made explicit by Josh

McCarthy‘s name for the field ―Artificial Intelligence‖. As we shall see, the real

controversy about artificial intelligence (AI) is not about the nature and program, it is

about nature of man.

But first, how would we possibly decide whether computers can think at all? Alan

Turing suggested a way of testing the assertion that a machine could think.

Kurt Gödel had provided that there were limitations to the power of computers; any

reasonably rich formal system is incomplete and the consistency such a system cannot

be proved within the system.

J.R. Lucas, repelled by the idea that people are just instances of formal system, refused

to accept Turing‘s response to the Gödel-based objection (Grabiner, 1986).

Gödel’s theorem related to mind-body problem: Gödel‘s theorem defeats rationality

because it shows that logical systems that are rich enough to permit self reference are

incomplete, in the sense of not providing answers to all of the questions that they can

pose. By Gödel‘s theorem, in any sufficiently rich logic same state able theorem (and

indeed, some true theorem) can neither be proved true or false. Moreover, in such logics

no general procedure can be devised that will decide whether any particular question

that is presented is decidable (Simon, 2000).

For many, it is still hard to conceive how the World of Subjective experience spring out

of merely physical events. This problem of qualia is the hardest and the main part of the

mind-body problem.

The problem is often summed up in the following question: How matter (i.e. body and

brain) becomes mind?‖ All sorts of dualists think it never does and some of them, like

70

Lucas and Penrose, think that Gödel‘s incompleteness theorem proves that. Their main

argument is that Gödel‘s theorem implies man-machine non-equivalence in the

following sense:

There is no machine which could capture all our mathematical intuitions (Šikič, 2005).

For many decades now it has been claimed that Gödel‘s two incompleteness theorems

preclude the possibility of the development of a true artificial intelligence which could

rival the human brain.

Gödel himself realized that the incompleteness theorems do not preclude the possibility

of a machine mind. In fact there is an interesting argument posed by Rudy Rucker

where he shows that it is possible to construct a Lucas style argument using

incompleteness theorems which actually suggests the possibility of aerating machine

minds (Sullins III, 1997).

Logic and Artificial Intelligence: Towards to Fuzzy AI: Throughout its relatively

short history, AI has been heavily influenced by the logical ideas. AI has drawn on

many research methodologies: the value and relative importance of logical formalisms

is questioned by some leading practioners, and has been debated in the literature from

time to time. But most members of the AI community would agree that logic has an

important role to play in at least some central areas of AI research, and on influential

minority considers logic to be the most important factor in developing strategic,

fundamental advances.

Logic is used in understanding problems intelligent reasoning and guiding the design of

mechanical reasoning systems.

Theoretical computer science developed out of logic, theory of computation (if this to

be considered a different subject from logic), and some related areas of mathematics

(SEP, 2003).

One of the most important contributions of artificial intelligence has been realization of

the importance of knowledge in the performance of many human tasks. This realization

has led AI researchers of concentrate on the issue of knowledge representation.

Attempts to represent human knowledge by AI researchers have led to development of

large number of clever paradigms for this purpose. However, in implementing these

paradigms the restriction to binary logic greatly reduced their power.

The use of fuzzy sets via the theory of approximate reasoning provides a very powerful

for extending the capability of binary logic in ways that enable a much better

representation of human knowledge (Yager, 1997).

Perhaps, the greatest success of artificial intelligence has been the expert system

paradigm. The typical structure of an expert system involves a collection rules, called

the rule base, which describe the knowledge about the domain in which expert system

works. There is a close parallel between fuzzy logic controller and expert system.

71

8. Uncertainty and fuzzy logic

There is a deep-seated tradition in science of dealing with uncertainty. Whatever its

form and nature-through the use of probability theory. Successes of this tradition are

undeniable. But we move further into the age of machine intelligence and automated

decision-making, a basic limitation of probability theory becomes a serious problem.

More specifically, in large measure, standard probability theory, call it PT, cannot deal

with information described in natural language: that is to put it simply, PT does not

have NL-capability (NL-Natural Language)

Uncertainty is an attribute of information. The path-breaking work of Shannon has led

to a universal acceptance of thesis that information is statistical in nature.

Concomitantly, existing theories of uncertainty are based on probability theory. The

generalized theory of uncertainty (GTU) departs from existing theories in essential

ways. First, the thesis that information is statistical in nature is replaced by a much more

general thesis that information is generalized constraint, with statistical uncertainty

being a special, albeit, important case. Equating information to a generalized constraint

is the fundamental thesis of GTU. Second, bivalence is abandoned through GTU, the

foundation of GTU is shifted from bivalent logic to fuzzy logic. As a consequence, in

GTU everything as or is allowed to be a matter of degree, or equivalently, fuzzy (Zadeh,

2006).

Among problems faced by knowledge engineering is that mechanizing inference and

decision. One reason why this problem is difficult is the existence of several facets to

uncertainty, and the discovery that traditional tools for representing uncertainty, such as

error interval analysis and probability theory are not able to grasp separately all facets of

uncertainty (Dubois, Prade, 1998).

Haziness, vagueness, fuzziness and classical logic: The theory of fuzzy sets in a

mathematical theory to deal with vagueness and other loose concepts, lacking strict

boundaries. It seems that ―vagueness‖ as it has been used in philosophy and logic since

the 20th

century, may be formalized by fuzzy sets, whereas ―haziness (a micro

geometrical approach) like other scientific concepts. E.g., interminancy is a concept that

needs to be formalized by probability theory and statics. Nevertheless, fuzzy

mathematics cannot be possibly be imagined without the use of t-norm and t-conorms

(Seising, 2008).

Fuzzy logic is an approach to computer science that mimics the way a human brain

thinks and solves problems. The idea of fuzzy logic is to approximate human decision

making using natural language terms instead of quantitative terms.

Interestingly, fuzzy science started in the questioning minds of philosophers. Confused

and inquisitive, from Buddha to Aristotle to Plato these ancient philosophers were

constantly searching for a ―rule of law‖ beyond true or false.

Fuzzy logic comes in when conventional logic fails. Fuzzy logic can deal with virtually

any preposition expressed in natural language.

72

Fuzzy logic, by exploring uncertainty and unpredictability, continues to shape the world

in which we live (Bih, 2006).

Fuzzy logic not only deals with problems at the technological side of computational

intelligence. Since what a fuzzy set does represent is a concrete use of predicate (or

linguistic label), and as Wittgenstein assented ―the meaning of a word is its use in the

language‖, fuzzy logic also deals with what is as the Gordian Knot of computational

intelligence, the problem of meaning, and this is a side of fuzzy logic that, in the way

towards computing with words, seems to be a great interest (Trillas, 2006).

It is well known that logic is the study of the laws of thought that govern the operation

of our mind. In general, a logical system consists of syntax and semantics, i.e., ―a

formal system for description of states of affairs and a proof theory for deducing the

entailment of a set of sentences. All the classical logic, fuzzy logic, and other non-

classical logics are efficient tools that have been created by people to model statements

about all kinds of things and the interactions among objects in the real world we are

living in (Ma, et al, 2006).

As fuzzy theorists and practioners, we frequently find our self confronting significant

philosophical issues in our work. Indeed if we are not doing so, we are probably and

possibly missing out a lot. While different fuzzy theories and application approaches

may be founded upon different set of philosophical presuppositions, all such theories

rest upon some epistemological and ontological assumptions, whether explicitly

acknowledged or not.

A lack of appropriate treatment of the philosophical grounding creates a situation of

discord, or not least a level of misunderstanding, between fuzzy theorists and

practioners on the one hand and crisp theorists and practioners on the other (Türkşen,

2006).

Fuzzy set theory provides a means for representing uncertainties. Historically,

probability theory has been the primary tool for representing uncertainty in

mathematical models. Because of this, all uncertain was assumed to follow the

characteristics of random uncertainty. (Ross, 1956)

Our understanding of physical processes is based largely on impressive human

reasoning. This imprecision (When compared to precise quantities required by

computers) is nonetheless a form of information that can be quite useful to humans. The

ability to embed such reasoning in hitherto intractable and complex problem is the

criterion by which the efficacy of fuzzy logic is judged. (Ross, 1995)

The generalized theory of uncertainty (GTU) differs from other theories in three

important respects. First, the thesis that information is a generalized constraint, with

statistical uncertainty being a special, albeit important case. Second, bivalence is

abandoned though out GTU, and third, one of the principal objectives of GTU is

achievement of NL-capability (Zadeh, 2006).

73

8.1. Defending fuzzy logic / specific aspects of human reasoning

Bart Kosko in his book: Fuzzy Thinking; The New Science of Fuzzy Logic, says that

the central thesis is that everything is a matter of degree. The world is grey, not black

and white. But western scientists and philosophers have refused to face up to this fact:

they persist in describing the grey world in black and white language. Their doing so is

what author calls the ―mismatch problem‖ a problem rooted in uncritical acceptance of

two-valued logic- binary faith. Binary logic says Kosko, sacrifices accuracy for

simplicity. Bivalence is a rounding off that works fine at extremes but fails everywhere

else. Indeed, the core principles of bivalent logic-the law of Excluded Middle and the

principle of Non-Contradiction- are merely limiting cases of a more proper multi-valued

logic (Kosko, 1993).

Kosko derived the conditional probability operator from first principle of fuzzy logic

and this proved that probability is a subset of fuzzy logic. Fuzzy logic is more effective

in representing complex causal mechanisms. The vertices and dimension of probability

are subsets of the ―fuzzy hypercube‖ fuzzy logic are thus the larger science (Halgeson

and Jobe, 1998).

It is well known that logic is the study at the laws of thought that govern the operation

of our mind. In general, a logical system consists of syntax and semantics. All classical

logic, fuzzy logic and other non-classical logics are efficient tools that have been

created by people to model statements about all kinds of things and the interactions

among objects in real world we are living in. Fuzzy logic admits existence of the

intermediate states and assumes that each statement has a degree of truth ranging from

0-1 Human thinking is based on knowledge obtained from external nature after

thousand-of-years‘ practicing and accumulating, which inherently is accompanied by

various kinds of uncertainty existing not only in the real world but also in the course of

outer information being reflected by human brain. The uncertainty comes into being

from the limitation of human recognition and in the course of information processing.

Fuzzy logic admits intermediate states of affairs and adepts a relatively flexible ―self

matching‖ mechanism of inference that is convenient in the analysis and application of

uncertainty in natural way.

Classical as well as non-classical logics, including fuzzy one, are all investigations of

the world from different philosophical backgrounds and methodologies. All these logic

have special features suitable for particular problems.

What is artificial intelligence? People with different backgrounds give different

answers. For us, artificial intelligence is the study of giving machines ability to think in

human way and acting a people, i.e. –to develop intelligence solve complex real

problems to realize machine, intelligence. Concretely, the goal of machine intelligence

is to give machines the ability of learning knowledge from the changing environment

and circumstances, to represent knowledge in appropriate and tractable forms, to make

proper decision using knowledge, and applying knowledge to resolve complex problems

in the real world. To achieve this aim, fuzzy logic can be taken as an alternative to the

classical logic (Ma et al, 2006).

74

The foundations fuzzy logic: The foundations fuzzy logic have became firmer and its

impact within the basic sciences- and especially in mathematical and physical sciences-

has become more visible and more substantive. And yet, there are still many

misconceptions about the aims of fuzzy logic and misjudgments of its strengths and

limitations.

One of the common misconceptions is rooted in semantics: as a label, fuzzy logic, FL,

has two different meanings. More specifically, in a narrow sense fuzzy logic, FLn, is a

logical system which aims at a formalization of approximate reasoning.

In a wide sense, fuzzy logic, FLw, is a coexistence with fuzzy set theory, FST. FLw is

far broader than Fln and contains Fln as one of its branches (Zadeh, 1999).

There are many misconceptions about fuzzy logic. Fuzzy logic is not fuzzy. Basically,

fuzzy logic is a precise logic of imprecision and approximate reasoning. More

specifically, fuzzy logic may be viewed as an attempt at formalization/mechanization of

two remarkable human capabilities. First, the capabilities to converse, reason, and make

rational decisions in an environment of imprecision, uncertainty, incompleteness of

information, conflicting information, partiality of truth and partiality of possibility- in

short, in an environment of imperfect information and a second, the capability to

perform a wide variety of physical and mental tasks without any measurements and any

computations.

Fuzzy logic is much more than a logical system. It has many facets. The principal facets

are: logical, fuzzy set-theoretic, epistemic and relational (Zadeh, 2008).

9. Epilogue

Regarding some futurists in 21st century and beyond our world would be probably more

imprecise and complex. ―The End of History‖s writer that in our past in our post human

future, Francis Fukuyama places bioethical problems into context by first explaining the

science behind the issues and then exploring the many way these issues (class

stratification, psycho tropic drug impact, and life expectancy) might affect society and

politics (Fukuyama, 2002). Jeremy Rifkin‘s ―The Biotech Century‖ discussed many of

the biological processes, technologies, moral dilemmas, and political issues that new

face humanity for first time (Rifkin, 1999). Michio Kaku future scientists deal with

more different approaches for visionary sights in his book, ―Visions of the Future‖ deals

with three revolutions in the 21st century: The Intelligence Revolution, The Biotech

Revolution and The Quantum Revolution (Kaku, 1999).

Another fact are chaotic structures and systems. Nature, being composed of all of these

things, will always have novelty and beauty that can never be exhausted. As with the M-

set, we can appreciate nature‘s beauty precisely because we can simulate it, but only to

limited accuracy. If all natural phenomena were either perfectly describable or

absolutely indescribable, not only would they uninteresting, but life would be

impossible (Flake, 1999).

75

AI, man, machines and loves (Critique of AI by a film) and fuzzy poem

AI: Artificial Intelligence is Steven Spielberg‘s epic tribute to Stanley Kubrick.

Although written and directed by Spielberg, the idea behind the film was conceived by

Kubrick. And what an austere vision it is. The intrinsic desire far artificial intelligence

in western civilization is nothing more than an attempt to eternalize its presence, to

ensure that we accept its power as a natural phenomenon.

In the final analyses, AI represents a colossal failure of imagination. Indeed, it sees

imagination itself a commodity to be distributed and consumed like all other

commodities: Its vision of the future is totally one-dimension and totally colonized by

white man and their alienating technologies (Komninou, 2003).

Final result, the humanity would be needed immortal or eternal mind and soul or as

quoted philosophers we would needed poetical beauty, absolute truth goodness. Overall

these concepts are not precise.

What could fuzzy logic possibly to with a quote a 13th

century Sufi poet? Fuzzy logic

can be an extremely versatile and flexible to with which to model systems that are

complex, vague and imprecise for new trust that the whole idea of going beyond right

and wrong. True and false is what fuzzy logic is all about. Fuzzy logic (like Rumi, I

suppose) is not a frequent topic around break tables at psychological conferences

(Mathe, 2002).

A voice and in interpretation about fuzzy word from Mevlana Jalaluddin Rumi (1207-

1273):

“Out beyond ideas of wrong-doing and right-doing, there is a field, I’ll meet you there.

When the soul lies down in that grass, the world is too full to talk about. Ideas,

language, even the phrase each other doesn’t make any sense.”

Rumi

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