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  ...presents...     Can There Be Artificial Intelligence?

                                                         by Tequila Willy



                      >>> a cDc publication.......1994 <<<

                        -cDc- CULT OF THE DEAD COW -cDc-

  ____       _     ____       _       ____       _     ____       _       ____

 |____digital_media____digital_culture____digital_media____digital_culture____|



     Since the dawn of history, men have dreamed of other forms of intelligent

life.  There is something within the nature of mankind to reach out, to become

like gods.  Today, in our technologically advanced society, the potential is

right around the corner.  Even in this modern world of technological marvels,

there are many hurdles to overcome.  If the technological boundaries are

overcome, there are still those who believe that no man-made device will think

like a human being.  The doubts of the unbelievers should fade from the

memories of the human race as the first machines begin to think.



     Philosophers and scientists alike have been questing for artificial

intelligence.  It has only been in this century that the goal has been at least

feasible.  When looking for artificial intelligence, a researcher must look

inward before starting anything else.  The human mind and soul are two things

that we have very little knowledge of.  The way our brains work, and why we are

able to think are some of the most important things in our society.  When we

are born, we are self-aware, and it is a general belief that self-awareness is

only possible in humans because of the way we are born.  In fact, most people

never pay any attention to what happens during the gestation period.  It has

been shown that once the brain is developed, it immediately starts to process

information.  It cannot be proven in either direction that there is self-

awareness at that state.  It could turn out, in the end, that computers

designed for thought will have to go through a gestation process, and learn

just like a child.  The opposition to the theory of artificial intelligence,

and the arguments against AI have helped to move research ahead by pointing out

flaws in AI theories.



     There is a lot of technical material presented below, so there are some

terms that should be explained before continuing.  The first term is serial

computer, which is a computer that is distinguished by its capability to handle

only one operation at a time.  The second term is parallel processing, which is

a method of computing where more than one operation can be handled at a time

(Churchland and Churchland 35).  For example, give a task to two computers, one

parallel processing, and one serial processing; the serial computer attacks the

problem one step at a time, taking a large amount of time while the computer

that is capable of parallel processing breaks the task down into simpler

operations and then executes the task concurrently with other nodes of the

processor.  The result is that the parallel processor, being able to do many

things at a time, finishes the task in a fraction of the time.  The third term

that is used often is a computing architecture known as neural networks.  A

neural network is a system of processors, or nodes of a processor, linked to

other nodes in the way neurons in the brain are connected (35).  The way that

the neural network works is that the strength of the connections made from the

input of the network to the output of the network allows a more humanlike

ability to operate in a more than binary basis (35).  To clarify, a human

neuron is capable of firing its electrical charge at many different levels,

with each level signifying a different thing (36).  This allows a greater

variety in the amount and variety of information that the computer can pass

along.  The next term used in this paper is classical artificial intelligence

(or AI for short).  Classical artificial intelligence was the school of AI that

felt that given a powerful enough computer and the properly crafted programs,

you could get a machine that would be able to think (34).



     John R. Searle, in his essay from the _Scientific American_ from January

1990, writes that machines, no matter what the power, or their internal

architecture will not be able to think.  His main argument is what he calls his

Chinese room experiment (Searle 26).  The experiment goes like this: first you

lock some person in a room where there is a door with two mail slots, one in

and one out.  Into the room now and then come a pile of Chinese symbols in

through the slot.  The person inside the room also has a rule book that

explains, in a language he understands, what he should do with the symbols

coming into the room, and having used the rule book to manipulate the symbols,

he drops the rearranged symbols down through the out slot (26).  His point,

using this example, is that if he does not understand Chinese because of

running a computer program for understanding Chinese, then another computer

wouldn't either.  This means that simply manipulating symbols isn't enough to

create cognition, or thinking, therefore, according to him, making it

impossible for a computer to think (26).



     He then breaks down his arguments into axioms that he draws his

conclusions from.  The first axiom is this: "Computer programs are formal

(syntactic)" (27).  Syntactic means purely formal.  He explains the axiom

further by an example, "A computer processes information by first encoding it

into the symbolism that the computer uses and then manipulating the symbols

through a set of precisely stated rules.  These rules constitute the program"

(27).  Before introducing his second axiom he points out that symbols and

computer programs are abstract entities.  In computers the symbols can stand

for anything the programmer wants.  So, according to Searle, the program has

syntax, yet it doesn't have semantics.  This leads to his next axiom, which is:

"Human minds have mental contents (semantics)" (27).  His third axiom is this:

"Syntax by itself is neither constitutive of nor sufficient for minds" (27).

His explanation of that axiom is quite simple.  He says that merely

manipulating symbols is not enough to guarantee knowledge of what they mean.

Later in his paper he poses another axiom, "Brains cause minds" (29).  In other

words that thought is dependent on the biological processes of the human brain.



     The first conclusion that he draws from his axioms is "Programs are

neither constitutive of nor sufficient for minds" (27).  This conclusion is

pretty clear, saying that computers are incapable of having minds.  The second

conclusion is: "Any other system capable of causing minds would have to have

causal powers equivalent to those of brains" (29).  His example of the

conclusion states that for an electrical engine to drive a car as fast as a gas

engine the electrical engine must produce an energy output at least as high as

a gas engine (29).  His third conclusion is that "Any artifact that produced

mental phenomena, any artificial brain, would have to be able to duplicate the

specific causal powers of brains, and it could not do that by simply running a

program" (29).  The fourth conclusion that he draws from his axioms is this:

"The way that human brains actually produce mental phenomena cannot be solely

by virtue of running a computer program" (29).



     The argument presented by John M. Searle is quite formidable, with his

Chinese room example, and then the arguments that he goes on to present.  Some

of the conclusions and axioms, however, although they look sound at first, are

deceptively untrue.  An analysis of the arguments will show that they are

faulty.



     First, Searle's Chinese room example only applies to symbol-manipulating

computers.  In S-M machines the prospect of one ever being able to think is

highly doubtful, only because their architecture is incomparable to human brain

structure.  The human brain is the only thing we know to definitely possess

intelligence.  The problem with Searle's Chinese room example, at least in

reference to parallel processing and neural networked machines is that they

don't work the way that S-M machines work.  They use a method of processing

called vector processing (Churchland and Churchland 36).  The way that it works

is that when you send a combination of neural activations on one level of the

net, it will pass through the network on certain vectors caused by the

activation pattern and then output in another unique pattern (36).  This

process is much like the way that the human brain is believed to work.  This

type of processing is such that symbols are never manipulated in the fashion

that is presented in the Chinese room argument.  Symbol manipulation in a

vector-processing system may or may not be one of the cognitive skills that it

may display as a characteristic (36).  Therefore, the Chinese room is

non-applicable to the argument.  Searle argues against parallel processing by

presenting what he calls a Chinese gymnasium (Searle 28).  The gist of the

example is instead of the one man in the room, the room is full of men in a

parallel architecture.  He explains that none of them understands Chinese, and

the only thing accomplished by the extra men is that it would output faster,

without any comprehension (28).  The problem with this argument is that it is

unnecessary that the individual men need to know Chinese, as a single neuron

doesn't know any language either, but the whole thing probably does (Churchland

and Churchland 37).  For his Chinese gymnasium example to be fair there would

have to be the entire populations of 10,000 Earths in the gym (37).  There is

no way to prove there is no comprehension of Chinese in a network of that

magnitude.  Essentially what you would have in a room that size, with that many

people, is a gigantic, slow brain.  Mr. Searle argues against this view by

saying that it really doesn't matter, if nobody understands Chinese, neither

will the entire system (Searle 29).  The answer to that objection is that it is

possible, with the right architecture, to teach a computer Chinese.  If the

computer's structure was brainlike, the computer would be no different from a

Chinese child learning to communicate.



     Searle's arguments for not believing that computers are capable of human

thought are based on several simple axioms that he believes are true in all

types of computers.  The axioms he presents are sound.  All, except the last

one, which was, "Brains cause minds" (29).  In that axiom he declares that

minds are only capable of existing in brains, because brains are a biological

organ, with neurotransmitters, etc... (29).  This premise is not necessarily

true.  For example, in the Churchland article, they present an example of how

that axiom is not true.  Carver A. Mead, a researcher at the California

Institute of Technology, and his colleagues used analog VLSI (Very Large Scale

Integration) techniques to build an artificial retina (Churchland and

Churchland 37).  The machine is not a computer simulation of a retina, but an

actual real-time information processing unit that responds to light (37).  The

circuitry is based on the actual organ in a cat, and the output is incredibly

similar to the actual output of the cat's retina (37).  The process that is

used is completely without neurochemicals, so there really is no need for them,

hence the supposition that a mind can only exist in a brain is absurd.



     The conclusions that he draws from those axioms are not without flaws.

His first conclusion is that "Programs are neither constitutive nor sufficient

for minds" (Searle 29).  In a standard sense, it is probably the correct

conclusion, at least for the classical AI.  The new artificial intelligence,

however, is a merging of hardware and software in a synergistic relationship,

so programs will not solely handle the challenge of intelligence, but the

software will play a significant part in it.  If you look at the rest of his

conclusions, you will find that they are really only applicable to formal

programs alone, not software/hardware synergies, so they must be irrelevant to

the argument.  With his second conclusion, he essentially agrees that there is

a very real possibility of an artificial intelligence, as long as its causal

powers are at least that of the brain.  Modeling computers after the human

brain makes it probable that it can be done.



     It is improbable that there will be any thinking machines for many years.

The future holds many keys to this process.  It is necessary there be a greater

understanding of the mechanics of thought and memory before this end is

possible.  Classical artificial intelligence is obviously not going to work,

for the reasons stated earlier in the paper.  The answer obviously lies in the

realm of parallel processing and neural networks.  It has been proven that very

complicated and fast matrices of electronics can replicate biological

functioning, as in the example of the artificial retinas (Churchland and

Churchland 37).  Where the possibility lies is in the realm of combining the

processing abilities of complex computer architectures and the increasingly

sophisticated software needed to harness this power.



     We may find a solution within the psychology of childhood development.

When a child is born it is a blank slate.  In essence, they do not have any

real formed concepts, like those of syntax and semantics.  This is the way that

we should perceive a newly made computer of the kind that represents the human.

Everything must start from scratch, therefore it is necessary to teach the

computer as you would a baby.  This process is harder than teaching a newborn

child, since they are born with cognizance, but with time and knowledge of what

a computer needs to learn to become self-aware, it is possible.  There are

currently experiments going on where a doctor and an army of assistants are

building a base of language, and entering it, with referents to what they mean,

into the computer.  They are essentially teaching the computer manually what is

normally experienced by a child.  For example, a single word can have immense

amounts of referents, such as: what it is, what it can be compared to, and what

connotations are generally associated with them.  A word like "duck" for

example, could take weeks of compiling information, since you have to not only

put the concept of "duck" together, but also that of a bird, of colors, of

feathers, the basics of anatomy, and popular notions associated with the word

"duck."  With each layer of explanations you encounter you find a whole new

level of terms to define.  It is well-known that even the least intelligent

human being carries around a simply astonishing amount of information.  The

hardest things to define are on the simplest level of understanding, the

general hope of researchers is that with enough of the complex composite

concepts, the computer will be able to use the whole of its knowledge to puzzle

out the simple pieces.  This idea seems entirely logical, since it is something

that human beings try to every single day.  Humans are the same in that

respect, if we knew these simple truths, all philosophers and other scientists

would be simply unnecessary, as we would know all those things.  To date, the

scientists trying this experiment have succeeded in inputting almost all the

knowledge that an average 3 year old child has.  The strange thing is that in a

system like this, the computer seems to have a curious nature.  This would lead

one to think that the machine were cognizant, although in reality it most

probably is not the case.  The programs that compose this machine are simply

calling for more input to make it run more efficiently.  Although this is not

real thought yet one would suppose that this will be possible when the

computer's electronic architecture is sufficient to begin to change its own

programs.  That means that it would be working enough like a brain to revise

its beliefs, since beliefs are nothing less than knowledge in itself.



     The brain is a gigantic scale information processing machine, which is

simply a biological form of computer.  The implications of this call for a

rational person to assume if it is possible for a biological machine to think,

it would follow there would be a machine of a non-biological (ie. electronic)

nature that would be able to think, at least it would be if the electronic

brain was built to the equivalent of a human brain.



     Technology has increased exponentially in the last thirty years, but we

are still many years away from the first truly cognizant machines.  Because of

the arguments brought up, it is really impossible to prove there will be

cognizant machines, at least in a deductive sense.  In an inductive sense it

could be said there is a strong probability there will be a day when there will

be an intelligent machine.  It has been proven that the answer definitely does

not lie in the realm of computer programs in the manner of classical artificial

intelligence, since the computer architecture that is necessary for thought is

simply impossible in the traditional symbol-manipulating machine.  That part of

the argument is not in doubt, it is when you come into the hardware/software

synergy arena that the battle becomes heated.  Mr. Searle presents some very

strong arguments against the possibility, but these arguments are not

sufficient to destroy the possibility of computer thought.  In a case of

predicting the future there can be no definite proof, but if science and

technology can raise to the challenge of replicating the function of a human

brain, there will be, eventually, a computer that can think.





                                  Works Cited:



Churchland, Paul and Churchland, Patricia. "Could A Machine Think?"

     _Scientific American_ Jan. 1990: 32-37.



Searle, John M.  "Is A Brain's Mind a Computer Program?"  _Scientific American_

     Jan. 1990: 26-31.

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\_______/|All Rights Reserved.                               11/01/1994-#289|



