Technology

Technology has traditionally evolved as the result of
human needs. Invention, when prized and
rewarded, will invariably rise-up to meet the free
market demands of society. It is in this realm that
Artificial Intelligence research and the resultant
expert systems have been forged. Much of the material
that relates to the field of Artificial Intelligence
deals with human psychology and the nature of
consciousness. Exhaustive debate on consciousness and
the possibilities of consciousnessness in machines has
adequately, in my opinion, revealed that it is most
unlikely that we will ever converse or interract with
a machine of artificial consciousness. In John
Searle’s collection of lectures, Minds, Brains and
Science, arguments centering around the mind-body
problem alone is sufficient to convince a reasonable
person that there is no way science will ever unravel
the mysteries of consciousness. Key to Searle’s
analysis of consciousness in the context of Artificial
Intelligence machines are refutations of strong and
weak AI theses. Strong AI Theorists (SATs) believe
that in the future, mankind will forge machines that
will think as well as, if not better than humans. To
them, pesent technology constrains this achievement.

The Weak AI Theorists (WATs), almost converse to the
SATs, believe that if a machine performs functions
that resemble a human’s, then there must
be a correlation between it and consciousness. To
them, there is no technological impediment to thinking
machines, because our most advanced machines already
think. It is important to review Searle’s refutations
of these respective theorists’ proposition to
establish a foundation (for the purpose of this essay)
for discussing the applications of Artificial
Intelligence, both now and in the future.
Strong AI Thesis
Strong AI Thesis, according to Searle,
can be described in four basic propositions.
Proposition one categorizes human thought as the
result of computational processes. Given enough
computational power, memory, inputs, etc., machines
will be able to think, if you believe this
proposition. Proposition two, in essence, relegates
the human mind to the software bin. Proponents of this
proposition believe that humans just happen to have
biological computers that run “wetware” as opposed to
software. Proposition three, the Turing proposition,
holds that if a conscious being can be convinced that,
through context-input manipulation, a machine is
intelligent, then it is. proposition four is where the
ends will meet the means. It purports that when we are
able to finally understand the brain, we will be able
to duplicate its functions. Thus, if we replicate the
computational power of the mind, we will then
understand it. Through argument and experimentation,
Searle is able to refute or severely diminish these
propositions. Searle argues that machines may well
be able to “understand” syntax, but not the
semantics, or meaning communicated thereby.
Essentially, he makes his point by citing the famous
“Chinese Room Thought Experiment.” It is here he
demonstrates that a computer” (a non-chinese speaker,
a book of rules and the chinese symbols) can fool a
native speaker, but have no idea what he is saying. By
proving that entities don’t have to understand what
they are processing to appear as understanding refutes
proposition one.
Proposition two is refuted by the
simple fact that there are no artificial minds or
mind-like devices. Proposition two is thus a matter of
science fiction rather than a plausible theory A good
chess program, like my (as yet undefeated) Chessmaster
4000 Trubo refutes proposition three by passing a
Turing test. It appears to be intelligent, but I know
it beats me through number crunching and symbol
manipulation. The Chessmaster 4000 example is also an
adequate refutation of Professor Simon’s fourth
proposition: “you can understand a process if you can
reproduce it.” Because the Software Toolworks
company created a program for my computer that
simulates the behavior of a grandmaster
in the game, doesn’t mean that the computer is indeed
intelligent. Weak AI Thesis
There are five basic propositions that
fall in the Weak AI Thesis (WAT) camp. The first of
these states that the brain, due to its complexity of
operation, must function something like a computer,
the most sophisticated of human invention. The second
WAT proposition
states that if a machine’s output, if
it were compared to that of a human counterpart
appeared to be the result of
intelligence, then the machine must be so. Proposition
three
concerns itself with the similarity
between how humans solve problems and how
computers do so. By solving problems
based on information gathered from their respective
surroundings and memory and by obeying
rules of logic, it is proven that machines can
indeed think. The fourth WAT
proposition deals with the fact that brains are known
to have
computational abilities and that a
program therein can be inferred. Therefore, the mind
is
just a big program (“wetware”). The
fifth and final WAT proposition states that, since the
mind appears to be “wetware”, dualism
is valid.
Proposition one of the Weak AI Thesis
is refuted by gazing into the past. People have
historically associated the state of
the art technology of the time to have elements of
intelligence and consciousness. An
example of this is shown in the telegraph system of
the
latter part of the last century.

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People at the time saw correlations between the brain
and
the telegraph network itself.
Proposition two is readily refuted by
the fact that semantical meaning is not addressed by
this argument. The fact that a clock
can compute and display time doesn’t mean that it has
any concept of coounting or the
meaning of time.
Defining the nature of rule-following
is the where the weakness lies with the fourth
proposition. Proposition four fails to
again account for the semantical nature of symbol
manipulation. Referring to the Chinese
Room Thought Experiment best refutes this
argument.
By examining the nature by which
humans make conscious decisions, it becomes clear that
the fifth proposition is an item of
fancy. Humans follow a virtually
infinite set of rules that rarely follow highly
ordered
patterns. A computer may be programmed
to react to syntactical information with
seeminly semantical output, but again,
is it really cognizant?
We, through Searle’s arguments, have
amply established that the future of AI lies not in
the semantic cognition of data by
machines, but in expert systems designed to perform
ordered tasks.
Technologically, there is hope for
some of the proponents of Strong AI Thesis. This hope
lies in the advent of neural networks
and the application of fuzzy logic engines.
Fuzzy logic was created as a subset of
boolean logic that was designed to handle data that
is neither completely true, nor
completely false. Intoduced by Dr. Lotfi Zadeh in
1964, fuzzy
logic enabled the modelling of
uncertainties of natural language.
Dr. Zadeh regards fuzzy theory not as
a single theory, but as “fuzzification”, or the
generalization of specific theories
from discrete forms to continuous (fuzzy) forms.
The meat and potatos of fuzzy logic is
in the extrapolation of data from seta of variables. A
fairly apt example of this is the
variable lamp. Conventional boolean logical processes
deal
well with the binary nature of lights.

They are either on, or off. But introduce the variable
lamp, which can range in intensity
from logically on to logically off, and this is where
applications demanding the application
of fuzzy logic come in. Using fuzzy algorithms on
sets of data, such as differing
intensities of illumination over time, we can infer a
comfortable lighting level based upon
an analysis of the data.
Taking fuzzy logic one step further,
we can incorporate them into fuzzy expert systems.

This systems takes collections of data
in fuzzy rule format. According to Dr. Lotfi, the
rules
in a fuzzy logic expert system will
usually follow the following simple rule:
“if x is low and y is high, then z is
medium”.
Under this rule, x is the low value of
a set of data (the light is off) and y is the high
value
of the same set of data (the light is
fully on). z is the output of the inference based upon
the degree of fuzzy logic application
desired. It is logical to determine that based upon
the
inputs, more than one output (z) may
be ascertained. The rules in a fuzzy logic expert
system is described as the rulebase.
The fuzzy logic inference process
follows three firm steps and sometimes an optional
fourth. They are:
1. Fuzzification is the process by
which the membership functions determined for the
input
variables are applied to their true
values so that truthfulness of rules may be
established.
2. Under inference, truth values for
each rule’s premise are calculated and then applied to
the output portion of each rule.
3. Composition is where all of the
fuzzy subsets of a particular problem are combined
into
a single fuzzy variable for a
particular outcome.
4. Defuzzification is the optional
process by which fuzzy data is converted to a crisp
variable. In the lighting example, a
level of illumination can be determined (such as
potentiometer or lux values).
A new form of information theory is
the Possibility Theory. This theory is similar to, but
independent of fuzzy theory. By
evaluating sets of data (either fuzzy or discrete),
rules
regarding relative distribution can be
determined and possibilities can be assigned. It is
logical to assert that the more data
that’s availible, the better possibilities can be
determined.
The application of fuzzy logic on
neural networks (properly known as artificial neural
networks) will revolutionalize many
industries in the future. Though we have determined
that conscious machines may never come
to fruition, expert systems will certainly gain
“intelligence” as the wheels of
technological innovation turn.
A neural network is loosely based upon
the design of the brain itself. Though the brain is
an impossibly intricate and complex,
it has
a reasonably understood feature in its
networking of neurons. The neuron is the
foundation of the brain itself; each
one manifests up to 50,000 connections to other
neurons. Multiply that by 100 billion,
and one begins to grasp the magnitude of the brain’s
computational ability.
A neural network is a network of a
multitude of simple processors, each of which with a
small amount of memory. These
processors are connected by uniderectional data busses
and process only information addressed
to them. A centralized processor acts as a traffic
cop for data, which is parcelled-out
to the neural network and retrieved in its digested
form. Logically, the more processors
connected in the neural net, the more powerful the
system.
Like the human brain, neural networks
are designed to acquire data through experience,
or learning. By providing examples to
a neural network expert system, generalizations are
made much as they are for your
children learning about items (such as chairs, dogs,
etc.).


Modern neural network system
properties include a greatly enhanced computational
ability
due to the parallelism of their
circuitry. They have also proven themselves in fields
such as
mapping, where minor errors are
tolerable, there is alot of example-data, and where
rules
are generally hard to nail-down.
Educating neural networks begins by
programming a “backpropigation of error”, which is
the foundational operating systems
that defines the inputs and outputs of the system. The
best example I can cite is the Windows
operating system from Microsoft. Of-course,
personal computers don’t learn by
example, but Windows-based software will not run
outside (or in the absence) of
Windows.
One negative feature of educating
neural networks by “backpropigation of error” is a
phenomena known as, “overfitting”.

“Overfitting” errors occur when conflicting
information
is memorized, so the neural network
exhibits a degraded state of function as a result. At
the worst, the expert system may
lock-up, but it is more common to see an impeded state
of operation. By running programs in
the operating shell that review data against a data
base, these problems have been
minimalized.
In the real world, we are seeing an
increasing prevalence of neural networks. To fully
realize the potential benefits of
neural networks our lives, research must be intense
and
global in nature. In the course of my
research on this essay, I was privy to several
institutions and organizations
dedicated to the collaborative development of neural
network
expert systems.
To be a success, research and
development of neural networking must address societal
problems of high interest and
intrigue. Motivating the talents of the computing
industry will
be the only way we will fully realize
the benefits and potential power of neural networks.
There would be no support, naturally,
if there was no short-term progress. Research and
development of neural networks must be
intensive enough to show results before interest
wanes.
New technology must be developed
through basic research to enhance the capabilities of
neural net expert systems. It is
generally
acknowledged that the future of neural
networks depends on overcoming many
technological challenges, such as data
cross-talk (caused by radio frequency generation of
rapid data transfer) and limited data
bandwidth.
Real-world applications of these
“intelligent” neural network expert systems include,
according to the Artificial
Intelligence Center, Knowbots/Infobots and intelligent
Help desks.

These are primarily easily accessible
entities that will host a wealth of data and advice
for
prospective users. Autonomous vehicles
are another future application of intelligent neural
networks. There may come a time in the
future where planes will fly themselves and taxis
will deliver passengers without human
intervention. Translation is a wonderful possibility
of these expert systems. Imagine the
ability to have a device translate your English spoken
words into Mandarin Chinese! This goes
beyond simple languages and syntactical
manipulation. Cultural gulfs in
language would also be the focus of such devices.
Through the course of Mind and
Machine, we have established that artificial
intelligence’s
function will not be to replicate the
conscious state of man, but to act as an auxiliary to
him. Proponents of Strong AI Thesis
and Weak AI Thesis may hold out, but the inevitable
will manifest itself in the end.
It may be easy to ridicule those
proponents, but I submit that in their research into
making
conscious machines, they are doing the
field a favor in the innovations and discoveries
they make.
In conclusion, technology will prevail
in the field of expert systems only if the philosophy
behind them is clear and strong. We
should not strive to make machines that may supplant
our causal powers, but rather ones
that complement them. To me, these expert systems
will not replace man – they shouldn’t.

We will see a future where we shall increasingly find
ourselves working beside intelligent
systems.