FREE ESSAY ON ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

Recently, the media has spent an increasing amount of broadcast time on new technology.
The focus of high-tech media has been aimed at the flurry of advances concerning
artificial
intelligence (AI). What is artificial intelligence and what is the media talking about?
Are these
technologies beneficial to our society or mere novelties among business and marketing
professionals? Medical facilities, police departments, and manufacturing plants have all
been
changed by AI but how? These questions and many others are the concern of the general
public
brought about by the lack of education concerning rapidly advancing computer technology.
Artificial intelligence is defined as the ability of a machine to think for itself.
Scientists and
theorists continue to debate if computers will actually be able to think for themselves
at one point
(Patterson 7). The generally accepted theory is that computers do and will think more in
the
future. AI has grown rapidly in the last ten years chiefly because of the advances in
computer
architecture. The term artificial intelligence was actually coined in 1956 by a group of
scientists
having their first meeting on the topic (Patterson 6). Early attempts at AI were neural
networks
modeled after the ones in the human brain. Success was minimal at best because of the
lack of
computer technology needed to calculate such large equations. 
AI is achieved using a number of different methods. The more popular implementations
comprise neural networks, chaos engineering, fuzzy logic, knowledge based systems, and
expert
systems. Using any one of the aforementioned design structures requires a specialized
computer
system. For example, Anderson Consulting applies a knowledge based system to commercial
loan
officers using multimedia (Hedburg 121). Their system requires a fast IBM desktop
computer.
Other systems may require even more horsepower using exotic computers or workstations.
Even
more exotic is the software that is used. Since there are very few applications that are
pre-written
using AI, each company has to write it's own software for the solution to the problem. An
easier
way around this obstacle is to design an add-on. The company FuziWare makes several
applications that act as an addition to a larger application. FuziCalc, FuziQuote,
FuziCell,
FuziChoice, and FuziCost are all products that are used as management decision support
systems
for other off-the shelf applications (Barron 111).
In order to tell that AI is present we must be able to measure the intelligence being
used.
For a relative scale of reference, large supercomputers can only create a brain the size
of a fly
(Butler and Caudill 5). It is surprising what a computer can do with that intelligence
once it has
been put to work. Almost any scientific, business, or financial profession can benefit
greatly from
AI. The ability of the computer to analyze variables provides a great advantage to these
fields.
There are many ways that AI can be used to solve a problem. Virtually all of these
methods require special hardware and software to use them. Unfortunately, that makes AI
systems expensive. Consulting firms, companies that design computing solutions for their
clients,
have offset that cost with the quality of the system. Many new AI systems now give a
special edge
that is needed to beat the competition. 
Created by Lotfi Zadeh almost thirty years ago, fuzzy logic is a mathematical system
that
deals with imprecise descriptions, such as new, nice, or large (Schmuller 14). This
concept
was also inspired from biological roots. The inherent vagueness in everyday life
motivates fuzzy
logic systems (Schmuller 8). In contrast to the usual yes and no answers, this type of
system can
distinguish the shades in-between. In Los Angeles a fuzzy logic system is used to analyze
input
from several cameras located at different intersections (Barron 114). This system
provides a
smart light that can decide whether a traffic light should be changed more often or
remain green
longer. In order for these smart lights to work the system assigns a value to an input
and
analyzes all the inputs at once. Those inputs that have the highest value get the highest
amount of
attention. For example, here is how a fuzzy logic system might evaluate water
temperature. If the
water is cold, it assigns a value of zero. If it is hot the system will assign the value
of one. But if
the next sample is lukewarm it has the capability to decide upon a value of 0.6
(Schmuller 14).
The varying degrees of warmness or coldness are shown through the values assigned to it.
Fuzzy
logic's structure allows it to easily rate any input and decide upon the importance.
Moreover,
fuzzy logic lends itself to multiple operations at once. 
Fuzzy logic's ability to do multiple operations allows it to be integrated into neural
networks. Two very powerful intelligent structures make for an extremely useful product.
This
integration takes the pros of fuzzy logic and neural networks and eliminates the cons of
both
systems. This new system is a now a neural network with the ability to learn using fuzzy
logic
instead of hard concrete facts. Allowing a more fuzzy input to be used in the neural
network
instead of being passed up will greatly decrease the learning time of such a network.
Another promising arena of AI is chaos engineering. The chaos theory is the cutting-edge
mathematical discipline aimed at making sense of the ineffable and finding order among
seemingly
random events (Weiss 138). Chaologists are experimenting with Wall Street where they are
hardly
receiving a warm welcome. Nevertheless, chaos engineering has already proven itself and
will be
present for the foreseeable future. The theory came to life in 1963 at the Massachusetts
Institute
of Technology. Edward Lorenz, who was frustrated with weather predictions noted that
they
were inaccurate because of the tiny variations in the data. Over time he noticed that
these
variations were magnified as time continued. His work went unnoticed until 1975 when
James
Yorke detailed the findings to American Mathematical Monthly. Yorke's work was the
foundation
of the modern chaos theory (Weiss 139). The theory is put into practice by using
mathematics to
model complex natural phenomena. 
The chaos theory is used to construct portfolio's of long and short positions in the
stock
market on Wall Street. This is used to assess market risk accurately, not to predict the
future
(Weiss 139). Unfortunately, the hard part is putting the theory into practice. It has yet
to impress
the people that really count: financial officers, corporate treasurers, etc. It is quite
understandable
though, who is willing to sink money into a system that they cannot understand? Until a
track
record is set for chaos most will be unwilling to try, but to get the track record
someone has to try
it, it's what is known as the catch-22. The chaos theory can be useful in other places as
well.
Kazuyuki Aihara, an engineering professor at Tokyo's Denki University, claims that chaos
engineering can be applied to analyzing heart patients. The pattern of beating hearth
changes
slightly and each person pattern is different (Ono 41). Considering this discovery a
dataprocessing
company in Japan has marketed a physical checkup system that uses chaos engineering.
This
system measures health and psychological condition by monitoring changes in circulation
at the
fingertip (Ono 41). Aihara admits that chaos-engineering has tremendous potential but
does have
limitations. He states, It can predict the future more accurately than any other system
but that
doesn't mean it can predict the future all the time. Along these lines Rabi Satter, a
computer
consultant with a BS in Computer Science, believes that the current sentiment that the
world is
rational and can be reduced to mathematical equations is wrong. In order to make great
strides in
this arena [AI] we need new approaches informed by the past but not guided by it. A fresh
voice if
you would. As one person said we are using brute force to solve the problem states
Satter. 
A few more implementations of artificial intelligence include knowledge-based systems,
expert systems, and case-based reasoning. All of these are relatively similar because
they all use a
fixed set of rules. Knowledge-based systems (KBS) are systems that depend on a large base
of
knowledge to perform difficult tasks (Patterson 13). KBS get their information from
expert
knowledge that has been programmed into facts, rules, heuristics and procedures. However,
the
power of a knowledge-based system is only as good as the knowledge given to it.
Therefore, the
knowledge section is usually separate from the control system and can be updated
independently.
This enables system updates and additional information to be added in a more efficient
manner
then making a whole new system from scratch (O'Shea 162).
Expert systems have proven effective in a number of problem domains that usually require
human intelligence (Patterson 326). They were developed in the research labs of
universities in the
1960's and 1970's. Expert systems are primarily used as specialized problem solvers. The
areas
that this can cover are almost endless. This can include law, chemistry, biology,
engineering,
manufacturing, aerospace, military operations, finance, banking, meteorology, geology,
and more.
Expert systems use knowledge instead of data to control the solution process. In
knowledge lies
the power is a theme repeated when building such systems. These systems are capable of
explaining the answer to the problem and why any requested knowledge was necessary.
Expert
systems use symbolic representations for knowledge and perform computations through
manipulations of the different symbols (Patterson 329). But perhaps the greatest
advantage to
expert systems is their ability to realize their limits and capabilities.
Case-based reasoning (CBR) is similar to expert system because theoretically they could
use they same set of data. CBR has been proposed as a more psychologically plausible
model of
the reasoning used by an expert while expert systems use more fashionable rule-based
reasoning
systems (Riesbeck 9). This type of system uses a different computational element that
decides the
outcome of a given input. Instead of rules in an expert system, CBR uses cases to
evaluate each
input uniquely. Each case would be matched to what a human expert would do in a specific
case.
Additionally this system knows no right answers, just those that were used in former
cases to
match. A case library is set up and each decision is stored. The input question is
characterized to
appropriate features that are recognizable and is matched to a similar past problem and
its solution
is then applied. 
Now that each type of implementation of AI has been discussed, how do we use all this
technology? Foremost, neural networks are used mainly for internal corporate applications
in
various types of problems. For example, Troy Nolen was hired by a major defense
contractor to
design programs for guiding flight and battle patterns of the YF-22 fighter. His software
runs on
five on-board computers and makes split-second decisions based on data from ground
stations,
radar, and other sources. Additionally it predicts what the enemy planes would do,
guiding the
jet's actions consequently (Schwartz 136). Now he and many others design financial
software
based on their experience with neural networks. Nolen works for Merrill Lynch & Co. to
develop
software that will predict the prices of many stocks and bonds. Murry Ruggiero also
designs
software, but his forecasts the future of the Standard & Poors index. Ruggiero's program,
called
BrainCel, is capable of giving an annual return of 292%. Another major application of
neural
networks is detecting credit card fraud. Mellon Bank, First Bank, and Colonial National
Bank all
use neural networks that can determine the difference between fraud and regular
transactions
(Bylinsky 98). Mellon Bank states the new neural network allows them to eliminate 90% of
the
false alarms that occur under traditional detection systems (Bylinsky 99).
Secondly, fuzzy logic has many applications that hit close to home. Home appliances win
most of the ground with AI enhanced washing machines, vacuum cleaners, and
air-conditioners.
Hitachi and Matsu*censored*a manufacture washing machines that automatically adjust for
load size and
how dirty the articles are (Shine 57). This machine washes until clean, not just for ten
minutes.
Matsu*censored*a also manufactures vacuum cleaners that adjust the suction power
according to the
volume of dust and the nature of the floor. Lastly, Mitsubishi uses fuzzy logic to slow
air-conditioners gradually to the desired temperature. The power consumption is reduced
by 20%
using this system (Schmuller 27).
The chaos theory is limited in scope at this time mainly because of lack of interest and
resources to experiment with. However, Wall Street will be hearing more about it for a
long time
to come. Also, the medical field has an interest because of its ability to distinguish
between natural
and non-natural patterns. The chaos theory has a foot in the door, but a breakthrough in
design
will have to come around first before any major moves toward the chaos theory will
happen.
Expert systems are prevalent all over the world. This proven technology has made its way
into almost everywhere that human experts live. Expert systems even can show an employee
how
to be an expert in a particular occupation. A Massachusetts company specializes in
teaching good
judgment to new employees or trainees. Called Wisdom Simulators, this company sells
software
that simulates nasty job situations in the business world. The ability to learn before
the need arises
attracts many customers to this type of software (Nadis 8). Expert systems have also been
applied
in medical facilities, diagnosis of mechanical devices, planning scientific experiments,
military
operations, and teaching students specialized tasks. 
Knowledge-based systems and case-based reasoning will be on the rise for a long time to
come. These systems are souped-up expert systems that provide more powerful searching
and
decision-making strategies. KBS is finding its home at help desks by working with
telephone
operators to direct calls. CBR will have close ties to law with its ability to use past
precedents to
determine a sentence and prison term. KBS is already being used by the Tennessee
Department of
Corrections for determining which inmates are eligible for parole (Peterson 37). 
Making recommendations on which AI systems work the best almost requires AI itself.
However, I believe that some are definitely better than others. Neural networks,
unfortunately,
have performance spectrums that continue to dwell at both extremes. While there are some
very
good networks that perform their designed task beautifully, there are others that
perform
miserably. Furthermore, these networks require massive amounts of computing resources
that
restrict their use to those who can afford it. On the other hand, fuzzy logic is
practically a win-win
situation. Although some are rather simple, these systems perform their duties quickly
and
accurately without expensive equipment. They can easily replace many mundane tasks that
others
computer systems would have trouble with. Fuzzy logic has enabled computers to calculate
such
terms as large or several that would not be possible without it (Schmuller 14). On the
other
hand, the chaos theory has potential for handling an infinite amount of variables. This
gives it the
ability to be a huge success in the financial world. It's high learning curve and its
primitive nature,
however, limits it to testing purposes only for the time being. It will be a rocky road
for chaos
theory and chaos engineering for several years. Finally, expert systems, knowledge-based
systems,
and cased-based reasoning systems are here to stay for a long time. They provide an
efficient, easy
to use program that yields results that no one can argue with. Designed correctly, they
are can be
easily updated and modernized. 
While the massive surge into the information age has ushered some old practices out of
style, the better ones have taken over with great success. The rate of advancement may
seem fast
to the average person, but the technology is being put to good use and is not out of
control. A
little time to experiment with the forefront technologies and society will be rewarded
with big
pay-offs. Soon there will be no place uncharted and no stone unturned. Computers are the
future
in the world and we should learn to welcome their benefits and improve their shortcomings
to
enrich the lives of the world.
Work Cited
166-173, 1997.
, pp. 177-190.
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