FREE ESSAY ON AI - EDGE OF EXCELLENCE

AI - EDGE OF EXCELLENCE

Running head: The Edge of Excellence 
The Edge of Excellence
Kathleen P. Munn
Community College of Philadelphia
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.
Neural networks have entered the spotlight with surprisingly successful results. A neural
network is a type of information processing system whose architecture is similar to the
structure of biological neural systems (Butler and Caudill 5). The neural network tries
to mimic the way a brain and nervous system work by analyzing sensory inputs and
calculating an outcome. Before it can be used a neural network must be trained. Some can
learn by themselves, some require training by doing, and others learn by trial and error.
A computer learns by naturally associating items the computer is taught and grouping them
together physically. Additionally, a computer can retrieve stored information from
incomplete or partially incorrect clues. Neural networks are able to generalize
categories based on specifics of the contents. Lastly, it is highly fault tolerant. This
means that the network can sustain a large amount of damage and still function. This type
of system is inherently an excellent design for any application that requires little
human intervention and that must learn on the go. 
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. 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.
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). 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. 
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. 
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. 
Case-based reasoning (CBR) is similar to expert system because theoretically they could
use the 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 (Patterson 329). This type of system uses a different computational
element that decides the outcome of a given input. 
Making recommendations on which AI systems work the best almost requires AI itself.
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. 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.
Bibliography
Butler, Charles, and Caudill, Maureen. Naturally Intelligent Systems. Cambridge: The MIT
Press, 1990.
Hedburg, Sara. See, Hear, Learn. Byte July 1993: (pp. 119-128).
Patterson, Dan W. Introduction to Artificial Intelligence and Expert Systems. Englewood
Cliffs: Prentice Hall, 1990.
Schmuller, Joseph, ed. PC AI March/April 1993: (pp. 8-29).
Weiss, Gary. Chaos Hits Wall Street-The Theory, That is. Business Week 2 Nov. 1992:
(pp.138-140).