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).
