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