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).
