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468 Part Three Key System Applications for the Digital Age

The computer would combine the membership function readings in a weighted
manner and, using all the rules, raise and lower the temperature and humidity.
Fuzzy logic provides solutions to problems requiring expertise that is diffi-
cult to represent in the form of crisp IF-THEN rules. In Japan, Sendai’s subway
system uses fuzzy logic controls to accelerate so smoothly that standing pas-
sengers need not hold on. Mitsubishi Heavy Industries in Tokyo has been able
to reduce the power consumption of its air conditioners by 20 percent by imple-
menting control programs in fuzzy logic. The autofocus device in cameras is
only possible because of fuzzy logic. In these instances, fuzzy logic allows incre-
mental changes in inputs to produce smooth changes in outputs instead of dis-
continuous ones, making it useful for consumer electronics and engineering
applications.
Management also has found fuzzy logic useful for decision making and
organizational control. A Wall Street firm created a system that selects
companies for potential acquisition, using the language stock traders under-
stand. A fuzzy logic system has been developed to detect possible fraud in med-
ical claims submitted by health care providers anywhere in the United States.

MACHINE LEARNING

Machine learning is the study of how computer programs can improve their
performance without explicit programming. Why does this constitute learning?
A machine that learns is a machine that, like a human being, can recognize
patterns in data, and change its behavior based on its recognition of patterns,
experience, or prior learnings (a database). For instance, a car-driving robot
should be able to recognize the presence of other cars and objects (people),
and change its behavior accordingly (stop, go, slow down, speed up, or turn).
The idea of a self-taught, self-correcting, computer program is not new, and has
been a part of the artificial intelligence field at least since the 1970s. Up until
the 1990s, however, machine learning was not very capable of producing useful
devices or solving interesting, business problems.
Machine learning has expanded greatly in the last ten years because of the
growth in computing power available to scientists and firms and its falling cost,
along with advances in the design of algorithms, databases, and robots. The
Internet and the big data (see Chapter 6) made available on the Internet have
proved to be very useful testing and proving grounds for machine learning.
We use machine learning everyday but don’t recognize it. Every Google
search is resolved using algorithms that rank the billions of Web pages based
on your query, and change the results based on any changes you make in your
search, all in a few milliseconds. Search results also vary according to your
prior searches and the items you clicked on. Every time you buy something on
Amazon, its recommender engine will suggest other items you might be inter-
ested in based on patterns in your prior consumption, behavior on other Web
sites, and the purchases of others who are “similar” to you. Every time you visit
Netflix, a recommender system will come up with movies you might be inter-
ested in based on a similar set of factors.
Neural Networks
Neural networks are used for solving complex, poorly understood problems
for which large amounts of data have been collected. They find patterns and
relationships in massive amounts of data that would be too complicated and
difficult for a human being to analyze. Neural networks discover this knowl-
edge by using hardware and software that parallel the processing patterns of

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