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Chapter 11 Managing Knowledge 471

FIGURE 11.9 HOW A NEURAL NETWORK WORKS

A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic.
The hidden layer then processes inputs, classifying them based on the experience of the model.
In this example, the neural network has been trained to distinguish between valid and fraudulent
credit card purchases.

purchase. Also, self-organizing neural networks can be trained by exposing
them to large amounts of data and allowing them to discover the patterns
and relationships in the data.
A Google research team headed by Stanford University computer scientist
Andrew Y. Ng and Google fellow Jeff Dean recently created a neural network
with more than one billion connections that could identify cats. The network
used an array of 16,000 processors and was fed random thumbnails of images,
each extracted from a collection of 10 million YouTube videos. The neural net-
work taught itself to recognize cats, without human help in identifying specific
features during the learning process. Google believes this neural network has
promising applications in image search, speech recognition, and machine lan-
guage translation (Markoff, 2012).
Whereas expert systems seek to emulate or model a human expert’s way
of solving problems, neural network builders claim that they do not program
solutions and do not aim to solve specific problems. Instead, neural network
designers seek to put intelligence into the hardware in the form of a generalized
capability to learn. In contrast, the expert system is highly specific to a given
problem and cannot be retrained easily.
Neural network applications in medicine, science, and business address
problems in pattern classification, prediction, financial analysis, and control
and optimization. In medicine, neural network applications are used for screen-
ing patients for coronary artery disease, for diagnosing patients with epilepsy
and Alzheimer’s disease, and for performing pattern recognition of pathology
images. The financial industry uses neural networks to discern patterns in
vast pools of data that might help predict the performance of equities, corpo-
rate bond ratings, or corporate bankruptcies. Visa International uses a neural
network to help detect credit card fraud by monitoring all Visa transactions for
sudden changes in the buying patterns of cardholders.
There are many puzzling aspects of neural networks. Unlike expert systems,
which typically provide explanations for their solutions, neural networks
cannot always explain why they arrived at a particular solution. Moreover,
they cannot always guarantee a completely certain solution, arrive at the

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