30-05-2012, 10:33 AM
Artificial Neural Networks: The next intelligence
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Abstract
This paper is divided in two parts. Part one examines the relevance of Artificial
Neural Networks (ANNs) for various business applications. The first section sets the
stage for ANNs in the context of modern day business by discussing the evolution of
businesses from Industrial Revolution to current Information Age to outline why
business today are in critical need of technology that sifts through massive data.
Next section introduces Artificial Neural Network technology as a favorable
alternative to traditional analytics and informs the reader of the basic concept
underlying the technology. Finally, third section screens through four different
applications of ANNs to gain an insight into potential business opportunities that lie
abound.
Introduction
Modern day businesses face unique challenges that were nonexistent prior to the
Industrial and Internet Revolution. Industrial revolution brought about the concept
of economies of scale, mass production and standardization. Businesses competed
on the grounds of operational efficiency and scale of production. As a result,
successful organizations grew larger to accommodate these practices and faced an
increasing amount of coordination costs to fulfill their services. Advances in
communication technology such as telephone, television, fax and internet have
greatly enhanced the organization’s ability to coordinate through chains of
geographically dispersed units, suppliers, and customers and enabled the large
corporations to minimize coordination costs. As a result of those advances and
improvements in transportation, businesses started competing on the grounds of
timely delivery of products/services and customer satisfaction.
Learning Algorithm
The crucial part of neural network alchemy is in its ability to learn from series of
iterations of input data (called the training period). The most basic algorithm that
enables this is known as the delta rule. In 1962, Widrow7 provided this first learning
algorithm for 2 layer ANNs (a.k.a. perceptron). By 1986, with advances in
computational technology and further academic work, Rumelhart, et al.,8 suggested
a more novel approach called the back propagation method that enabled learning for
multilayered feed forward networks. Essentially, delta rule was insufficient for
training networks with hidden layers that did not have direct inputs and outputs.