Artificial Intelligence

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Term Paper Title	Artificial Intelligence
# of Words	2640
# of Pages (250 words per page double spaced)	10.56

Artificial Intelligence

Artificial Intelligence

ABSTRACT

Current neural network technology is the most progressive of the artificial
intelligence systems today.  Applications of neural networks have made the
transition from laboratory curiosities to large, successful commercial
applications.  To enhance the security of automated financial transactions,
current technologies in both speech recognition and handwriting recognition are
likely ready for mass integration into financial institutions.

RESEARCH PROJECT

TABLE OF CONTENTS
     Introduction   1
     Purpose   1
     Source of Information    1
     Authorization  1
     Overview  2
     The First Steps     3
     Computer-Synthesized Senses   4
     Visual Recognition  4
     Current Research    5
     Computer-Aided Voice Recognition   6
     Current Applications    7
     Optical Character Recognition 8
     Conclusion     9
     Recommendations     10
     Bibiography    11

INTRODUCTION

Purpose

The purpose of this study is to determine additional areas where artificial
intelligence technology may be applied for positive identifications of
individuals during financial transactions, such as automated banking
transactions, telephone transactions , and home banking activities.  This study
focuses on academic research in neural network technology . This study was
funded by the Banking Commission in its effort to deter fraud.

Overview

Recently, the thrust of studies into practical applications for artificial
intelligence have focused on exploiting the expectations of both expert systems
and neural network computers.  In the artificial intelligence community, the
proponents of expert systems have approached the challenge of simulating
intelligence differently than their counterpart proponents of neural networks.
Expert systems contain the coded knowledge of a human expert in a field; this
knowledge takes the form of "if-then" rules.  The problem with this approach is
that people don't always know why they do what they do. And even when they can
express this knowledge, it is not easily translated into usable computer code.
Also, expert systems are usually bound by a rigid set of inflexible rules which
do not change with experience gained by trail and error. In contrast, neural
networks are designed around the structure of a biological model of the brain.
Neural networks are composed of simple components called "neurons" each having
simple tasks, and simultaneously communicating with each other by ...

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