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 complex
interconnections. As Herb Brody states, "Neural networks do not require an
explicit set of rules. The network - rather like a child - makes up its own
rules that match the data it receives to the result it\'s told is correct" (42).
Impossible to achieve in expert systems, this ability to learn by example is the
characteristic of neural networks that makes them best suited to simulate human
behavior. Computer scientists have exploited this system characteristic to
achieve breakthroughs in computer vision, speech recognition, and optical
character recognition. Figure 1 illustrates the knowledge structures of neural
networks as compared to expert systems and standard computer programs. Neural
networks restructure their knowledge base at each step in the learning process.

This paper focuses on neural network technologies which have the potential to
increase security for financial transactions. Much of the technology is
currently in the research phase and has yet to produce a commercially available
product, such as visual recognition applications. Other applications are a
multimillion dollar industry and the products are well known, like Sprint
Telephone\'s voice activated telephone calling system. In the Sprint system the
neural network positively recognizes the caller\'s voice, thereby authorizing
activation of his calling account.

The First Steps

The study of the brain was once limited to the study of living tissue. Any
attempts at an electronic simulation were brushed aside by the neurobiologist
community as abstract conceptions that bore little relationship to reality.
This was partially due to the over-excitement in the 1950\'s and 1960\'s for
networks that could recognize some patterns, but were limited in their learning
abilities because of hardware limitations. In the 1990\'s computer simulations of
brain functions are gaining respect as the simulations increase their abilities
to predict the behavior of the nervous system. This respect is illustrated by
the fact that many neurobiologists are increasingly moving toward neural network
type simulations. One such neurobiologist, Sejnowski, introduced a three-layer
net which has made some excellent predictions about how biological systems
behave. Figure 2 illustrates this network consisting of three layers, in which
a middle layer of units connects the input and output layers. When the network
is given an input, it sends signals through the middle layer which checks for
correct output. An algorithm used in the middle layer reduces errors by
strengthening or weakening connections in the network. This system, in which
the system learns to adapt to the changing conditions, is called back-
propagation. The value of Sejnowski\'s network is illustrated by an experiment by
Richard Andersen at the Massachusetts Institute of Technology. Andersen\'s team
spent years researching the neurons monkeys use to locate an object in space
(Dreyfus and Dreyfus 42-61). Anderson decided to use