Artificial Inteligence

Artificial Inteligence - ABSTRACT Current neural network...

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ABSTRACT A 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. r 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 1 INTRODUCTION I · 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 O 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
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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
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This note was uploaded on 09/03/2011 for the course ECON 101 taught by Professor Smith during the Spring '09 term at Harvard.

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Artificial Inteligence - ABSTRACT Current neural network...

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