Artificial Inteligence Essay

2576 words - 10 pages

ABSTRACTCurrent neural network technology is the most progressive of the artificial intelligencesystems today. Applications of neural networks have made the transition from laboratorycuriosities to large, successful commercial applications. To enhance the security of automatedfinancial transactions, current technologies in both speech recognition and handwritingrecognition are likely ready for mass integration into financial institutions.RESEARCH PROJECTTABLE OF CONTENTSIntroduction 1Purpose 1Source of Information 1Authorization 1Overview 2The First Steps 3Computer-Synthesized Senses 4Visual Recognition 4Current Research 5Computer-Aided Voice Recognition 6Current Applications 7Optical Character Recognition 8Conclusion 9Recommendations 10Bibiography 11INTRODUCTION· PurposeThe purpose of this study is to determine additional areas where artificial intelligencetechnology may be applied for positive identifications of individuals during financialtransactions, such as automated banking transactions, telephone transactions , and homebanking 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.OverviewRecently, the thrust of studies into practical applications for artificial intelligencehave focused on exploiting the expectations of both expert systems and neural networkcomputers. In the artificial intelligence community, the proponents of expert systemshave approached the challenge of simulating intelligence differently than their counterpartproponents of neural networks. Expert systems contain the coded knowledge of a human expertin a field; this knowledge takes the form of 'if-then' rules. The problem with this approachis that people don't always know why they do what they do. And even when they can express thisknowledge, it is not easily translated into usable computer code. Also, expert systems areusually bound by a rigid set of inflexible rules which do not change with experience gainedby trail and error. In contrast, neural networks are designed around the structure of abiological model of the brain. Neural networks are composed of simple components called'neurons' each having simple tasks, and simultaneously communicating with each other bycomplex interconnections. As Herb Brody states, 'Neural networks do not require an explicitset of rules. The network - rather like a child - makes up its own rules that match thedata it receives to the result it's told is correct' (42). Impossible to achieve in expertsystems, this ability to learn by example is the characteristic of neural networks that makesthem best suited to simulate human behavior. Computer scientists have exploited this systemcharacteristic to achieve breakthroughs in computer vision, speech recognition, and opticalcharacter recognition. Figure 1 illustrates the knowledge structures of neural networksas compared to expert systems and standard computer programs. Neural networks...

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