Реферат: Computer History Essay Research Paper ABSTRACTCurrent neural

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