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