Реферат: Computer History Essay Research Paper ABSTRACTCurrent neural

Analog devices carry out the same operation in one step and so decrease the power consumption

of silicon circuits by a factor of about 10,000″ (qtd in Thompson 251).

Besides validating their neural network, the accuracy of this silicon chip displays the usefulness

of analog computing despite the assumption that only digital computing can provide the accuracy

necessary for the processing of information.

As close as these systems come to imitating their biological counterparts, they still have a long

way to go. For a computer to identify more complex shapes, e. g., a person?s face, the professors

estimate the requirement would be at least 100 times more pixels as well as additional circuits

that mimic the movement-sensitive and edge-enhancing functions of the eye. They feel it is possible

to achieve this number of pixels in the near future. When it does arrive, the new technology will

likely be capable of recognizing human faces.

Visual recognition would have an undeniable effect on reducing crime in automated financial transactions.

Future technology breakthroughs will bring visual recognition closer to the recognition of individuals,

thereby enhancing the security of automated financial transactions.

? Computer-Aided Voice Recognition

Voice recognition is another area that has been the subject of neural network research.

Researchers have long been interested in developing an accurate computer-based system capable

of understanding human speech as well as accurately identifying one speaker from another.

? Current Research

Ben Yuhas, a computer engineer at John Hopkins University, has developed a promising system for

understanding speech and identifying voices that utilizes the power of neural networks. Previous attempts

at this task have yielded systems that are capable of recognizing up to 10,000 words, but only when each

word is spoken slowly in an otherwise silent setting. This type of system is easily confused by back

ground noise (Moyne 100).

Ben Yuhas’ theory is based on the notion that understanding human speech is aided, to some small degree,

by reading lips while trying to listen. The emphasis on lip reading is thought to increase as the

surrounding noise levels increase. This theory has been applied to speech recognition by adding a

system that allows the computer to view the speaker?s lips through a video analysis system while

hearing the speech.