ion 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 persons 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 ...
