Information Technology and Productivity
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It is largely assumed that the advances in information technology realized in the latter half of the 1900s resulted in productivity gains in the workplace. Computers were able to perform millions of calculations far faster and with greater accuracy than their human counterparts, robots could work 24-hour shifts without breaks and without varying from a set pattern, and the World Wide Web makes it possible to communicate with individuals throughout the world. There have, in fact, been significant increases in productivity in various work functions, but there are also disadvantages in relying too heavily on technology. This research examines some of the areas which have realized productivity gains (including expert systems and Web based training), and also considers the trap of information technology.CASE software automates many of the functions that system designers and programmers must perform. Analysts can use CASE to generate flow chart symbols by using a mouse to designate symbol choices and screen positions, and create data dictionaries through the labeling of processes. In some products, data dictionary entries are automatically made with many of the fields filled in based on the development environment. Process description at a detailed level (such as structured English) can allow automatic generation of program code based on algorithms entered by the developer, and prototype models can also be generated in most CASE software products (
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orking memory holds the results of the last "thought" and the input which triggers the next action. There is also a knowledge acquisition subsystem used to keep the knowledge base up to date, and a explanation system which can be used to explain the expert system's "reasoning." Expert systems also have a user interface of some type to provide information to the user.
In fact, all expert systems require that users be able to retrieve information quickly and effectively in order to be useful. To assist in this retrieval, developers have turned to natural language processing. This type of processing uses everyday speech patterns, words and grammar for commands. Users are able to use such systems more quickly than the command systems currently in use, and the results of natural language processing are promising.
Natural language processing is directly related to artificial intelligence (although it can be used with traditional computer techniques, as well). Concepts of an intelligent machine are inherently linked to its use and the manipulation of the primary representation of knowledge: words (Yoon, Guimaraes, & O'Neal, 1995, p. 85). The goal of natural language processing is to have computers understand and respond to comm
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Some common words found in the essay are:
Using EDIS, Systems Executive, Wide Web, Guimaraes O'Neal, North Carolina, CBT CD ROMs, Intranets Intranets, Thomas Kleiner, Data Corporation, Sumner Ryan, expert systems, information technology, information systems, executive information, executive information systems, access information, neural networks, document imaging, employees access, natural language, language processing, natural language processing, yoon guimaraes o'neal, journal systems management, guimaraes o'neal 1995,
Approximate Word count = 5324
Approximate Pages = 21 (250 words per page)
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