The Rise of Intelligent Agents: Automated Conversion of Data to Information

Martha Young and Michael Jude   Today’s Top Stories   or  Other Data Mining Stories  

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February 5, 2004 (Computerworld) -- Business process virtualization (BPV) is fundamentally about using automation and intelligent networked technologies to increase efficiency, reduce costs and improve employee/customer interaction. Additionally, though, it's about the application of information to decision-making.
Information isn't the same as data. Analysis through paralysis, where the quality of decision-making is thought to be proportional to the amount of available data, is the logical consequence of a failure to distinguish between the two. BPV provides tools for reducing data to information. These are largely management approaches to analysis, but there is also an increasing number of automated tools for building and simulating decisions.
Of course, it would be ideal if there were technologies that could automatically generate decision models in response to rational questions. In such an approach, one would ask a computer to independently collect and analyze data, draw conclusions and present the results. In such an approach, one could, for example, ask a computer terminal to search all internal company sources and externally reachable sources for information on a specified competitor, its products and its potential threat to the business in a particular market. Within minutes, a neat two-page summary would be delivered with appropriate footnoting. Better yet, one could ask the computer how it arrived at its synopsis and receive cogent replies.
Such technology has been of abiding interest for some time. The HAL 9000 computer of 2001 fame is perhaps the most famous example of fictional approaches to such technology; Star Trek's library computer is another. In fact, such fictional approaches are so well known that it frequently comes as a surprise to people who are unfamiliar with real technology that such machine-based intelligence isn't available.
A so-called intelligent agent is difficult to achieve because computers, unlike humans, have no ability to infer context from data. Where humans can usually be counted on to figure out conversational dialogue when spoken in a language familiar to them, this task is very difficult for a computer. Humans have the ability to bring a lifetime of experience to a pronouncement and arrive at a good approximation of its intent. Computers, at least so far, have had only rudimentary capabilities along these lines. Computers generally achieve some semblance of context assessment through a series of rather sophisticated if-then rules. This brute-force method is time-consuming and requires large quantities of storage.
In Stephen Cass' article, "A Fountain of Knowledge," appearing in "IEEE Spectrum Online," he discusses IBM's activities in developing just such a computing agent. This concept, named WebFountain, is currently a plethora of rack-mounted processors, routers and over 160TB of disk space consuming a footprint the size of half a football field. Looking back

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