Features
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Automation of Business Rules: Non-Programmatic Representation Joe
DiGiovanni advocates automated business rules in a non-programmatic, business
analyst-maintainable manner to reduce development costs and simplify maintenance. |
Web Based Data Management: HTML vs. PDF vs. XML Elizabeth Thede
examines three approaches to searching and retrieving documents. |
AI Languages Provide Software Development Alternatives:
Imperative Versus Declarative Alex Ettinger tells you how to select
software development tools correctly for each stage of your software project. |
AI@Work WebFamilies Applies Probabilistic Spreadsheet Analysis
to Secure Venture Capital; Web Data Acquisition Appliances: Data Servers
Online; Data Mining Identifies and Targets Prospective Buyers: National
Household Database for Direct Mail Campaigning. |
Web-Based Expert Systems are on the Way: Java-Based Web Delivery Dustin
Huntington discusses the delivery of efficient expert systems via Java applets
for expert advice and problem-solving knowledge. |
Neural Networks Assist the Financial Auditor: Risk Reduction Going
Concern Evaluations Eben Sutton explores the feasability and accuracy
of two different models of risk prediction. |
New Data Mining Industry Standards: Moving from the Monks to the Mainstream
Eric Apps overview four current software standards initiatives: Extensible
Mark up Language (XML); Simple Object Access Protocol (SOAP); Predictive
Model Mark up Language (PMML); and Microsoft's OLE DB for data mining and
the effect on the next generation DM solutions. |
Regulars |
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Editorial |
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Intelligence Files - The Customer is Always Right |
by David Blanchard |
AI and the Net - Expert Systems at Work - From the
Farm to the Grocery Store |
by Mary Kroening |
The Book Zone - Fuzzy Cluster Analysis and Data
Mining: Concepts and Techniques |
by Will Dwinnell |
Product Updates -----------------------------> |
21 late breaking product announcements from
around the world in the fields of: |
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Business Forecasting |
Data Mining |
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Internet and Web |
Expert System Development Tools |
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Intelligent Portals |
Languages |
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Modeling and Simulation |
Neural Networks |
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Voice and Speech Recognition |
Announcements |
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Training |
Users Groups |
Product Service Guide - Provides access to information
on an entire category of products |
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PC AI Blackboard - AI advertisers bulletin board |
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Editorial:
Business Computing Tied to AI Research
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Whether the history of Artificial Intelligence (AI) started with the first
computer (1941) or an application like the Logic Theorist (Newell and Simon
- 1955), serious AI research and development quickly approaches the half
century mark (see www.aaai.org/Pathfinder/html/bbhist.html
or www.shai.com/ai_general/history.html
). The field of Artificial Intelligence (a term first expressed at the 1956
Dartmouth conference) continues to evolve and innovate. Early work by John
McCarthy (Lisp - 1958), the Department of Defense's Advanced Research Projects
Agency (ARPA the forerunner of DARPA - 1963), Edward Feigenbaum (DENDRAL
the first expert system - 1965), Alain Colmerauer (Prolog - 1972), Mark
Stefik and Peter Friedland (Molgen, the first object-oriented representation
of knowledge - 1978), and John Hopfield (Neural Networks - 1982) are among
those that led the way. |
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Much of this work has left the academic halls of research and joined the
competitive Information Technology mainstream. Knowledge management, object-oriented
development, data mining, and intelligent agents are but a few of the AI
fraternity graduates, now gainfully employed in the competitive business
world. Almost unnoticed is the extensive contribution that AI research has
made to today's computer based society - from the concept of time-sharing
(McCarthy), to the window/mouse based GUI (Xerox Parc), to the Internet
(DARPA) - AI research has played a key role. |
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As with any industry, companies and institutions come and go and key players
change. In the early years, Minsky, McCarthy, and Feigenbaum, were in turn
the leaders. Now technology has advanced and specialized to the point that
no single person or institution carries the AI banner. Each technology has
its own champions and cheerleaders, whether neural networks, intelligent
agents, fuzzy logic, or intelligent web portals - the list goes on. |
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PC AI began publishing in 1987, witnessing the evolution of these great
developments and the innovation of many others. Participating in this dynamic
landscape, and wrapping up our 14th year, are both exciting and rewarding.
As technologies evolve and change, so do we, focusing on the latest applications
of intelligent technology. |
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We couldn't keep up with the technology deluge without an excellent team
of columnists. David Blanchard, our AI industry watchman, surveys the past,
present, and future to enlighten us on what's happening in this fast growing
industry. David has already reported how many of the original AI companies,
long presumed dead, actually survived quite nicely, owing to the very power
and flexibility of their AI technology. Our commentator on web AI applications,
Mary Kroening, continues to uncover new AI based applications, available
at a browser near you. Will Dwinnell, our resident book review editor, searches
the world looking for the best books to recommend to our readers. In his
free time, he collects assorted pieces of this information and sends it
our way in the form of an article. |
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Intelligent web based applications, risk analysis, business modeling and
prediction, rule automation and data mining are all hot topics among the
business elite. This issue examines their application to business and finance.
The interest in web portals has spurred an interest in web-based expertise
and advisors, data acquisition as well as imporved web based data management.
Exploring risk analysis, modeling, and prediction we provide two excellent
examples: using intelligent risk analysis to support a venture capital business
model and reducing risk by improving prediction results through modeling.
An intelligent approach to rule automation utilizes business rules without
extensive rule programming. Another topic we cover is knowledge management
through data mining and the effects new industry standards will play. One
company utilizes data mining technology to optimize the results of their
target marketing. |
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We want to thank our readers for bringing us to our 15th year and best
wishes for a successful 2001. |
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Cheers! |
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Terry Hengl |