Info Tech Talk (Summer 1999)

Recent Technology Infusions – A look at the application of speech recognition and collaborative technologies into the IRMC program.

X Marks the Spot – Learn more about a new markup language that is revolutionizing the Web!

Reflecting on Technology - Professor Flanagan provides an interesting perspective on the role of technology.

As a means to meet business goals, information technology (IT) applications have evolved from data collection and analysis tools to more sophisticated mechanisms that focus on decision making. One of the challenges facing public and private organizations is to use IT applications to enhance strategic decision making. The purpose of article is to: (1) illustrate how the Information Resources Management College of the National Defense University (NDU) uses IT applications as an integral part of its curriculum to enhance strategic decision making, (2) discuss some of the insight gained, and, (3) draw parallels between experience gained at the NDU with potential use at corporate universities. The following scenario illustrates the use of IT at NDU:

Students in the Advanced Management Program walk into an enhanced decision making lab in Marshall Hall. They carry with them laptop computers pre-loaded with software including GroupSystems, a groupware application. They are about to embark on a two-hour exercise.

The 16 students fire up their laptops and access GroupSystems via a wireless local area network (LAN). In front of them are three screens capable of showing immersible and non-immersible virtual reality worlds. On screen #1, a virtual world appears showing a Virtual Command Center in Bosnia. "Intelligent avatars" roam the Virtual Command Center accessing valuable information.

On screen #2, intelligent agents (powerful search engines) are actively retrieving specific information from external sources. On screen #3, a group brainstorming session is underway incorporating the information feeds from the other two screens to address two strategic questions regarding future U.S. policy in Bosnia. At the conclusion of the exercise, the results are stored in a Lotus Notes database for future students to access.

The purpose of this exercise is to determine if students make better decisions when exposed to more realistic decision environments and where external information can help avoid groupthink. From a curriculum perspective, the exercise is part of a fourteen-week academic program that makes students use IT applications to address strategic business and national security challenges.

Students are not just exposed to sophisticated technologies. They are encouraged to integrate technologies to address strategic issues. Examining the scenario from a technology perspective, virtual reality, groupware, intelligent agents, wireless LANs and access to World Wide Web are all at play simultaneously. The challenge is to educate students to creatively use IT applications to address business needs. Solely relying on a field of dreams approach -- "if you build it, they will come" -- to implement technology based solutions is courting disaster.

The experience gained at the NDU is applicable at any corporate or public university. The immediate challenge for corporate universities is to integrate technologies and orchestrate scenarios that expose students to situations that enhance decision making in realistic environments. The long-term challenge for corporate universities is to start thinking strategically how to represent non-physical virtual worlds. It is easy to virtually represent buildings so you can "walk" through them in virtual space. It is much more difficult to represent abstract concepts (i.e., change management; policy processes; R&D processes) using appropriate metaphors that promote learning. Faculty at the IRM College has started creating virtual worlds that represent abstract concepts such as policy processes.

In one on-going experiment, a faculty member has created a prototype virtual world called "Pixelated Policy". The overall goal of this particular virtual world is to represent the dynamics of the federal information resources management policy process. At present, visual metaphors are key. For example, in the case of "Pixelated Policy", a cemetery replete with tombstones engraved with the names of failed policies is used to represent "dead" policy. Click on the tombstones and you get an explanation of the specific policy and why it failed. But are visual representations using color intensity, spatial and proximity relationships and movement the only way to represent concepts? No, it is only a matter of time before smell and tactile senses will be used. Imagine using the feeling and smell of a dead fish to represent something inherently bad.

The key challenge is to start experimenting to ascertain if the metaphors and representations chosen: (1) promote learning, and (2) contribute to meeting business objectives.

1999 has been an adventuresome year for the Information Technology Department of the IRM College. This article will explain how speech recognition and collaborative decision support have been incorporated into the curriculum. In both cases technology has been adapted to help make the IRM College courses different and exciting to our students, particularly the CIO certificate students who take so many of our offerings.

Speech recognition has been a part of the Technology Track and Critical Information Systems Technologies course for about 5 years. Speech recognition has advanced to the point where cultural changes are more important than technology breakthroughs. As a result, speech recognition remained a single lesson devoted to demonstrating and explaining the potential of the technology with some discussion of how it could be applied. In April 1999, Steve Knode wanted to change the instructional method. He wanted the students of the Intelligent Decision Systems (IDS) intensive course to use speech recognition and to work out for themselves how they would use it to their advantage.

Room 110 was equipped with microphones and 14 copies of Dragon Systems Point and Speak speech recognition software. The students have to spend about 45 minutes training the software to identify the unique way they talk. After the training the students send each other e-mails and generally test out their new capabilities. Even on relatively slow computers (Pentium 166-Mhz processors) the students have enjoyed the lesson and felt it was worthwhile. The first use of speech recognition by a student was completing the end-of-course survey using voice.

The labs are expected to receive new computers soon. When that happens, the software will be upgraded. In the near future Dragon Systems expects to release a network version of their software. This new version will require microphones that use the Universal Serial Bus (USB). The advantage to this potential upgrade is every computer on the network equipped with the USB can become voice enabled. This will certainly add to the lesson in the IDS course.

The second major technology occurrence is the use of Team Expert Choice in the Security and Electronic Commerce course. Jack Egan and Ai-Mei Chang have brought the Team Expert Choice software tool into their courses. Jack Egan uses Team Expert Choice to solicit student discussion in his Security course. He writes scenarios and offers the students possible outcomes. The students use radio controlled hand-held devices to enter their choice for desired outcomes. The computer tallies and displays the student feedback. Jack then directs the discussion parlaying the student’s inputs to spur further discussion.

Ai-Mei Chang takes a direct approach to using Team Expert Choice. Her approach is testing. She gives the students an on-line quiz. After each question the students key in their answer. She sees the results, tells the students the correct answer, informs them of the source of the answer, and when appropriate the significance of the question and answer. As course managers Jack Egan and Ai Mei Chang seem favorable to using the new technology. It helps the students stay involved in lessons and get more out of the course. New technologies and new ways of using technology help differentiate IRM College courses. These have been just three course manager’s innovations in this calendar year.

Drop the Phrase "Knowledge Management Conference" into the text box of your favorite search engine. You will likely find something very interesting - hundreds of conferences on Knowledge Management (KM). Drop the words "Knowledge Management Definition" into the same search engine and you will find something else very interesting - hundreds of definitions - for the same term! KM hasn't found its way into Webster's dictionary yet, but when it does we should all express our sympathy to the editors.

For many researchers who have spent careers studying and attempting to understand the sources and constructs of knowledge, this sudden interest in KM must seem very odd and very superficial. How can you manage something if you don't know what it is? How can you manage the most complex and perplexing thing on earth? How can you manage something as ethereal and temporal as knowledge?

Knowledge is obviously not something new but something very old - as old as man. The codification of "knowledge" is also very old. Cave drawings, Aristotelian logic, and language itself are all examples of codified knowledge. But likely more than any other entity, physical or abstract, knowledge is amorphous. It is constantly changing, it is constantly being reborn.

Cognitive scientists have gone to quite a bit of trouble and debate to at least classify knowledge into four categories. Those categories are perception, reasoning, memory and language. Each of these categories relates quite well to the way our brain seems to work. We perceive or sense through sight, sound, touch, taste, and smell. We remember by drawing upon our mental store - some recent events, some distant. We reason through processes which often confound us but are largely based upon drawing analogies to what has worked in the past. And we declare, through verbal and written language (or many other symbol systems) the results of the mixing of perception, memory, and reasoning.

Other researchers, most notably in the area known as intelligent systems, have gone to considerable trouble to create ontologies for representing knowledge. Ontology in the strict sense is the study of the nature of existence. As it relates to knowledge management it could be defined as the study of representational systems. Formal ontologies provide a basis for representing objects, functions, relationships, and events. Once these ontologies are established and then instantiated, or filled with specific cases, they can be utilized for later searches, drawing analogies or making conclusions.

There are many knowledge ontologies. But some of the better known ones include semantic networks, neural networks, predicate calculus, ID3, conceptual dependency diagrams, and CyC-L. These schemas are all quite complex, and require in-depth study. The Handbook of Artificial Intelligence provides a very effective primer in this area¹. These ontologies are also the fundamental technology in use in the rising intelligent agent arena.

Ultimately ontologies are tested for their efficacy to deliver systems which exhibit characteristics which humans would label as "intelligent." If an automated system responds to us in the same way we would expect an intelligent human to respond then we can say the system has artificial (man made) intelligence.

If the purpose of KM is to make it easier for us to draw upon the lessons learned by our predecessors or our own past experiences, then the criteria for judging a "good" KM system then is the same as those characteristics we would associate with an expert or sage. Just such characteristics were developed by researchers in the field of expert systems back in the early 1980s. But they hold just as well today.

An ideal knowledge management system would then provide this type of in-depth assistance.

Even if we understand knowledge and it's purpose, the challenge we yet face is how to best codify it. What is the best digital representation scheme for knowledge? What formalism and mechanism will provide the most benefit?

To that end there are two general approaches - formal and informal. The formal approach provides a codified ontology such as one of those itemized above. The informal approach allows the contributors to specify their own ontology. These approaches are not necessarily mutually exclusive, although no commercial product exists today which allows for both simultaneously. As an application example, imagine a video store such as Blockbusters. We are trying to determine where the films should be placed on the shelves. Using the formal approach, a detail analysis of each film would be performed. Pro forma rules would be established for classifying all the films.

For example if, in a film "Blazing Starships", there were more drama scenes than romantic scenes and the film star was Harrison Ford, we would classify it as an Adventure film. Using the informal approach, each customer seeing the film would write down their opinion in a book about the film. Future customers would read through the customer comment book about "Blazing Starships" and decide for themselves as to whether the film was worth watching. A combination system might read through the customer comments, and if the majority of customers classified it as adventure, it would formalize that classification.

	Formal	Informal
Internal Structure	codified, follows ontology	loose, user defined, database oriented
Basic Features	Q&A, reasoning, relevance	fast store, sort, query
Tracking	end point reasoning derivation	user defined threads
Display	narrow focus	angular, executive
Commercial Products	CyC	Lotus Notes

Both formal and informal approaches have their place in the organizational arena. It is likely best to start using the informal approach. Creating ontologies is a difficult process and requires a greater level of discipline and effort than providing an "open ended" tool to knowledge workers. Firms such as the "Big 5" accounting firms and multinational organizations have found success in keeping and sharing their workers informal knowledge using tools such as Lotus Notes. As in any system introduction however, managers must be aware of some of the traps that these knowledge collaboration tools may bring.

1. Barr, A. Cohen, P, and Feigenbaum, E., eds. The Handbook of Artificial Intelligence. vols I - IV. Addison Wesley Publishers. 1982.

2. Neilson, Robert. Collaborative Technologies and Organizational Learning. Idea Group Publishing. 1997.

Hypertext Markup Language is the current language of today’s web pages. This language uses a set of standard tags (similar to reveal codes in WordPerfect) to define the content of a web page. Web developers are limited to using these standard tags such as <P> to start a new paragraph or <bold> to "bold" some text. Along came the World Wide Web Consortium, a group which developed the Extensible Markup Language more popularly called "XML" to create user-defined tags. This approach adding a great deal of flexibility for web page design. People on the web can now share both the format and the data on the World Wide Web, intranets, and extranets.

Let’s say that computer vendors develop a standard way to describe the information about a computer product such as processor speed, memory size, and so on. XML tags could be defined based on this information and this would allow a user to send an intelligent agent to each vendor's Web site, gather data, and then make a valid comparison among vendor products.

Here’s another example. Perhaps we create a tag called <PHONENUM> to indicate that the data that followed it was a phone number. An XML file can be processed purely as data by a program. This means that it would be easy for a program to locate the phone number and process it. This data could be stored, displayed, or even dialed automatically.

It is expected that XML will have a tremendous impact on electronic commerce because it would be a way to define business data on the web. Examples of business data items include customer data, product data and supplier data. This is certainly an improvement over HTML which cannot define such data fields.

In 1995 I wrote the Manager's Guide to Expert Systems. Since four years have passed, I wanted to update this article. I thought I could simply change the applications I mentioned and draw some inferences from the new applications to shed light on the current trends in expert systems. However, I liked the original article and I did not identify a single trend in the applications worthy of special merit, therefore I have changed my intent from updating to commenting on the 1995 work.

I wish I had known in 1995 that expert systems would strengthen the technical infrastructure rather than attack specific management issues. Since 1995, expert systems have not forced their way into the mainstream, in fact, just the opposite has occurred. In the past 4 years, there are many excellent examples of fielded expert systems. What is surprising is that they tend to address technical rather than managerial issues. Expert systems automating the call center and help desk functions are very much the rage now. Expert systems handle telecommunications issues on the fly and in real time. What is important to glean from this observation? Technical issues are easily addressed with technical solutions. Expert systems enable technicians to solve a problem one time rather than devote the energy, time and talent to repeating the solution. Nowhere is this more important that at the help desk. Routine problems take up the vast amount of resources at a help desk. Expert systems give high quality solutions to repeatable problems. This in turn gives better service to the customers and frees up human experts to address the more exotic problems.

Another point I wish I had known in 1995 was that no one cares how a problem gets solved. A solution stands on its merits by how useful it is and not how wonderful the underlying technology is. In 1999, most end-users do not care how a problem gets solved, they do however want the problems solved. Should a vendor or Information Systems professional use expert systems to solve a problem it is the quality of the solution that impresses end-users. Furthermore, companies and agencies are willing to pay for solutions. As a result more and more expert systems are currently hidden from the end-users' view. End-users see the results, not the technology.

Continuing in the same thought, new applications like intelligent agents and biometrics have expert systems or rule based systems embedded in them. It is this coming together of differing technologies to solve a real problem that is exciting and interesting in 1999. Therefore, very important expert systems have blended into the technology landscape of 1999. While newer technologies capture the headlines, expert systems diligently solve many of the real technical and business problems. If I had a crystal ball in 1995, I could have foreseen these trends. But, if I had a crystal ball I would have written an intelligent agent as a front-end to an expert system that predicted the stock market and well … I hope you get the point.