Transferring Technology

I read one interview of Bob Orchard, the Group Leader of the Integrated Reasoning in NRC-IIT at IEA/AIE conference 2004. Bob has good points on transferring technology to companies. Bob’s group has long term projects with Air Canada and Canadian Railway Via for diagnosis systems.

From his opinion, industrial companies have a reluctance to accept anything that they don’t understand or that has not proven itself. Many companies appear to be late adopters and unwilling to put even a relative small effort into testing new technology. One needs to be persistent, to be upfront with the clients about the expectations of the technology and their need to be involved, to use real data with all of its deficiencies, and to be willing to go beyond just a simple prototype. Here is his recipe:

• Convince the company to do a study where you demonstrate the benefits to them.
• Keep the cost to the company as low as possible, but don't do it without getting commitment to the project and all of the support you need (access to any real data, to company expertise, and to those who will be most affected by the technology).
• Make sure that everyone is very clear about what the expectations are for the new technology: what it will do and what it won't do; what the risks are that could impede its success.

Avoid Low Level Busy

Enjoying long working hours is the life style of researchers. My body and brain are used to it. Daniel told me, busy won’t lead to the objectives. For me, I need to spend more time in writing killer papers. He is really my friend.

What’s on Daniel’s list :

has a rich personal life;

gives great courses;

gets a lot of funding;

has many students;

publishes a lot papers each year;

consults on industrial/governmental projects;

manages something (departement, project, program).

Daniel’s opinion is to do one or two at once.

When I am teaching

Time is so tight when I am teaching. It is the first time I teach web services in the e-business course. It is a good challenge to teach a new topic. How can I make myself clear? How can I make student work out?

One fundamental hint for me to teach and choose the research topic for the students (master students) is to make sure that the training will bring the students good career prospective. Most of the students come to university for a professional training (the rest are for passing time). So the professors have to think what training you provide to your students so that they can get a job after graduation. I found Canadian Universities do not use the employment rate and the income as index to rank the universities as American Universities. I also found the courses in computer science in some Canadian Universities are too far away from the market demands. The professors like to teach the students the subjects they are good at. They do not bother to look into the demand of the industry, or they do not care the future of their students. That is too bad.

The registration rate of my class proves that the students are smart. Well, some students are really smart. But not all. Quit a few students do not have enough experiences to debug their code. Their ability to solve problems is quite low. They work only under well tuned situation, and solve very simple problems. If they do not improve, I do not think they can work in an industrial environment. That is just training. Why are they like this?

For most of the Chinese students, which are a big portion in my class, I found our Chinese style training build a lot of followers, not the initiators. They are satisfied in knowing what you teach them. They have weak ability to explore the new world. My current master student proves this again and again. He never did something more than me, even the trivial things. I am tired of this. Do I not demonstrate enough?

Grid Computing vs. Web Services vs. Semantic Web

Grid computing views computing, storage, data set, expensive scientific instruments and so on as utilities to be delivered over the Internet seamlessly, transparently, and dynamically as and when needed by the virtualization of these resources [1].

Applications: Scientific computation and collaboration for large scale problems. Examples:

Challenges: query data, visualization, routing, remote control, network management issues

Associated Buzz Words: virtual organization

The term Web services describes a standardized way of integrating Web-based applications using the XML, SOAP, WSDL and UDDI open standards over an Internet protocol backbone. XML is used to tag the data, SOAP is used to transfer the data, WSDL is used for describing the services available and UDDI is used for listing what services are available.

Application: Business Processes Integration, e.g. supply chain management

Challenges: web services discovery, composition,

Associated Buzz Words: virtual enterprise, workflow

Semantic Web, by W3C, “… an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. It is the idea of having data on the Web defined and linked in a way that it can be used for more effective discovery, automation, integration and reuse across various application..

Applications: portals, semantic enhanced query,

Challenges: machine understandability

[1] Foster, I. and Kesselman, C., The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann, 1999.

A Long Trip Back

I came from my vacation in Pyrenees and ECAI conference in Valencia. Both events were exhaust. European Conference on Artificial Intelligence is one of the top conferences in AI. It has a style of traditional science in Europe.

For my research area, MBD is going to distributed system, agent-based system, plan + monitoring + diagnosis combined technology. Qualitative modeling, FMEA, and new industrial oriented application are still in the center place. Qualitative Reasoning is now for Q2Q problem, which uses qualitative model to refine the quantitative model. Machine learning attracts more attention as a modeling technique. I found I need more knowledge in the whole domain to become an expert.

Semantic Web is an effort of the AI people, though industry companies try to stay away from it. From research point of view, it dose not try to solve the open questions in knowledge representation, but to build its strength from zero to reach a level the current theories allow it to reach. People can argue that semantic web studies do not focus enough on “web” (stolen from a French woman professor whose name is unknown to me). The innovative applications are emerging, as people are calling on them.

Internet should provide more excited applications for the AI people. ECAI is not the best place to see it.