Over the past 15 years, Artificial Intelligence (AI) techniques have shown
promise of providing the ability to capture `human intelligence' into a
computer program. The sub-fields of Expert Systems and Artificial
Neural Networks are finding increased use in industry to perform tasks
previously considered the domain of human thinking. But, are these
methods just a new type of software much like that of the past or is real
magic hidden within the lines of code? Will this field continue to enjoy
high growth rates and if so, what developments will we see in the near
future? Which algorithms and methodologies will continue to grow? This paper discusses a number of paradigms which can be used to
create successful applications. There are certain barriers; some
psychological - some linked with knowledge acquisition, that must be
overcome or avoided to ensure system performance. Development time
also requires careful consideration before embarking on an Al project.
The paper focusses on constructing goal-driven systems that transfer
high-technology solutions from the research laboratory to the plant floor. Respect for system users is of paramount importance - whether their
involvement is to access contained information or to participate in
program maintenance as new knowledge becomes available. Examples of
successful and unsuccessful AI implementation in Canadian industry and
academia provide evidence of the importance of this issue. The paper also
examines some current constraints on applying AI in the real-world.
Among these problems are automated knowledge acquisition, methods to
acquire data during run-time, real-time systems (hardware versus
software), explanation and justification features and adaptive/leaning
systems.