https://www.investopedia.com/terms/k/knowledge-engineering.asp
http://terms.naer.edu.tw/detail/1306714/
Machine
learners versus knowledge engineers
The most determined
resistance comes from machine learning’s perennial foe: knowledge engineering.
Unfortunately, the two camps often talk past each other. They
speak different languages: machine learning speaks probability, and knowledge
engineering speaks logic.
Even if by some
miracle we managed to finish coding up all the necessary pieces, our troubles
would be just beginning. Over the years, a number of research groups have
attempted to build complete intelligent agents by putting together algorithms
for vision, speech recognition, language understanding, reasoning, planning,
navigation, manipulation, and so on. Without a unifying framework, these
attempts soon hit an insurmountable wall of complexity: too many moving parts,
too many interactions, too many bugs for poor human software engineers to cope
with.
Why pay experts to
slowly and painfully encode knowledge into a form computers can understand,
when you can extract it from data at a fraction of the cost? What about all the
things the experts don’t know but you can discover from data? And when data is
not available, the cost of knowledge engineering seldom exceeds the benefit.
Domingos, Pedro. The Master Algorithm:
How the Quest for the Ultimate Learning Machine Will Remake Our World (pp. 34-38).
Basic Books. Kindle edition.
