programming-for-dummies

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Problem Solving
Although expert systems have proven to work within a limited domain, such
as troubleshooting an aircraft or diagnosing patients for diseases, they
haven’t proved popular in commercial applications.
One problem is the difficulty in extracting a human expert’s knowledge and
translating it into a set of rules. After you’ve translated an expert’s knowledge
into a set of IF-THEN rules, a second problem is debugging these rules with
the help of the human expert to make sure the expert system works just like
the human expert. A third problem is that expert systems are clumsy to use.
When someone wants help with a problem, he can tell a human expert what
the problem may be and the human expert can start examining the problem
directly. Computer expert systems don’t have that luxury, so using an expert
system is like playing a game of Twenty Questions with the expert systems
constantly asking questions, like “What is the white blood cell count?” After
the human user responds, the expert system bombards the user with
another question to get more information. Using an expert system can be
slow, clumsy, and frustrating, which is why they’re rarely used despite so
many years of research.
Besides the problem of storing a human expert’s knowledge into a series of
IF-THEN rules, a final problem is updating the expert system’s knowledge,
which requires interviewing the human expert all over again and then debugging
the expert system once more to make sure the update information is
accurate. Given these problems, expert systems are more often too much
trouble to use than they’re worth.
Natural language processing
In science fiction movies, artificially intelligent computers are always able to
understand human language, which is known as natural language processing
(NLP). The goal of NLP is to make computers even easier to use. By accepting
spoken or written commands to the computer, NLP frees users from having to
learn the arcane and cryptic syntax of ordinary computer commands.
The first problem with understanding any human language is to understand
the meaning of each specific word. This problem is rather trivial because it
involves nothing more than identifying a word and then looking up its meaning
in a dictionary data structure that links the word to its definition.
If human language was logical, this would be all that NLP would have to do.
Unfortunately, the meaning of words often depends on their context, which
is difficult to program into a computer. For example, the phrases fat chance
and slim chance actually mean the same thing although the adjectives fat and
slim might seem like antonyms.