Let's take a quick review of all that we have learned about artificial
intelligence in these lessons.
- We introduced
the topic of artificial intelligence by discussing the types of problems
that computers can solve well and the types of problems that humans
can solve well. We concluded that computers are good at well-defined,
repetitive computations but poor at complex tasks like reasoning.
- We compared the
human mind to the fastest computers. Although computer technology has
grown rapidly in the past 20 years, the human mind still excels over
the computer in storage capacity and connection complexity. Computers,
however, excel at data transfer since the electrons that represent their
data travel at the speed of light!
- We investigated the field of natural
language processing, and we saw that since the English language
is ambiguous, it is difficult for computers to determine the precise
meaning of a sentence. Language processing software must consider three
aspects of a sentence when determining its meaning: syntax, semantics,
- We studied how computers approach the problem of game
playing. In order for computers to compete at games like chess and
checkers, they must search an immense number of potential moves to find
a good move. This is accomplished by constructing a game tree to represent
the possible moves from a given state of the game. By searching this
tree "intelligently", the computer can reduce the number of
moves it must consider.
- We examined two technologies that have emerged from the field of visual
processing. The first technology is optical character recognition
(OCR) and its primary purpose is to read text from paper and translate
the images into a form that the computer can manipulate. The second
technology is handwriting recognition and its primary purpose is to
convert handwritten language into machine editable text.
- We saw how artificial
neural networks can be used to solve problems that do not have an
algorithmic representation. One such class of problems is pattern recognition.
Artificial neural networks can be trained to recognize certain patterns
using known example patterns. Since handwriting recognition is really
a special case of pattern recognition, neural networks can be used to
solve this problem.
- We briefly explored the parts of an expert
system and saw several examples of these systems online.