Let's take a quick review of all that we have learned about artificial intelligence in these lessons.

1. 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.
   
   
2. 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!
   
   
3. 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, and context.
   
   
4. 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.
   
   
5. 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.
   
   
6. 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.
   
   
7. We briefly explored the parts of an expert system and saw several examples of these systems online.