The field of artificial intelligence (AI) encompasses the study of how to apply computers to problem domains that have traditionally been handled by humans only. A good example of such a problem domain is natural language. Current research in AI is attempting to find ways to use computers to automatically perform natural language translation and to recognize spoken words and convert them into written language. These applications of AI technology demonstrate the general goal of AI research:

To develop more powerful, versatile programs that can handle problems currently handled efficiently only by the human mind [Balci 1996].

In thinking about this goal, it is helpful to consider what types of problems the human mind can handle efficiently and what types of problems computers can handle efficiently. In his text Computer Science: An Overview, Glenn Brookshear makes the following comparison between humans and computers:

"Algorithmic machines are designed to perform precisely defined tasks with speed and accuracy, and they do this extremely well. However, machines are not gifted with common sense. When faced with a situation not foreseen by the programmer, a machine's performance is likely to deteriorate rapidly. The human mind, although often floundering on complex computations, is capable of understanding and reasoning. Consequently, whereas a machine might outperform a human in computing solutions to problems in nuclear physics, the human is much more likely to understand the results and determine what the next computation should be" [Brookshear 1997].

We can summarize Brookshear's comments in the following graph [Biermann 1990]. Notice that computer performance and task complexity are inversely related. This reflects the fact that computers are good at performing well-defined, repetitive computations but poor at complex tasks like reasoning. On the other hand, humans struggle to accurately add a long column of numbers without error while they have little problem conversing and reasoning with friends using natural language.

An important question arises when we consider the idea of computer intelligence: how do we determine whether a particular computer has demonstrated intelligence? The answer to this question really depends on the context in which you discuss artificial intelligence. From a philosophical perspective, "one considers questions regarding intelligence itself and whether machines can possess actual intelligence or merely simulate its presence." From an applied perspective, the question is "how technology can be applied to produce machines that behave in intelligent ways" [Brookshear 1997].

In these lessons, we will mainly focus on the applied perspective of AI although we will briefly consider the philosophical side in the next lesson. Some of the most important applications in the field of AI include natural language processing, visual processing, game playing, expert systems, and neural networks. In our study, we will look at some examples of these technologies. By the end of this section, you should be able to do the following:

References