Ai And Expert Systems Essay Research Paper — страница 2

  • Просмотров 406
  • Скачиваний 12
  • Размер файла 20

strengths. Each one these elements is called a neurode. The term neurode is similar to the biological neuron. The term was modified slightly to indicate an artificial nature. Memory is stored by a certain pattern of the connection weights between the neurodes. Processing information is performed by changing and spreading the connection’s weights among the network. Before it can be used a neural network must be trained. Some can learn by themselves, some require training by doing, and others learn by trial and error. A computer learns by naturally associating items the computer is taught and grouping them together physically. Additionally, a computer can retrieve stored information from incomplete or partially incorrect clues. Neural networks are able to generalize categories

based on specifics of the contents. Lastly, it is highly fault tolerant. This means that the network can sustain a large amount of damage and still function. Its performance fades proportionally as the neurodes disappear (Butler and Caudill 8). This type of system is inherently an excellent design for any application that requires little human intervention and that must learn on the go. Created by Lotfi Zadeh almost thirty years ago, fuzzy logic is a mathematical system that deals with imprecise descriptions, such as “new”, “nice”, or “large” (Schmuller 14). This concept was also inspired from biological roots. The inherent vagueness in everyday life motivates fuzzy logic systems (Schmuller 8). In contrast to the usual yes and no answers, this type of system can

distinguish the shades in-between. In America a fuzzy logic system is used to analyze input from several cameras located at different intersections (Barron 114). This system provides a “smart light” that can decide whether a traffic light should be changed more often or remain green longer. In order for these “smart lights” to work the system assigns a value to an input and analyzes all the inputs at once. Those inputs that have the highest value get the highest amount of attention. For example, here is how a fuzzy logic system might evaluate water temperature. If the water is cold, it ass! igns a value of zero. If it is hot the system will assign the value of one. But if the next sample is lukewarm it has the capability to decide upon a value of 0.6 (Schmuller 14). The

varying degrees of warmness or coldness are shown through the values assigned to it. Fuzzy logic’s structure allows it to easily rate any input and decide upon the importance. Moreover, fuzzy logic lends itself to multiple operations at once. Fuzzy logic’s ability to do multiple operations allows it to be integrated into neural networks. Two very powerful intelligent structures make for an extremely useful product. This integration takes the pros of fuzzy logic and neural networks and eliminates the cons of both systems (Liebowitz 113). This new system is a now a neural network with the ability to learn using fuzzy logic instead of hard concrete facts. Allowing a more fuzzy input to be used in the neural network instead of being passed up will greatly decrease the learning

time of such a network. Another promising arena of AI is chaos engineering. The chaos theory is the cutting-edge mathematical discipline aimed at making sense of the ineffable and finding order among seemingly random events (Weiss 138). Chaologists are experimenting with Wall Street where they are hardly receiving a warm welcome. Nevertheless, chaos engineering has already proven itself and will be present for the foreseeable future. The theory came to life in 1963 at the Massachusetts Institute of Technology. Edward Lorenz, who was frustrated with weather predictions noted that they were inaccurate because of the tiny variations in the data. Over time he noticed that these variations were magnified as time continued. His work went unnoticed until 1975 when James Yorke detailed

the findings to American Mathematical Monthly. Yorke’s work was the foundation of the modern chaos theory (Weiss 139). The theory is put into practice by using mathematics to model complex natural phenomena. The chaos theory is used to construct portfolio’s of long and short positions in the stock market on Wall Street. This is used to assess market risk accurately, not to predict the future (Weiss 139). Unfortunately, the hard part is putting the theory into practice. It has yet to impress the people that really count: financial officers, corporate treasurers, etc. It is quite understandable though, who is willing to sink money into a system that they cannot understand? Until a track record is set for chaos most will be unwilling to try, but to get the track record someone