here is how a fuzzy logic system might evaluate water temperature. If the water is cold, it assigns 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 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). Th...
