.. 9). 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 has to try it, it’s what is known as the catch-22.
The chaos theory can be useful in other places as well. Kazuyuki Aihara, an engineering professor at Tokyo’s Denki University, claims that chaos engineering can be applied to analyzing heart patients. The pattern of beating hearth changes slightly and each person pattern is different (Ono 41). Considering this discovery a dataprocessing company in Japan has marketed a physical checkup system that uses chaos engineering. This system measures health and psychological condition by monitoring changes in circulation at the fingertip (Ono 41).
Aihara admits that chaos-engineering has tremendous potential but does have limitations. He states, It can predict the future more accurately than any other system but that doesn’t mean it can predict the future all the time. Along these lines Rabi Satter, a computer consultant with a BS in Computer Science, believes that the current sentiment that the world is rational and can be reduced to mathematical equations is wrong. In order to make great strides in this arena [AI] we need new approaches informed by the past but not guided by it. A fresh voice if you would.
As one person said we are using brute force to solve the problem states Satter. A few more implementations of artificial intelligence include knowledge-based systems, expert systems, and case-based reasoning. All of these are relatively similar because they all use a fixed set of rules. Knowledge-based systems (KBS) are systems that depend on a large base of knowledge to perform difficult tasks (Patterson 13). KBS get their information from expert knowledge that has been programmed into facts, rules, heuristics and procedures. However, the power of a knowledge-based system is only as good as the knowledge given to it.
Therefore, the knowledge section is usually separate from the control system and can be updated independently. This enables system updates and additional information to be added in a more efficient manner then making a whole new system from scratch (O’Shea 162). Expert systems have proven effective in a number of problem domains that usually require human intelligence (Patterson 326). They were developed in the research labs of universities in the 1960’s and 1970’s. Expert systems are primarily used as specialized problem solvers. The areas that this can cover are almost endless.
This can include law, chemistry, biology, engineering, manufacturing, aerospace, military operations, finance, banking, meteorology, geology, and more. Expert systems use knowledge instead of data to control the solution process. In knowledge lies the power is a theme repeated when building such systems. These systems are capable of explaining the answer to the problem and why any requested knowledge was necessary. Expert systems use symbolic representations for knowledge and perform computations through manipulations of the different symbols (Patterson 329). But perhaps the greatest advantage to expert systems is their ability to realize their limits and capabilities. Case-based reasoning (CBR) is similar to expert system because theoretically they could use they same set of data.
CBR has been proposed as a more psychologically plausible model of the reasoning used by an expert while expert systems use more fashionable rule-based reasoning systems (Riesbeck 9). This type of system uses a different computational element that decides the outcome of a given input. Instead of rules in an expert system, CBR uses cases to evaluate each input uniquely. Each case would be matched to what a human expert would do in a specific case. Additionally this system knows no right answers, just those that were used in former cases to match. A case library is set up and each decision is stored.
The input question is characterized to appropriate features that are recognizable and is matched to a similar past problem and its solution is then applied. Now that each type of implementation of AI has been discussed, how do we use all this technology? Foremost, neural networks are used mainly for internal corporate applications in various types of problems. For example, Troy Nolen was hired by a major defense contractor to design programs for guiding flight and battle patterns of the YF-22 fighter. His software runs on five on-board computers and makes split-second decisions based on data from ground stations, radar, and other sources. Additionally it predicts what the enemy planes would do, guiding the jet’s actions consequently (Schwartz 136).
Now he and many others design financial software based on their experience with neural networks. Nolen works for Merrill Lynch & Co. to develop software that will predict the prices of many stocks and bonds. Murry Ruggiero also designs software, but his forecasts the future of the Standard & Poors index. Ruggiero’s program, called BrainCel, is capable of giving an annual return of 292%.
Another major application of neural networks is detecting credit card fraud. Mellon Bank, First Bank, and Colonial National Bank all use neural networks that can determine the difference between fraud and regular transactions (Bylinsky 98). Mellon Bank states the new neural network allows them to eliminate 90% of the false alarms that occur under traditional detection systems (Bylinsky 99). Secondly, fuzzy logic has many applications that hit close to home. Home appliances win most of the ground with AI enhanced washing machines, vacuum cleaners, and air-conditioners. Hitachi and Matsu*censored*a manufacture washing machines that automatically adjust for load size and how dirty the articles are (Shine 57).
This machine washes until clean, not just for ten minutes. Matsu*censored*a also manufactures vacuum cleaners that adjust the suction power according to the volume of dust and the nature of the floor. Lastly, Mitsubishi uses fuzzy logic to slow air-conditioners gradually to the desired temperature. The power consumption is reduced by 20% using this system (Schmuller 27). The chaos theory is limited in scope at this time mainly because of lack of interest and.