Artificial Intelligence (AI) and the Expert System Defined
Consulting applies a knowledge-based system to commercial loan officers using multimedia (Hedburg 121). Their system requires a fast IBM desktop computer. Other systems may require even more horsepower by using exotic computers or workstations. The software used is even more exotic. Considering there are very few applications that are pre-written using AI, each company has to write it's own software to determine the solution to their specific problem.
An easier way around this obstacle is to design an add-on. The company FuziWare has developed several applications which act as additions to larger applications. FuziCalc, FuziQuote, FuziCell, FuziChoice, and FuziCost are all products used as management decision support systems for other off -- the shelf applications (Barron 111).
In order to tell that AI is present, we must be able to measure the intelligence being used. For a relative scale of reference, large supercomputers can only create a brain the size of a fly (Butler and Caudill 5). It is, however, surprising what a computer can do with that intelligence once it has been put to work.
Almost any scientific, business, or financial industry can greatly benefit from Artificial Intelligence. The computer's ability to analyze variables provides a great advantage to these individuated fields. There are many ways that AI can be used to solve a problem. Virtually all these methods require special hardware and software to use them; making AI systems expensive to employ. Consulting firms -- companies that design computing solutions for their clients -- have offset that cost with the quality of the system. Many new AI systems now provide a special edge that is required to upstage the competition.
Three Major Components of an Expert system
Artificial intelligence includes knowledge-based systems, expert systems, and case-based reasoning. Each 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). Knowledge-based systems get their information from expert knowledge that has been programmed into facts, rules, heuristics, and procedures. The power of a knowledge-based system, however, is only as good as the knowledge provided.
The knowledge section, therefore, is usually separate from the control system and can be updated independently. This process enables system updates and additional information to be added in a more efficient manner than creating a new system from scratch (O'Shea 162).
Expert systems have proven effective in a number of problem domains that typically require human intelligence (Patterson 326). These intellectual systems were developed in the university research labs in the 1960's and 1970's. Expert systems are primarily used as specialized problem solvers. The areas that can be covered by AI specialized problem solvers are almost endless; law, chemistry, biology, engineering, manufacturing, aerospace, military operations, finance, banking, meteorology, and geology are but a few of the applicable disciplines..
Expert systems use knowledge instead of data to control the solution process. Expert systems use symbolic representations for knowledge and perform computations through manipulations of different symbols (Patterson 329).
Perhaps the greatest advantage to expert systems, however, is their ability to realize their limits and capabilities. Case-based reasoning (CBR) is similar to an expert system because, theoretically, they could employ the same set of data. CBR has been proposed as a more psychologically plausible model of the reasoning used by an expert and 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 given input. Instead of the standardized rules in an expert system, CBR uses cases to evaluate each input uniquely. Each case is then matched to what a human expert would do in a similar and specific case.
Additionally this system knows no right answers -- just those that were used to match with former cases. A case library is set up and each decision is stored. The input question is characterized to appropriate recognizable features and then matched to a similar past problem where its solution is then applied.
Neural networks have entered the spotlight with surprisingly successful results. A neural network is a type of information processing system whose architecture is similar to the structure of biological neural systems (Butler and Caudill 5). The neural network tries to mimic the way a brain and nervous system work by analyzing sensory inputs and calculating an outcome. A neural network is usually composed of simple decision-making elements that are connected with variable weights and strengths. Each of these elements is called a neurode. The term neurode is similar to the biological neuron; modified slightly to indicate its 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 still others learn by trial and error. A computer learns by naturally associating items, being taught, and grouping like items 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. Like the human body, the neural network's 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 must learn 'on the fly'.
Created by Lotfi Zadeh nearly thirty years ago, fuzzy logic is a mathematical system that deals with imprecise descriptions, such as "new," "nice," or "large" (Schmuller 14). Similar to the neurode, this concept was inspired from biological roots, too.
The inherent vagueness in everyday life motivates fuzzy logic systems (Schmuller 8). In contrast to the usual yes and no answers, this type 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 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 assigns a value of zero. If it is hot, the system will assign the value of one. If the next sample is lukewarm, however, 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 degrees of importance.
Moreover, fuzzy logic lends itself to multiple simultaneous operations. Fuzzy logic's ability to do multiple operations allows it to be easily integrated into neural networks. Two very powerful intelligent structures make for an extremely useful product. This integration takes the advantages of fuzzy logic and neural networks and eliminates the disadvantages of both systems (Liebowitz 113).
This new system is a now a neural network with the ability to learn by using fuzzy logic instead of 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 on 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 chaos theory came to life in 1963 at the Massachusetts Institute of Technology. Edward Lorenz -- frustrated with weather predictions -- noted that they were inaccurate because of 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
Yorke's work went on to become 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 portfolios of long and short positions in the stock market on Wall Street. This data is used to accurately assess…