Artificial Intellignece
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Artificial Intelligence has been a subject of great interest to people for many years. With the invention of modern day computers and programming languages, many believe that the era of Artificial Intelligence has been or is on the brink of being reached. Artificial Intelligence is a part of computer science that relates to the creation of a machine that can accomplish tasks that involve intelligence. There have been many examples of this in the media depicting the mainstream beliefs on Artificial Intelligence. There was the robot in the TV show, “Lost in Space,” C-3PO and R2D2 in “Star Wars,” etc. Should machines become independent in such a way that they can learn and think for themselves so that they could imitate and perform the complex tasks of a living person, then the era of Artificial Intelligence has been reached.
The theory of Artificial Intelligence was unfathomable until the invention of the Computer in 1941. The task of the computer during those times was to simply process data, but this was a revolutionary step that made people believe in the possibility of Artificial Intelligence. The next step that made Artificial Intelligence a current of thought among the Computer-minded population was the Feedback Theory developed by Norbert Wiener. This theory hypothesized that intelligence comes from the observation of ones surroundings and reacting to them in an appropriate manner. In 1956, John McCarty, considered to be the father of Artificial Intelligence, gathered the best and brightest of the field and held a conference addressing the subject of Artificial Intelligence. This conference laid the groundwork for Artificial Intelligence research. For the next several years, programs would be developed that would be used to perform logical tasks such as math problems.
In artificial intelligence, there must some sort of a framework, or philosophy to progress through the question about the nature of mind, to clarify goals, methods, concepts and to solve technical problems. Basically, programmers must develop a framework, or a “mind” with which the robot could use to think. This mind collects data as it encounters its surroundings over a period of time and through these experiences, creates its own assumptions of its present situation or state. In addition, Artificial Intelligence must have some elements of consciousness and subconsciousness. In this way, the robot must become like a character from a Virginia Woolf novel.
Throughout the history of computers, programmers have developed programming languages for the writing of computer programs. The most common computer languages used in implementing Artificial Intelligence has been LISP, PROLOG, C/C++, JAVA, and PYTHON. The language that is most traditionally used is LISP because of its relatively fast prototyping and fast execution. Also, since it has been used for a long time, many programmers have a much easier time drawing resources from the community in doing their own Artificial Intelligence related work. Java is a much more recently used program. It is argued that it is not as powerful, nor as fast as LISP, making it a useful programming language only when portability is paramount. The other languages are also useful for the implementation of Artificial Intelligence but LISP is the most mainstream.
There are many ways that AI can be used to solve a problem. Virtually all of these methods require special hardware and software to use them. Unfortunately, that makes AI systems expensive. 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 give a special edge that is needed to beat the competition.
New developments include neural networks, which have surprisingly successful results. A neural network is a type of information processing system whose architecture is similar to the structure of human neural systems. 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 one these elements are 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. Changing and spreading the connections weights among the network performs processing information. Before it can be used a neural network must be trained, because while some can learn by themselves most require training by doing. 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 and this type of system is an excellent design for any application