Alan Turing and Artificial Intelligence Artificial Intelligence is known as a science of making computers do things usually is required by humans. It has definitely been a long process because of the attainment human intelligence has but it has slowly progressed. With success in limited and simplified domains. Intelligence in general is not built by one single ability but is built up of an arrangement of separate components. Dealing with Artificial Intelligence research is surrounded by the components of normal intelligence which are: learning, reasoning, problem solving, perception and language understanding. All of these have formed Alan Turing’s research on Artificial Intelligence. Learning is recognized by many different forms. The easiest form is trial and error. Which we have learned since young ages. For example, in a checkers game by trying a specific move and eliminating the option next time the computer with have already seen this move been done and will be able to recognize it almost immediately. There is also rote learning which is very simple to executing onto a computer. Generalization is a more challenging way of inputting problems and solving them. Generalizing problems into the computer leaves the learner to be able to act on further solutions to improve former solutions. It is then that it remembers the data that was put in the program previously.
Reasoning and problem solving complement each other by reasoning you are also eliminating some problems making it easier to solve the problem. Reasoning is laying out inferences that are dealing with the situation going on. The two are known as deductive and inductive. With deductive cases it is basically assured with the truth for the conclusion. Inductive just supports the truth for the conclusion. The problem with this and artificial intelligence is that it is very difficult to determine the relevant from irrelevant. In problem solving there are two methods used: general and special purpose. The methods are defined in their title, special focuses on a specific problem and embeds on particular features to that problem. In the case of general purpose it deals with a more broad circumstance of problems. Means and analysis is the step by step reduction of getting from the current solution to the ending solution which is used in the method of general purpose. Many actions are involved to getting from one goal to the other.
In perception environment is number one with real or artificial sense organs, scanning them to make an analysis to determine their relationships and features. Analyzing an object can be tricked to the human eye, if you look at an object at different angles and views it can be looked at like many different objects but is the same which makes this complicated. In relation to artificial perception it is a well advanced self motivated device that as a program of acting like a car such as controlling moderate speeds through roads and also cleaning up trash along the way.
With language understanding it I easy to misinform different signs, language is a system of signs by convention. Signs throughout life like stop lights giving us small language to stop and go and giving us direction. Natural meaning is a conventional way through showing language through nature’s aspects. Ways of separating languages are through productivity. Productive language lets an unlimited number of different sentences be formed together. It is easy for a computer to use different languages for answering questions and statements and they don’t always have to understand the language.
Just like in science there are three separate categories this research is put in to. First there is strong artificial intelligence, then simplified artificial intelligence and also cognitive simulation. All the above have different jobs they must follow. The one used the most highly is strong AI. Strong AI aims to create a machine that has the ability to in distinguish that between human