Artificial intelligence or AI also known as machine intelligence or MI and this is the reverse of natural intelligence which includes humans and other living beings, as everything fully depends and demonstrated by machine. Which, basically means the area which gives full emphasis on creating the intelligent machines that work very similar to human beings. Some artificial intelligence activities involved in the computer are as below:

  • Speech recognition
  • Learning
  • Planning
  • Problem-solving

CLASSIFICATION OF ARTIFICIAL INTELLIGENCE (AI)

Artificial intelligence or AI can be classified in various ways but important are the two ways. Artificial intelligence first classification is either weak AI or strong AI also known as narrow AI which is designed and trained for only a particular task. Personal assistants which are virtual like the Apple iPhone’s Siri are the form of weak AI.

The strong AI is just like the human brain and its cognitive capabilities whenever there is an unfamiliar task given it is capable enough to find the solution.

The work that is associated with artificial intelligence is very technical and needs specialization. However, there are some problems you get while working on computer coding for some important traits which includes:

  • Learning.
  • Reasoning.
  • Perception.
  • Problem-solving.
  • Language.

LEARNING

There are different forms of learning included in artificial intelligence and the simplest form is the trial and error one. For example- a simple program to solve the problems in computer chess game it tries the moves in a random manner until the mate is found. The program usually stores the moves or the position so, that when the next time the computer encounters the position it can declare the result accordingly. The memorizing of the simple items and procedures is called as rote learning which is very easy to carry out on the computer. And the most challenging one is the generalization. Which basically means applying past experience in the new incidents.

REASONING

This means drawing or summing up the appropriate inference according to the situation. And inferences are basically of two types that are inductive or deductive. For example, if we consider “George who went to the restaurant or to the airport. He is not in the airport so must be in the restaurant” similarly, “similar type of accident was caused previously due to the failure of the instrument, so this accident was caused due to the failure of the instrument”. So, the most important difference between inductive and deductive is that. In deductive the premise truth is being guaranteed by the truth of conclusion whereas in case of inductive the truth of premise is supported by conclusion truth without giving absolute assurance. In science, there is a lot of use of inductive reasoning the models are developed after the collection of tentative data to describe and predict the future behavior. While going to the deductive reasoning is common for mathematical or logical where the elaborate structures which are undeniable are built up with small sets of axioms and rules.
It has considerably gained a lot of success and draws an inference from computer programming, especially the deductive reasoning. Generally, the true reasoning always doesn’t contain the drawing inference, it includes something more than like drawing inference which related and relevant to the solution of the particular task or work. This is one of the difficult tasks that is done in the Artificial Intelligence.

PROBLEM-SOLVING

To reach a particular goal or target the systematic search done for it including a certain range of possible action is called as problem-solving which is an important aspect of artificial intelligence. There are two types of problem-solving methods general purpose and special purpose. For every specific tailored problem and gets exploited for any specific feature of the situation and the problems get embedded in it this is called a special purpose method. Whereas in case of general purpose it involves a wide variety of problems. This includes means-end analysis or the step-by-step and reduction of the difference present in the current state and final goal is called the general purpose technique. There are lists of means allows the program to select its action- this majorly includes the words which are for a simple robot that are PICK UP, PUT DOWN, MOVE FORWARD, MOVE BACK, MOVELEFT, MOVERIGHT until the desired goal is reached.

A lot of different problems have been solved by using this artificial intelligence. For examples, sequential moves in a board game, composing different mathematical proofs and manipulating virtual objects in the computer-generated world.

PERCEPTION

The environment is scanned by different sensory organs, real or artificial and the scene gets into different objects for different spatial objects this is known as perception. The analysis is always complicated and can’t deny with the fact that every object appears different from the different angle of view depends on the direction and illumination intensity in the scene and how the object gets contrast from the surrounding scene.

Nowadays artificial intelligence has become very much advanced and optical sensors can be enabled to identify the individuals, on the open road the motorcycles can drive at a moderate speed and robots can roam easily collecting the soda cans from building to buildings. The first system that implemented the perception and action was named as FREDDY, this robot has a television eye which was moving and pincer hand but the robot was stationary. It was constructed during the year the 1966-73 in Scotland. It was able to identify a variety of objects very easily and could assemble simple artifacts like toys and random heap components.

LANGUAGE

A system containing signs with a conventional meaning was called as Language. The language is just not confined to the words we write or speak. We have mini-language the traffic signals which delivers the message of having the hazard ahead. These are very distinct languages that the convention of the linguistic language posses and linguistic here means very different from the original one. A productive and successful language can result in unlimited and variety of sentences.
It would be very easy to write a program in human understandable language so that the responses, comments, queries can be easily understood. Although the machine doesn’t understand the language there are certain principles like the commands which are stored allows them to understand or distinguish between human understandable and non-understandable languages. Then evolves a genuine understanding where the computer even getting the commands fails to acknowledge the language. This thing still doesn’t have a permanent solution. And it depends upon a lot of factors, one’s behavior and history etc. can be trained and to take place in linguistic communication and by making it interactive.

REFERENCES

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