Artificial Intelligence (AI) is a branch of computer science that is trying to give "intelligence to machines." But the concept of intelligence in itself is controversial, and making these machines without life "smart" is something almost impossible. But we can safely say that the AI seeks to produce intelligent behavior from machines. What is the difference between intelligence and mental behavior? You can exercise intellectual behavior in a narrow field for some time without being really smart. For example, a computer that plays chess at the master level does not even know that it is playing chess. But for an outsider, the opinion is that he is clever as a master. We also need only this rational behavior for many practical purposes.
AI uses ideas from various fields of knowledge, such as computer science, economics, biology, social sciences, mathematics, and even grammar. It also has various applications in many areas of life. That is, it is an interdisciplinary question that takes ideas from virtually all areas of knowledge and has applications in many different areas of life. Some of the branches of AI are discussed below. This is in no way a complete list.
1. Playing games:
Playing games such as checkers, chess or moves requires a lot of intelligence for a person, and therefore these tasks were one of the earliest sights of AI. Samuel wrote a drafts program in the 60s, and many people contributed to game theory. Finally, when the computer could defeat the then world champion in a chess game, it was considered a victory of the machine over man, although this was not the case. Currently, there are well-known algorithms for the game, and the game is considered to fall into the scope of algorithms than AI.
2. Automatic theorem:
It is believed that mathematicians are supermassive creatures, and therefore, in early childhood, AI tried to show intelligence by creating machines capable of provoking theorems by themselves. Having some basic assumptions and rules, they tried to prove theorems by combining these rules, getting new assumptions, etc. A typical example is the Gelernters program for providing geometry. It was later realized that the intelligence of human experts in this field is not easily amenable to them, and common sense and knowledge in the field of mathematics must be used to prove the theorems. There are currently not many events in this area.
3. Natural language processing (NLP)
Languages such as English, French or Malayalam, which are used by men, are called natural languages. The language we speak often depends on the context. “Did you shoot a tiger?” Has different meanings when referring to a hunter and a photographer. Also our language is incomplete. Natural language processing deals with understanding this language using knowledge of the rules and context of grammar. This is a wide range of applications and is an area of active research. Also translation between these languages is studied in AI.
4. Vision, speech recognition and similar areas.
Observing the animal and recognizing it as a cat is a child's play, but a challenge for computers. Modern AI programs focus on the recognition of objects and faces and behavior based on vision. It has many uses in robot navigation, crime detection, hostilities, and so on.
5. Expert systems
Human experts are rare, expensive and die. If we spend a large amount and train a person as a neurologist, then the maximum that we can expect is 30-40 years. And we can not take a copy of the neurologist! Therefore, if we can train the computer in the same experience or be precise expert behavior, at least in a narrow field, the utility is high. Expert systems are engaged in extracting expert knowledge and transferring them to computers. This is the creation of software that can demonstrate expert behavior. Over the past few years, this field has undergone rapid growth.
6. Non-public networks
The brain of animals consists of neurons and calculations of the processes (thinking) passing through these neuron networks. Why not imitate this and develop the mind? Neural networks started from this base. They are able to learn, adapt and predict. Assuming a simple language, a neural network is a set of computational units (real or created) that are interconnected and interact to compute. Neural networks have applications in control systems, speech processing and natural language, vision, and many other areas.
There are different areas in AI. This is a vast and new area. I will tell about it in the following article.
