Introduction to artificial intelligence: artificial intelligence, abbreviated as AI in English. It is a new technical science to study and develop theories, methods, technologies and application systems that simulate, extend and expand human intelligence. Artificial intelligence is a branch of computer science, which tries to understand the nature of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. The research in this field includes robot, language recognition, image recognition, natural language processing and expert system. Since the birth of artificial intelligence, the theory and technology are becoming more and more mature, and the application fields are expanding. It is conceivable that the technological products brought by artificial intelligence in the future will be the "container" of human intelligence, and may also exceed human intelligence.
Second, research value: for example, heavy scientific and engineering calculations are originally undertaken by the human brain. Today's computers can not only do this kind of calculation, but also do it faster and more accurately than the human brain. Therefore, contemporary people no longer regard this kind of calculation as "a complex task that needs human wisdom to complete". It can be seen that the definition of complex work changes with the development of the times and the progress of technology, and the specific goals of artificial intelligence naturally develop with the changes of the times. On the one hand, we have made continuous progress, on the other hand, we have turned to more meaningful and difficult goals.
The mathematical basis of general machine learning is statistics, information theory and cybernetics. It also includes other non-math subjects. This kind of "machine learning" is highly dependent on "experience". Computers need to constantly acquire knowledge and learn strategies from the experience of solving a class of problems. When encountering similar problems, they use experience and knowledge to solve problems and accumulate new experiences, just like ordinary people. We can call this way of learning "continuous learning". But in addition to learning from experience, human beings can also create, that is, "jumping learning." This is called "inspiration" or "epiphany" in some cases. All along, the most difficult thing to learn about computers is "epiphany". Or more strictly speaking, it is difficult for computers to learn "qualitative change independent of quantitative change" in learning and practice, and it is difficult to directly change from one property to another, or from one concept to another. Because of this, "practice" here is not the same as human practice. The process of human practice includes both experience and creation. This is what smart researchers dream of.