Artificial intelligence (AI) is a very challenging science, and people engaged in this work must understand computer knowledge, psychology and philosophy. Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, computer vision, etc. Generally speaking, the purpose of artificial intelligence is to make the computer, a machine, think like a human.
In 1955, Shannon co-developed The Logic TheoriST program, which is a program that uses a tree structure. When the program is running, it searches in the tree to find the most likely answer. Explore the branches of the close tree to get the correct answer. This program can be said to play an important role in the history of artificial intelligence. It has had such a huge impact on academics and society that many of the thinking methods we adopt still come from this program in the 1950s.
In 1956, McCaughey, another famous scientist in the field of artificial intelligence, convened a meeting to discuss the future development direction of artificial intelligence. Since then, the name of artificial intelligence has been officially established. This conference was not a huge success in the history of artificial intelligence, but this conference gave the founders of artificial intelligence an opportunity to communicate with each other and paved the way for the future development of artificial intelligence. role. After that, the focus of artificial intelligence began to become the establishment of practical systems that can solve problems on their own, and the system was required to have self-learning capabilities. In 1957, Shannon and others developed a program called the General Problem Solver (GPS), which extended Wiener's feedback theory and could solve some more general problems. While other scientists were working hard to develop systems, the scientist on the right made a major contribution. He created the table processing language LISP. This language is still used by many artificial intelligence programs, and it has almost become synonymous with artificial intelligence. , to this day, LISP is still developing.
In 1963, MIT received support from the U.S. government and the Department of Defense to conduct artificial intelligence research. The U.S. government was not for anything else, but to maintain a balance with the Soviet Union in the Cold War. Although This purpose is a bit gunpowder, but its result has led to tremendous development of artificial intelligence. Many programs developed subsequently are very eye-catching, and MIT developed SHRDLU. In this period of great development in the 1960s, the STUDENT system could solve algebraic problems, while the SIR system began to understand simple English sentences. The emergence of SIR led to the emergence of a new discipline: natural language processing. The expert system that appeared in the 1970s became a huge progress. For the first time, it was known that computers can replace human experts in some tasks. Due to the improvement of computer hardware performance, artificial intelligence can carry out a series of important activities. It is a part of life. Important aspects have begun to change human life. In terms of theory, the 1970s was also a period of great development. Computers began to have simple thinking and vision. It must be mentioned that in the 1970s, another artificial intelligence language, Prolog, was born. Together with LISP, it almost became An indispensable tool for artificial intelligence workers. Don’t think that artificial intelligence is far away from us. It has already entered our lives, including fuzzy control, decision support, etc. There are shadows of artificial intelligence. Let the computer, a machine, replace humans in simple intellectual activities and free humans for other more beneficial tasks. This is the purpose of artificial intelligence. Problem solving.
The first great achievement of artificial intelligence is the chess-playing program. Certain techniques applied in the chess-playing level, such as looking a few steps ahead, decompose difficult problems into some easier sub-problems, and develop into Basic artificial intelligence technologies such as search and problem induction. Today's computer programs are capable of playing various square boards and chess at tournament level. However, what has not yet been addressed includes abilities that human chess players have but cannot yet express clearly. Such as the ability of chess masters to understand the game.
Another problem involves the original concept of the problem, which is called the choice of problem representation in artificial intelligence. People can often find a way to think about the problem, which makes the solution easier and solves the problem. So far, AI programs have learned how to think about the problems they are trying to solve, i.e. search the solution space for better solutions. Logical reasoning and theorem proving.
Logical reasoning is one of the most persistent areas of artificial intelligence research, in which it is particularly important to find ways to focus only on relevant facts in a large database, paying attention to credible proofs, and revise these proofs as new information becomes available. Questions about speculation in mathematics. Finding a proof or disproof of a theorem not only requires the ability to perform deductions based on assumptions, but also many informal tasks, including medical diagnosis and information retrieval, can be formalized in the same way as theorem proving problems. Therefore, in the research of artificial intelligence methods Theorem proving is an extremely important topic. Natural language processing.
Natural language processing is a typical example of the application of artificial intelligence technology in practical fields. After years of hard work, this field has achieved a large number of eye-catching results. The main topic in this field is: how computer systems can generate and understand natural language based on topics and conversational situations, focusing on a large amount of common sense - world knowledge and expected roles. This is an extremely complex encoding and decoding problem. Intelligent information retrieval technology.
Affected by the rapid development of "()* (*) technology, information acquisition and refinement technology have become urgent topics in contemporary computer science and technology research. Apply artificial intelligence technology to this Research in the field is an opportunity and breakthrough for artificial intelligence to move towards widespread practical applications.
Expert system is currently the most active and most effective research field in artificial intelligence. It is a specific field. Program systems that contain a large amount of knowledge and experience. In the research of "expert systems" or "knowledge engineering", there has been a trend of successful and effective application of artificial intelligence technology. Human experts can achieve excellent problem solving because of their rich knowledge. ability. Then if the computer program can embody and apply this knowledge, it should also be able to solve the problems solved by human experts, and it can help human experts discover errors in the reasoning process. This has been confirmed, such as in mineral exploration and chemistry. In terms of analysis, planning and medical diagnosis, expert systems have reached the level of human experts. Successful examples include: the PROSPECTOR system discovered a molybdenum deposit worth more than 100 million US dollars, and the performance of the DENDRL system has exceeded the level of ordinary experts. Used by hundreds of people in chemical structure analysis, the MY CIN system can provide consultation on diagnosis and treatment plans for blood infectious diseases. With formal identification results, it has been able to diagnose and provide treatment plans for bacterial blood diseases and meningitis. More than experts in this field. Machine translation
Machine translation is also the most active research field in artificial intelligence. It is based on the three disciplines of linguistics, mathematics and computer science. Linguists provide dictionaries and grammar rules suitable for computer processing, mathematicians formalize and code the materials provided by linguists, and computer scientists provide software means and hardware equipment for machine translation, and perform program design. Machine translation cannot be achieved in any one aspect, and the quality of machine translation depends entirely on the joint efforts of these three aspects. Judging from the existing achievements, the quality of machine translation is still far from the ultimate goal. Far. Chinese mathematician and linguist Professor Zhou Haizhong once pointed out in the paper "Fifty Years of Machine Translation": To improve the quality of machine translation, the first thing to solve is the language itself rather than the programming problem; relying on a few programs alone It is definitely impossible to improve the quality of machine translation by building a machine translation system. At the same time, he also pointed out that before humans have yet to understand how the human brain performs fuzzy recognition and logical judgment of language, machine translation must achieve "faithfulness and expressiveness". ", elegance" level is impossible.