Artificial intelligence (AI). 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. Artificial intelligence is a new technology science that studies and develops the theories, methods, technologies and application systems that simulate, extend and expand human intelligence. Since the birth of artificial intelligence, the theory and technology have become increasingly mature, and the application fields have been expanding, but there is no unified definition.

Artificial intelligence is a simulation of the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can think like human beings, or it may exceed human intelligence. But this kind of self-thinking advanced artificial intelligence still needs breakthroughs in scientific theory and engineering.

Artificial intelligence is a challenging science, and people engaged in this work must understand computer knowledge, psychology and philosophy. Artificial intelligence is a very extensive science, which is composed of different fields, such as machine learning and computer vision. Generally speaking, one of the main goals of artificial intelligence research is to enable machines to be competent for some complex tasks that usually require human intelligence. But in different times, different people have different understandings of this "complex work".

The definition of industrial intelligence can be divided into two parts, namely "artificial" and "intelligence". "Artificial" is easier to understand and less controversial. Sometimes we have to consider what human beings can do, or whether human intelligence is high enough to create artificial intelligence, and so on. But in general, "artificial system" is an artificial system in the usual sense.

There are many questions about what "intelligence" is. This involves consciousness, ego, mind (including unconscious mind) and other issues. It is generally believed that the only intelligence people know is their own intelligence. However, our understanding of the necessary elements of our own intelligence and human intelligence is very limited, so it is difficult to define what "artificial" intelligence is. So the study of artificial intelligence often involves the study of human intelligence itself. Other intelligence about animals or other artificial systems is generally considered as a research topic related to artificial intelligence.

Artificial intelligence has been paid more and more attention in the computer field. It has been applied to robots, economic and political decision-making, control systems and simulation systems.

Artificial intelligence robot

Professor Nelson of the famous Stanford University Artificial Intelligence Research Center in the United States defines artificial intelligence in this way: "Artificial intelligence is a subject about knowledge-how to express knowledge, how to acquire and use knowledge." Another professor Winston of Massachusetts Institute of Technology thinks: "Artificial intelligence is to study how to make computers do intelligent work that only people can do in the past." These statements reflect the basic ideas and contents of artificial intelligence. That is, artificial intelligence is the basic theory, method and technology to study the law of human intelligence activities, construct an artificial system with certain intelligence, and study how to make computers do the work that needed human intelligence in the past, that is, how to use computer software and hardware to simulate some intelligent behaviors of human beings.

Artificial intelligence is a branch of computer science, which has been called one of the three frontier technologies (space technology, energy technology and artificial intelligence) in the world since 1970s. It is also considered as one of the three frontier technologies (genetic engineering, nano-science, artificial intelligence) in 2 1 century. This is because it has developed rapidly in the past 30 years, has been widely used in many disciplines, and has achieved fruitful results. Artificial intelligence has gradually become an independent branch, with its own system in theory and practice.

Artificial intelligence is a subject that studies how to make computers simulate some human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). ). It mainly includes the principle that computers realize intelligence, which makes computers similar to human brain intelligence and enables computers to achieve higher-level applications. Artificial intelligence will involve computer science, psychology, philosophy and linguistics. It can be said that almost all disciplines of natural science and social science have gone far beyond the scope of computer science. The relationship between artificial intelligence and thinking science is the relationship between practice and theory. Artificial intelligence is at the technical application level of thinking science, and it is an application branch. From the perspective of thinking, artificial intelligence is not limited to logical thinking, only image thinking and inspiration thinking can promote the breakthrough development of artificial intelligence. Mathematics is often regarded as the basic science of many disciplines, and mathematics has also entered the field of language and thinking. The subject of artificial intelligence must also borrow mathematical tools. Mathematics not only plays a role in the scope of standard logic and fuzzy mathematics, but also enters the discipline of artificial intelligence, which will promote each other and develop faster.

2 Research Value Editor

Robots with artificial intelligence

For example, heavy scientific and engineering calculations were 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. [ 1]

This is what smart researchers dream of.

S.C WANG, a data researcher in Digin Data Center in 2065438+03, developed a new data analysis method and derived a new method to study the properties of functions. The author finds that the new data analysis method provides a "creative" way for the computer society. In essence, this method provides a quite effective way to simulate human creativity. This practice is endowed by mathematics, and it is a "ability" that ordinary people can't have but computers can have. Since then, computers are not only good at calculation, but also good at creation because they are good at calculation. Computer scientists should categorically deprive "creative" computers of their over-comprehensive computing power, otherwise computers will really "catch" human beings one day. [ 1]

When reviewing the derivation process and mathematics of the new method, the author expanded his understanding of thinking and mathematics. Mathematics is concise, clear, reliable and modeled. In the history of mathematics development, the brilliance of creativity of mathematics masters shines everywhere. These ideas are presented in the form of various mathematical theorems or conclusions, and the greatest feature of mathematical theorems is that they are based on some basic concepts and axioms, expressed in a modular language and rich in information. It should be said that mathematics is the simplest and straightforward subject that embodies (at least one) creative model. [ 1]

3 Introduction to Science Editor

practical application

Machine vision: machine vision, fingerprint recognition, face recognition, retina recognition, iris recognition, palmprint recognition, expert system, automatic planning, intelligent search, theorem proving, games, automatic programming, intelligent control, robotics, language and image understanding, genetic programming, etc.

Subject category

Artificial intelligence is an interdisciplinary subject, which belongs to the intersection of natural science and social science.

Involving disciplines

Philosophy and cognitive science, mathematics, neurophysiology, psychology, computer science, information theory, cybernetics, uncertainty theory.

Research category

Natural language processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combinatorial scheduling problem, perception problem, pattern recognition, soft computing of logic programming, imprecise and uncertain management, artificial life, neural network, complex system, genetic algorithm.

Consciousness and artificial intelligence

Artificial intelligence is essentially a simulation of human thinking information process.

The simulation of human thinking can be carried out in two ways. One is structural simulation, which imitates the structural mechanism of the human brain and creates a "brain-like" machine; The second is functional simulation, which temporarily abandons the internal structure of the human brain and simulates it from its functional process. The appearance of modern electronic computer is a simulation of the thinking function of human brain and an information process of human brain thinking.

Weak artificial intelligence is developing rapidly now, especially after the economic crisis in 2008, the United States, Japan and Europe all hope to realize re-industrialization through robots. Industrial robots are developing at an unprecedented speed, which further promotes the continuous breakthrough of weak artificial intelligence and related industries. Many jobs that must be done by people can now be done by robots.

However, strong artificial intelligence is temporarily at the bottleneck and requires the efforts of scientists and humans.

4 development stage editor

1in the summer of 956, a group of far-sighted young scientists, led by MacArthur, Minsky, Rochester and Shennong, got together to study and discuss a series of related problems of using machines to simulate intelligence, and put forward the term "artificial intelligence" for the first time, marking the formal birth of this new discipline. IBM's "Deep Blue" computer defeated the human world chess champion, which is the perfect performance of artificial intelligence technology.

Since 1956 was formally put forward, artificial intelligence has made great progress in the past 50 years and has become a broad cross-cutting and frontier science. Generally speaking, the purpose of artificial intelligence is to make computer machines think like people. If you want to build a thinking machine, you must know what thinking is, and further, what wisdom is. What kind of machine is intelligent? Scientists have made cars, trains, planes, radios and so on. They imitate the functions of our body organs, but can they imitate the functions of the human brain? So far, we only know that this thing in our crown is an organ composed of billions of nerve cells. We know very little about this thing, and imitating it may be the most difficult thing in the world.

When the computer appeared, human beings began to really have a tool that can simulate human thinking. In the following years, countless scientists worked hard for this goal. Nowadays, artificial intelligence is no longer the patent of several scientists. Almost all computer departments of universities in the world are studying this subject, and college students who study computer must also take such a course. Thanks to everyone's unremitting efforts, computers now seem to have become very intelligent. For example,1In May 1997, the deep blue computer developed by IBM defeated Kasparov, the chess master. You may not have noticed that in some places, computers help people do other jobs that originally belonged to human beings, and computers play a role for human beings with their high speed and accuracy. Artificial intelligence has always been the frontier subject of computer science, and computer programming languages and other computer software also exist because of the progress of artificial intelligence.

5 technical research editor

The machine used to study the main material basis of artificial intelligence and realize the technical platform of artificial intelligence is the computer, and the development history of artificial intelligence is linked with the development history of computer science and technology. In addition to computer science, artificial intelligence also involves information theory, cybernetics, automation, bionics, biology, psychology, mathematical logic, linguistics, medicine, philosophy and many other disciplines. The main contents of artificial intelligence research include: knowledge representation, automatic reasoning and search methods, machine learning and knowledge acquisition, knowledge processing system, natural language understanding, computer vision, intelligent robots, automatic programming and so on.

Artificial Intelligence and Robot Research is an international Chinese periodical, focusing on the latest progress in the field of artificial intelligence and robot research. It is published by Hans Publishing House. This journal supports ideological innovation and academic innovation, advocates scientific prosperity and integrates academics and ideas. It aims to provide a communication platform for scientists, scholars and researchers around the world to spread, share and discuss the problems and development in different directions in the field of artificial intelligence and robot research.

research field

Research on artificial intelligence technology

intelligent robot

Pattern recognition and intelligent system

Virtual reality technology and its application

System simulation technology and its application

Industrial process modeling and intelligent control

Intelligent computing and machine games

Artificial intelligence theory

Speech recognition and synthesis

machine translation

Image processing and computer vision

Computer perception

Computer neural network

Knowledge discovery and machine learning

Intelligent Building Technology and Its Application

Other disciplines of artificial intelligence

research method

At present, there is no unified principle or paradigm to guide the research of artificial intelligence. Researchers debated many issues. Several long-standing questions are: should artificial intelligence be simulated psychologically or neurologically? Or human biology has nothing to do with artificial intelligence research, just as bird biology has nothing to do with aviation engineering? Can intelligent behavior be described by simple principles, such as logic or optimization? Or do you have to solve a lot of completely unrelated problems?

Can intelligence be expressed by high-level symbols, such as words and thoughts? Or do you need to deal with "sub-symbols"? John JOHN HAUGELAND put forward the concept of GOFAI (excellent old-fashioned artificial intelligence) and suggested that artificial intelligence should be classified as synthetic intelligence. [29] This concept was later adopted by some non-GOFAI researchers.

Brain simulation

Main projects: cybernetics and computational neuroscience

From 1940s to 1950s, many researchers explored the relationship among neurology, information theory and cybernetics. It also created some initial intelligence built by electronic networks, such as turtles and John Hopkins Beast by W. GREY WALTER. These researchers often hold technical association meetings at Princeton University and ratio clubs in Britain. Until 1960, most people had given up this method, although these principles were put forward again in the 1980s.

symbol manipulation

Main entrance: GOFAI

When digital computers were successfully developed in 1950s, researchers began to explore whether human intelligence could be simplified to symbolic processing. The research mainly focuses on Carnegie Mellon University, Stanford University and Massachusetts Institute of Technology, each with its own independent research style. John howe Grande called these methods GOFAI (Excellent Old-fashioned Artificial Intelligence). [33] In the 1960s, the symbolic method made great achievements in simulating advanced thinking in small-scale proof procedures. Methods based on cybernetics or neural networks are secondary. [34] In the 1960s and 1970s, researchers were convinced that symbolic methods could eventually create machines with strong artificial intelligence, which was also their goal.

Cognitive simulation economists herbert simon and Allen Newell studied human problem-solving ability and tried to formalize it. At the same time, they laid the foundation for the basic principles of artificial intelligence, such as cognitive science, operational research and management science. Their research team uses the results of psychological experiments to develop programs that simulate human problem-solving methods. This method was inherited from Carnegie Mellon University and reached its peak in 1980s. Unlike allen newell and herbert simon, JOHN MCCARTHY believes that machines do not need to simulate human thoughts, but should try to find the essence of abstract reasoning and problem solving, regardless of whether people use the same algorithm or not. His lab at Stanford University is devoted to using formal logic to solve many problems, including knowledge representation, intelligent planning and machine learning. The University of Edinburgh is also committed to logical methods, which has promoted the development of PROLOG and logical programming science in other parts of Europe. Researchers at Stanford University, such as marvin minsky and Seymour Piper, have found that special solutions are needed to solve the problems of computer vision and natural language processing-they think there are no simple and universal principles (such as logic). Roger Shank described their "illogical" approach as "sloppy". Common sense knowledge bases (such as DOUG LENAT's CYC) are an example of "sloppy" AI, because they have to manually write a complex concept at one time. Based on knowledge, a large-capacity memory computer appeared around 1970, and researchers began to construct knowledge into application software in three ways. This "knowledge revolution" has promoted the development and planning of expert system, which is the first successful form of artificial intelligence software. The "knowledge revolution" has also made people realize that many simple artificial intelligence software may need a lot of knowledge.

Sub-symbol method

In 1980s, symbolic artificial intelligence stagnated. Many people thought that symbolic system could never imitate all human cognitive processes, especially perception, robotics, machine learning and pattern recognition. Many researchers began to pay attention to solving specific artificial intelligence problems with sub-symbol method.

Researchers in the fields of bottom-up, interface agent, embedded environment (robot), behaviorism and new AI robots, such as RODNEY BROOKS, deny symbolic artificial intelligence and focus on basic engineering problems such as robot movement and survival. Their work once again pays attention to the views of early cybernetic researchers and puts forward the application of control theory in artificial intelligence. This is consistent with the demonstration of representation perception in the field of cognitive science: higher intelligence needs individual representation (such as movement, perception and image). In 1980s, DAVID RUMELHART once again put forward neural network and connectionism, which, together with other subsymbol methods such as fuzzy control and evolutionary computation, belong to the research field of computational intelligence.

statistical method

In 1990s, the research of artificial intelligence developed complex mathematical tools to solve specific branch problems. These tools are real scientific methods, that is, the results of these methods are measurable and verifiable, and they are also the reasons for the success of artificial intelligence. * * * The mathematical language used also allows cooperation among existing disciplines (such as mathematics, economics or operational research). STUART J. RUSSELL and PETER NORVIG point out that these advances are no less than "revolution" and "the success of NEATS". Some people criticize these technologies for paying too much attention to specific problems without considering the long-term goal of strong artificial intelligence.

integration

Intelligent Agent Example An intelligent agent is a system that can perceive the environment and take actions to achieve its goals. The simplest intelligent agents are those programs that can solve specific problems. More complex agent include human beings and human organization (such as companies). These paradigms allow researchers to study a single problem and find useful and verifiable solutions without considering a single method. Agents that solve specific problems can use any feasible methods-some agents use symbolic methods and logical methods, others are sub-symbolic neural networks or other new methods. Paradigm also provides a common language for researchers to communicate with other fields-such as decision theory and economics (also using the concept of abstract subject). In 1990s, the paradigm of intelligent agent was widely accepted. Agent architecture and cognitive architecture researchers have designed some systems to deal with the interaction between intelligent agents in multi-agent systems. A system containing symbols and sub-symbols is called hybrid intelligent system, and the research on this system is artificial intelligence system integration. The hierarchical control system provides a bridge between the sub-symbol AI of the reaction level and the highest traditional symbol AI, and at the same time relaxes the time of planning and world modeling. Rodney brooks's inclusive architecture is an early hierarchical system plan.

Intelligent simulation

Simulation of machine vision, hearing, touch, feeling and thinking mode: fingerprint recognition, face recognition, retina recognition, iris recognition, palmprint recognition, expert system, intelligent search, theorem proving, logical reasoning, games, information induction and dialectical processing.

Subject category

Artificial intelligence is an interdisciplinary subject, which belongs to the three-way cross-discipline of natural science, social science and technical science.

Involving disciplines

Philosophy and cognitive science, mathematics, neurophysiology, psychology, computer science, information theory, cybernetics, uncertainty, bionics, social structure and Scientific Outlook on Development.

Research category

Language learning and processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combinatorial scheduling problems, perception problems, pattern recognition, logic programming, soft computing, imprecise and uncertain management, artificial life, neural networks, complex systems and genetic algorithms, the most critical issue is to shape and improve the independent and creative thinking ability of machines.

application area

Machine translation, intelligent control, expert system, robotics, language and image understanding, gene programming robot factory, automatic programming, aerospace application, huge information processing, storage and management, performing complex or large-scale tasks that combined organisms cannot perform, and so on.

It is worth mentioning that machine translation is an important branch of artificial intelligence and the first application field. However, as far as the existing machine translation is concerned, the translation quality of the machine translation system is far from the ultimate goal; The quality of machine translation is the key to the success of machine translation system. Professor Zhou Haizhong, a mathematician and linguist in China, once pointed out in the article "Fifty Years of Machine Translation": To improve the quality of machine translation, the first thing to be solved is the language itself rather than the programming problem; It is certainly impossible to improve the quality of machine translation by relying on several programs to make a machine translation system; In addition, it is impossible for machine translation to reach the level of "faithfulness and elegance" before human beings have figured out how the brain makes fuzzy recognition and logical judgment on language.

security issue

Artificial intelligence is still under study, but some scholars believe that it is very dangerous to let computers have IQ, and it may resist human beings. This hidden danger has also happened in many movies. The most important key is whether to allow the machine to have independent consciousness to produce and continue. If the machine has autonomous consciousness, it means that the machine has the same or similar creativity, self-protection consciousness, emotion and spontaneous behavior as human beings.

Realization method

There are two different ways to realize artificial intelligence on the computer. One is to use traditional programming technology to make the system present intelligent effect, regardless of whether the method used is the same as that used by human body or animal body. This method is called engineering method, and it has made achievements in some fields, such as character recognition and computer chess. The other is modeling method, which not only depends on the effect, but also requires the realization method to be the same as or similar to that used by human beings or biological organisms. Genetic algorithm and artificial neural network belong to the latter type. Genetic algorithm simulates the genetic-evolutionary mechanism of human or organism, while artificial neural network simulates the activity of nerve cells in human or animal brain. In order to obtain the same intelligent effect, these two methods can usually be used. Using the former method, you need to specify the program logic in detail manually, which is more convenient if the game is relatively simple. If the game is complex, the number of characters and the activity space increase, the corresponding logic will be very complex (exponential increase), and manual programming will be very cumbersome and error-prone. Once an error occurs, it is very troublesome to modify the original program, recompile and debug it, and finally provide users with a new version or patch. When adopting the latter method, the programmer should design an intelligent system (a module) for each role to control. This intelligent system (module) knows nothing at first, just like a newborn baby, but it can learn, gradually adapt to the environment and deal with all kinds of complicated situations. This kind of system often makes mistakes at first, but it can learn a lesson, and it may be corrected next time it runs, at least it won't make mistakes forever, and it doesn't need to release a new version or patch. Using this method to realize artificial intelligence requires programmers to have biological thinking methods, which is a bit difficult to get started. But once in the door, it can be widely used. Because this method does not need to specify the activity rules of the role in programming, it is usually more labor-saving when applied to complex problems.