The Concept and Development of Artificial Intelligence (AI: Concept, Development, and Application)

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Artificial Intelligence (AI) is a field aimed at making computers similar to human intelligence. In recent years, the development and application of AI in various fields have made significant achievements, which has attracted widespread attention. This article will briefly introduce the concept, development process, and application fields of AI.

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1. The concept of artificial intelligence Artificial intelligence is usually defined as making computers have the ability similar to human intelligence, such as learning, reasoning, problem solving, knowledge expression, planning, navigation, natural language processing, pattern recognition, perception, etc. 1 The research purpose of artificial intelligence is to expand human intelligence by exploring the essence of intelligence - promoting intelligent subjects to listen (speech recognition, machine translation, etc.) Can see (image recognition, character recognition, etc.), can speak (voice synthesis, human-computer dialogue, etc.), can think (human-computer game, expert system, etc.), can learn (knowledge representation, machine learning, etc.), and can act (robots, autonomous vehicle, etc.) 2.

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Artificial intelligence can be divided into different types based on its performance level and goals, such as weak AI, strong AI, general AI, and super AI. 1 Weak AI, also known as narrow AI or artificial narrow intelligence (ANI), refers to trained AI focused on performing specific tasks.

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Weak AI drives most AI we use now, such as voice assistant, image recognition system, autonomous vehicle and other strong AI, also known as comprehensive AI or Artificial General Intelligence (AGI), which refers to AI with intelligence level equal to or higher than that of human beings.

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Strong artificial intelligence can understand and handle any complex and abstract problem, and has self-awareness and emotions. Currently, strong artificial intelligence is still a theoretical concept and has not yet implemented universal artificial intelligence, also known as broad artificial intelligence or Artificial Broad Intelligence (ABI), which refers to AI between weak and strong artificial intelligence.

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General artificial intelligence can demonstrate high-level intelligence in multiple fields and tasks, and has a certain degree of adaptive and transfer learning ability. General artificial intelligence is an important goal of current AI research. Super artificial intelligence, also known as super intelligence or super level artificial intelligence (ASI), refers to AI that far surpasses any human or other known form of intelligence level.

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Super artificial intelligence can master all fields and tasks, and has unlimited creativity and imagination. Super artificial intelligence is an extreme assumption that may have a profound impact on human society

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2. The development of artificial intelligence The development of artificial intelligence can be traced back to the 1950s. It has gone through many stages, such as symbolism, machine learning, knowledge engineering, expert systems, intelligent agent and deep learning. 1 Symbolism: This is the early stage of artificial intelligence. It mainly uses logical reasoning and symbolic operations to simulate human thinking processes.

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The representative works of semiotics include machine theorem proving, checkers programs, ELIZA dialogue systems, etc. The advantage of semiotics is that it can handle abstract and complex problems, but its disadvantage is that it requires a large amount of manual coding and knowledge representation, and it is difficult to handle uncertainty and fuzziness in machine learning. This is the mid-term stage of the development of artificial intelligence, Mainly using mathematical and statistical methods to enable computers to automatically learn laws and knowledge from data.

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The representative works of machine learning include perceptron, K-nearest neighbor algorithm, logical regression, etc. The advantage of machine learning is that it can handle a large amount of data and noise, but the disadvantage is that it requires a large number of computing resources and sample data, and it is difficult to explain and verify knowledge engineering: this is the late stage of the development of artificial intelligence, and the method of expert system and knowledge base is mainly used to simulate the knowledge and experience of human experts.

The representative works of knowledge engineering include knowledge engineering such as DENDRAL chemical analysis system and MYCIN medical diagnosis system. The advantage of knowledge engineering is that it can handle problems in specific fields, but the disadvantage is that it requires a large number of experts to participate in and maintain, and it is difficult to adapt to changes and expand intelligent agent: this is a new stage of the development of artificial intelligence, The method of multi-agent system interacting with environment is mainly used to simulate human behavior and decision-making in complex environment.

The representative works of intelligent agent include IBM Dark Blue Supercomputer, Google AlphaGo program and other intelligent agent. The advantage is that they can handle dynamic and uncertain environments, but the disadvantage is that they need a lot of exploration and trial and error, and it is difficult to coordinate and cooperate with deep learning: this is the current stage of the development of artificial intelligence, Mainly using deep neural networks and big data analysis methods to enable computers to automatically extract features and knowledge from massive data.

The representative works of deep learning include AlexNet image classification network, BERT natural language understanding network, etc. The advantage of deep learning is that it can handle high-dimensional and nonlinear problems, but the disadvantage is that it requires a large amount of computing resources and annotated data, and is difficult to interpret and debug