What does machine intelligence include?

The connotation of machine intelligence includes: 1. The basis of brain cognition, which is a kind of judgment and generative thinking of machine learning; 2. Machine perception and pattern recognition, which recognizes the graphics encountered and so on. make judgments; 3. Natural language processing and understanding, allowing robots to directly understand natural language, is the basis for realizing true human-machine communication; 4. Knowledge engineering is the study of how to express, acquire and acquire knowledge in large-scale data Techniques of reasoning.

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What does the connotation of machine intelligence include?

The connotation of machine intelligence includes the basis of brain cognition, machine perception and pattern recognition, natural language processing and understanding, and knowledge engineering.

• Basics of brain cognition: Simply put, it is a kind of discriminative and generative thinking of machine learning. Understand the characteristics of an object to identify the object; then generate an object based on these characteristics. For example, if there is a flower, the machine will determine its shape, find the corresponding concept, and then use these elements to regenerate a flower.

• Machine perception and pattern recognition: An intelligent machine will not wait for humans to "feed" it data, but will perceive the world by itself and respond to the things it encounters. Recognize graphics, etc. to make judgments.

• Natural language processing and understanding: "Natural language" is the language we actually use in real life, as opposed to the language used to operate computer systems, etc. Purposely developed programming language. Allowing robots to directly understand natural language is the basis for realizing true human-machine communication.

• Knowledge engineering: is a technology that studies how to express, acquire and reason knowledge in large-scale data.

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Machine intelligence research must have a solid mathematical foundation, because in the future, AI theoretical research must be used when studying machine intelligence application. Therefore, analysis, algebra, physical statistics, mathematical model experiments, etc. in applied mathematics, and applied mathematics in machine intelligence can be said to be the foundation of the foundation.

Machine intelligence is an extension of the new generation of automation, used to assist humans, change the nature of work, and make work more efficient. The basis of machine intelligence is computing, and the core is a machine that can learn, which brings humans into an intelligent society. Machine intelligence imitates a variety of human abilities: "Perception" such as AI's visual function and image recognition ability; "Action" enables machines to act like humans, such as handling object handling tasks in a variety of different situations; " "Human IQ" is based on the research of network models and algorithms, using artificial neural networks to form practical application systems. When a machine has human IQ and can think and handle problems like a human, this is also a high degree of artificial intelligence; "Creativity "Creativity is actually the ability to learn. If a machine wants to have the ability to learn and create like a human, it also needs to learn. We call it "deep learning." The motivation is to build a neural network that can simulate the human brain for analytical learning. It imitates the mechanism of the human brain to interpret data.

Artificial Intelligence is a branch of computer science that attempts to understand the nature of intelligence and produce a new intelligent machine that can respond in a manner similar to human intelligence. Research in the field includes robotics, language recognition, image recognition, natural language processing and expert systems, etc. Since the birth of artificial intelligence, the theory and technology have become increasingly mature, and the application fields have also continued to expand. It can be imagined that the technological products brought by artificial intelligence in the future will be the "containers" of human wisdom. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can think like humans and may even exceed human intelligence.

Artificial intelligence technology is also called machine intelligence. Generally speaking, artificial intelligence refers to human intelligence technology realized through ordinary computer programs. Artificial intelligence technology includes machine learning, machine vision, robotics, natural language processing and automation.

Automation is the process of automatically generating system or process functionality. Robotic process automation, for example, can be programmed to perform high-volume, repeatable tasks typically performed by humans. RPA differs from IT automation in that it can adapt to changing circumstances.

Machine learning is the science of making computers act without programming. Deep learning is a subset of machine learning and in very simple terms it can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms: supervised learning, in which data sets are labeled so that patterns can be detected and used to label new data sets; unsupervised learning, in which data sets are unlabeled and ordered based on similarities or differences; and reinforcement learning, in which The dataset is not labeled, but after performing an action or several actions, the AI ​​system is given feedback.

Machine vision is the science of making computers see. Machine vision uses cameras, analog-to-digital conversion, and digital signal processing to capture and analyze visual information. It is often compared to human vision, but machine vision is not bound by biology and can be programmed to see through walls. It is used in a variety of applications from signature recognition to medical image analysis. Computer vision, which focuses on machine-based image processing, is often confused with machine vision.

Natural language processing (NLP) is the processing of human rather than computer language through computer programs. One of the older and most famous examples of NLP is spam detection, which looks at the subject line and text of an email and determines whether it is spam. Current NLP methods are based on machine learning. NLP tasks include text translation, sentiment analysis, and speech recognition.

Pattern recognition is a branch of machine learning that focuses on identifying patterns in data. Today the word is obsolete.

Robotics is an engineering field focused on the design and manufacture of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used on assembly lines in automobile production or by NASA to move large objects in space. Recently, researchers are using machine learning to build robots that can interact in social environments.

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