Artificial Intelligence Core Technology

Computer vision, machine learning, natural language processing, robotics and speech recognition are the five core technologies of artificial intelligence, and they all become independent sub-industries.

1. Computer Vision: Computer vision technology uses a sequence of image processing operations and machine learning techniques to decompose image analysis tasks into manageable, small-block tasks.

2. Machine Learning: Machine learning is the automatic discovery of patterns from data. Once a pattern is discovered, predictions can be made. The more data processed, the more accurate the prediction will be.

3. Natural language processing: The processing of natural language texts refers to the ability of computers to handle text similar to humans. For example, the person, place, etc. mentioned in the document are automatically recognized, or the terms in the contract are extracted and made into a table.

4. Robotics: In recent years, as core technologies such as algorithms have been upgraded, robots have made important breakthroughs. Examples include drones, housekeeping robots, and medical robots.

5. Biometrics: Biometrics can be integrated with computers, optics, acoustics, biosensors, biostatistics, and personal identification using the inherent biometric characteristics of the human body such as fingerprints, faces, irises, veins, sounds, gait, etc. Applied to judicial identification.

With the development of science and technology, biometrics technology has become an important method of personal identification or authentication technology. Face recognition is an important branch of biometric recognition, and it is non-invasive and most intuitive and intuitive to the user. It is easy to accept, however, some existing machine learning algorithms mostly use shallow structures, while shallow structured networks can hardly represent complex functions. At the same time, multi-layer perceptual machines proposed in the past can represent complex functional relationships but do not have good learning algorithms. In recent years, deep learning technology has been widely recognized by the industry, and has made rapid progress in various related fields, especially the application of deep learning technology in the field of face recognition. At this year's Expo, various manufacturers have also launched human faces. Identification technology. With the constant change of market demand and different applications, face recognition technology also develops a variety of products to meet the needs of users.

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