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The Top 7 AI technologies

Today, I will introduce to you a few key technologies that can only be developed manually. From speech recognition to smart home, from man-machine war to unmanned driving, the "evolution" of artificial intelligence has brought surprises again and again to some details of life in our society, and the prospects for more intelligent products rely on AI technologies  What will it be like? Let's take a look at the definition of the important key technologies of artificial intelligence in the white paper on the standardization of artificial intelligence.
AI technologies are  is related to whether artificial intelligence products can be successfully applied to our life scenarios. In the field of artificial intelligence, it generally includes seven important key technologies: machine learning, knowledge graph, natural language processing, human-computer interaction, computer vision, biometric recognition, and AR/VR.

1.AI technologies: Machine learning

Machine Learning is among AI technologies , it is an interdisciplinary subject involving statistics, system identification, approximation theory, neural networks, optimization theory, computer science, brain science and many other fields. New knowledge or skills, reorganizing the existing knowledge structure to continuously improve its own performance, is the core of AI technologies. Data-based machine learning is one of the important methods in modern intelligent technology. It starts from observational data (samples) to find laws, and uses these laws to predict future data or unobservable data. 

According to the learning mode, machine learning is classified into supervised learning, unsupervised learning and intensive learning.

According to the learning method, machine learning can be divided into traditional machine learning and deep learning.

2.AI technologies:  Knowledge Graph

Knowledge graph is among AI technologies, it  is essentially a structured semantic knowledge base, which is a graph data structure composed of nodes and edges. It describes concepts and their mutual relationships in the physical world in symbolic form. Its fundamental unit is "entity-relationship- entity" triples, as well as entities and their associated attribute-value pairs. Different entities are connected with each other through relationships to form a networked knowledge structure. In the knowledge graph, each node represents an "entity" in the real world, and each edge is a "relationship" between entities. In layman's terms, a knowledge graph is a relational network obtained by connecting all different types of information, providing the ability to analyze problems from the perspective of "relationship".

The AI technologies Knowledge graphs can be used in public security fields such as anti-fraud, inconsistency verification, and group fraud. Data mining methods such as exception analysis, static analysis, and dynamic analysis are required. In particular, knowledge graph has great advantages in search engine, visual display and precision marketing, and has become a popular tool in the industry. However, the development of knowledge graph still has great challenges, such as the problem of data noise, that is, the data itself has errors or the data is redundant. With the deepening of knowledge graph applications, there are still a series of important key technologies that need to be broken through.

3.AI technologies:  Natural Language Processing

The Natural Language Processing is among AI technologies.
The study of various theories and methods that can achieve effective communication between humans and computers using natural language has affected many fields, mainly including machine translation, machine Browse understanding and question and answer systems and more.

machine translation

Machine translation technology refers to the use of computer technology to achieve the translation process from one natural language to another natural language. With the development of the contextual representation of high and low text and the ability of knowledge logical reasoning, the knowledge graph of natural language continues to expand, and machine translation will make greater progress in the fields of multi-round dialogue translation and text translation.

semantic understanding

Semantic understanding technology refers to the process of using computer technology to understand the text and answer questions related to the text. Semantic understanding focuses more on the understanding of high and low text and the control of the accuracy of the answer. With the release of the MCTest dataset, more attention has been paid to semantic understanding, and rapid development has been achieved, and related datasets and corresponding neural network models emerge in an endless stream. 

Question answering system

Question answering systems are divided into open domain dialogue systems and domain specific question answering systems. Question answering system technology refers to the technology that allows computers to communicate with people in natural language like humans. People can submit questions expressed in natural language to the question answering system, and the system returns highly relevant answers. Although many application products of question answering system have appeared, most of them are applied in the fields of information service system and smartphone assistant in reality. There are still problems and challenges in the robustness of question answering system.

Natural language processing faces four major challenges:

First, there are uncertainties at different levels such as lexical, syntactic, semantic, pragmatic and phonetic;

The second is the unpredictability of unknown linguistic phenomena caused by new vocabulary, terminology, semantics and grammar;

Third, insufficient data resources make it difficult to cover complex language phenomena;

Fourth, it is difficult to describe the ambiguity and complicated relationship of semantic knowledge with a simple mathematical model, and semantic calculation requires nonlinear calculation with huge parameters.

4.AI technologies:  Human-computer interaction

Human-computer interaction is among AI technologies,  mainly studies the exchange of information between people and computers, mainly including two parts of information exchange between people and computers and between computers and people. Human-computer interaction is a comprehensive subject closely related to cognitive psychology, ergonomics, multimedia technology, and virtual reality technology. The traditional information exchange between people and computers mainly relies on interactive devices, mainly including keyboards, mice, joysticks, data clothing, eye trackers, position trackers, data gloves, pressure pens and other input devices, as well as printers, drawing Instruments, monitors, helmet-mounted displays, speakers and other output devices. In addition to the traditional basic interaction and graphic interaction, human-computer interaction technology also includes technologies such as voice interaction, emotional interaction, somatosensory interaction, and brain-computer interaction.

5.AI technologies:  Computer Vision

Computer vision is among AI technologies, it is  the science of using computers to imitate the human visual system, giving computers the ability to extract, process, understand and analyze images and image sequences similar to humans. Autonomous driving, robotics, intelligent medical and other fields all need to extract and process information from visual signals through computer vision technology. Recently, with the development of deep learning, preprocessing, feature extraction and algorithm processing are gradually integrated to form an end-to-end artificial intelligence algorithm technology. According to the problems to be overcome, computer vision can be divided into five categories: computational imaging, image understanding, three-dimensional vision, dynamic vision, and video encoding and decoding.

At present, computer vision technology is developing rapidly and has a preliminary industrial scale. Prospects The development of computer vision technology mainly faces the following challenges:

One is how to better combine with other technologies in different application fields. Computer vision has gradually matured and can surpass human beings, but in some problems, it cannot reach a very high level. high precision;

The second is how to reduce the development time and manpower cost of computer vision algorithms. At present, computer vision algorithms require a large amount of data and manual annotation, and require a long research and development cycle to achieve the accuracy and time-consuming required by the application field;

The third is how to speed up the design and development of new algorithms. With the emergence of new imaging hardware and artificial intelligence chips, the design and development of computer vision algorithms for different chips and data acquisition equipment is also one of the challenges.

6.AI technologies:  Biometric identification

Biometric identification is among AI technologies, it is a technology refers to a technology that identifies and authenticates an individual's identity through an individual's physiological or behavioral characteristics. From the perspective of the application process, biometric identification is usually divided into two stages: registration and identification. In the registration stage, the biological representation information of the human body is collected by sensors, such as the use of image sensors to collect optical information such as fingerprints and faces, and the use of microphones to collect acoustic information such as speech, and data preprocessing and feature extraction techniques are used to process the collected data. , get the corresponding features for storage.

The identification process adopts the same information collection method as the registration process to carry out information collection, data preprocessing and feature extraction to recognize others, and then compare and analyze the extracted features with the stored features to complete the identification. From the perspective of application tasks, biometric identification is generally divided into two tasks: identification and confirmation. Identification refers to the process of determining the identity of the person to be recognized from the repository, which is a one-to-many problem; The process of comparing the information of a specific individual in the library to determine the identity is a one-to-one problem.

The AI technologies Biometric identification technology involves a wide range of biometric features, including fingerprints, palm prints, faces, irises, finger veins, voiceprints, gait and other biometric features. The identification process involves image processing, computer vision, speech recognition, machine Advanced study and many other technologies. At present, biometric identification, as an important intelligent identity authentication technology, has been widely used in finance, public security, education, transportation and other fields.

7.AI technologies: VR/AR

Virtual reality (VR)/augmented reality (AR) is among AI technologies, it  is a new type of audiovisual technology with computer as the core. Combined with relevant science and technology, a digital environment that is highly similar to the real environment in terms of vision, hearing and touch is generated within a certain range. The user interacts with objects in the digital environment with the help of necessary equipment, influences each other, and obtains a feeling and experience similar to the real environment. This is achieved through display devices, tracking and positioning devices, tactile interaction devices, data acquisition devices, and special chips.

From the perspective of technical characteristics, according to different processing stages, virtual reality/augmented reality can be divided into five aspects: acquisition and modeling technology, analysis and utilization technology, replacement and distribution technology, display and interaction technology, and technical specification and evaluation system. The acquisition and modeling technology study how to digitize and model the physical world or human creativity. The difficulty is the digitization and modeling technology of the three-dimensional physical world; the analysis and utilization technology focuses on the analysis, understanding, search and knowledge of digital content. The difficulty lies in the semantic representation and analysis of the content; the replacement and distribution technology mainly emphasizes the large-scale digital content circulation, conversion, integration and personalized service for different end users in various network environments, and its core is open Content replacement and copyright management technology; display and replacement technology focuses on various display technologies and interaction methods for digital content that are in line with human habits, in order to improve people's cognitive ability to complex information, the difficulty lies in building a natural and harmonious human-computer interaction environment ; Standardization and evaluation system focuses on the standardization of virtual reality/augmented reality basic resources, content cataloging, source coding, etc. and the corresponding evaluation technology.

At present, the challenges faced by virtual reality/augmented reality mainly include four aspects: intelligent acquisition, ubiquitous equipment, free interaction and perception fusion. There are a series of scientific and technical problems in hardware platforms and devices, core chips and devices, software platforms and tools, and related norms and specifications. Generally speaking, virtual reality/augmented reality presents the development trend of intelligent virtual reality system, seamless integration of virtual and real environment objects, all-round and warm natural interaction.