What are the application fields of artificial intelligence?
Artificial intelligence had a wide range of applications, covering many aspects: 1. ** Transportation **: Realizing autonomous driving, vehicles achieve autonomous driving with the help of technologies such as laser radar, cameras, and infrared sensors; 2. ** Medical Health **: - ** Intelligent diagnosis **: Through deep learning and big data analysis, it can quickly analyze a large number of medical images, medical records, and physiological parameters, providing doctors with accurate and comprehensive diagnosis information; - ** Smart Medical **: Big data, 5G, cloud computing, artificial intelligence, and other technologies are deeply integrated with the medical industry, playing a role in auxiliary diagnosis, medical imaging, disease detection, and drug development; 3. ** Financial technology **: In terms of intelligent risk control, it uses deep learning and machine learning algorithms to analyze and model massive amounts of data to provide accurate and efficient risk assessment services for financial institutions; 4. ** Education **: To realize customized learning and provide students with customized learning resources and courses according to their learning situation and interests; 5. ** Agriculture **: Real-time monitoring and data analysis of crops with the help of drones, satellite remote sensing and other technical means; 6. ** Retailing **: It has a wide range of applications, including customer flow statistics, smart supply chain, unmanned convenience stores, unmanned warehouses/unmanned vehicles, etc. For example, Jingdong's unmanned warehouse used a large number of intelligent logistics robots. Through artificial intelligence, deep learning, image intelligent recognition, big data application and other technologies, industrial robots could make independent judgments and behaviors, complete various complex tasks, and realize automaton in commodity sorting, transportation, warehouse and other links. Through the Face Recognition function in the map technology, the user portrait of the customer flow could be established from the dimensions of gender, age, expression, new and old customers, and stay time to provide a data base for adjusting business strategies and improve the conversion rate. 7. ** Security field **: Intelligent security uses artificial intelligence systems to implement security control and analyze human body, behavior, vehicles, images, etc. 8. ** Industrial manufacturing **: - With the advancement of the industrial manufacturing 4.0 era, the application of artificial intelligence in the manufacturing field became increasingly widespread. - Lathe loading and unloading robot is an automated equipment used in the field of lathes. It can automatically complete the loading and unloading operations of the work piece during the process of the lathes. It can improve the degree of automaton, production efficiency, and processing accuracy of the lathes, and reduce manual operation errors and labor intensity. 9. ** Intelligent robots **: For example, driverless cars (also known as wheeled mobile robots) mainly rely on the intelligent driving controller in the car to achieve driverless driving; 10. ** Biomedicals **: - ** Fingerprint identification **: It can identify the operator or the person being operated based on the lines and details of the human fingerprint. It has been widely used in daily life. - ** Face Recognition **: By analyzing and comparing the visual features of the face, it can identify the face and give the position, size, and location of the main facial organs. It can further extract the identity features and compare it with the known face to identify the identity. It is important data in daily life and business operations. It can integrate personal big data and even rebuild the credit system rules. - ** Retinal recognition **: It uses low-density infrared rays to capture the unique features of the retina. The retina is an extremely fixed biological feature that is not worn, aged, or affected by diseases. The user does not need to directly come into contact with the device and is difficult to deceive. - ** Iris recognition **: It is the most convenient and accurate application of all bioweapon technologies. It is widely regarded as the most promising bioweapon technology in the 21st century. It has wide application prospects in various fields such as security, national defense, and e-commerce. - ** Palmprint recognition **: It is a relatively new biometric-based identification technology. Palmprints contain far more information than fingerprints. It can determine a person's identity by using the line features, point features, texture features, and geometric features of the palmprint. 11. ** Intelligent Information Searching Technology Field **: With the increase in the amount of information stored in the database system, it is of practical significance to solve the problem of intelligent searching. 12. ** Intelligent Control Field **: Able to drive intelligent machines without human intervention to achieve control goals; 13. Expert System Domain: It was an important branch of early AI. It could be seen as a computer intelligence program system with specialized knowledge and experience. It used knowledge representation and knowledge reasoning techniques in artificial intelligence to simulate complex problems that could usually be solved by domain experts. 14. ** Automatic Planning Domain **: Starting from a specific problem state, seek a series of actions and establish an operation sequence until the target state is obtained. It can be used to monitor the problem solving process and detect errors before causing greater harm. It has the advantages of simplified search, resolving target contradictions, and providing a basis for error compensation. 15. ** E-commerce domain **: It is used to create recommendation engines, which can be used to better interact with customers. 16. ** Domain of prediction and analysis **: - ** Regression **: Enter labeled data and generate values from continuous series after prediction model processing. The application includes the customer's stock value and net profit, revenue and growth forecast, price changes, credit default risk, and stock trading calculations; - ** Category **: Enter labeled data. After processing by the classification model, the input data will be divided into one or more categories. For example, the junk mail filter is a dual-classification application. The application also includes credit risk, loan approval, and customer churn. The recommendation system was a special information filtering system that was related to classification. It could make recommendations based on existing information to improve customer conversion rate, sales rate, satisfaction, and Retention rate. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
Eight application fields of artificial intelligence
The following are some of the main application areas of artificial intelligence: 1. Driverless cars rely on the intelligent driving controller in the car to achieve driverless state. This is an important application of artificial intelligence in transportation. 2. Intelligent medical field: Through the deep integration of big data, 5G, cloud computing, artificial intelligence and other technologies with medical care, it can be used to assist in diagnosis, medical imaging, disease detection, drug development, etc. 3. Intelligent security field: Using artificial intelligence systems to implement security control, it can analyze human bodies, behaviors, vehicles, images, etc. 4. Intelligent manufacturing: With the development of the industrial manufacturing 4.0 era, the application of artificial intelligence in the manufacturing field became more and more widespread. 5. Military field: For example, the US military applied artificial intelligence in the field of command platforms to improve combat effectiveness and reduce the maintenance cost of command platforms; in the field of network security, it was used for active defense and attack; in the field of target identification, it improved the accuracy of target identification in complex environments; in the field of intelligence processing, it optimized processes and extracted valuable information. 6. Machine vision related fields: It plays an important role in parts identification and positioning, product inspection, mobile robot navigation, remote sensing image analysis, surveillance and tracking, national defense systems, and other scenes that are difficult for human vision to perceive. 7. Biomedicals: Including fingerprint recognition, Face Recognition, retina recognition, iris recognition, palmprint recognition, etc., used for identification or recognition. 8. Intelligent Information Searching Technology Field: It helps to solve the problem of intelligent searching after the database system has increased the amount of information. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
What are the application fields of artificial intelligence?
Artificial intelligence had a wide range of applications, including the following: 1. ** Computer Vision (LV) Field **: - ** Object detection and tracking **: For example, it is used in autonomous vehicles, drones, and security cameras to detect and track objects such as vehicles and pedestrians in real time. - ** Image and video recognition **: The neural network model can accurately identify and classify images and videos. It can be applied to image search engines, content review and recommendation systems, etc. Search engines such as Google and Bing can provide image search results, and platforms such as Meta and Youtube can review content. - ** Face recognition **: High-precision face recognition and matching, used for security access control, surveillance, and personal marketing, such as security screening at the airport and government buildings, and analyzing customer behavior preferences by the retail industry. 2. ** Natural Language Processing (NPL) Domain **: - ** Text processing **: Including word separation, part-of-speech tagging, syntactical analysis, and sematic analysis to help the computer understand natural language text data. - ** Word expression **: Transform words into a computer readable format through neural network model and a large amount of corpus-based training, capture the meaning of the words, and lay the foundation for subsequent tasks. - ** Text classification and sentiment analysis **: Models such as Consecutive Neutral Network (CPR) or Cyclic Neutral Network (RHN) can be established to classify or analyze the sentiment tendency of texts. - ** Machine translation **: Using neural network models and Bilingual-Language Corpus training to achieve automatic translation of natural language texts. In addition, the Language Large Model (LLM) could also achieve human-computer dialogue, automatic summary generation, and information search. 3. ** Speech recognition (Audio) field **: Using deep learning technology to realize tasks such as analysis, recognition, and synthesis of audio signals. 4. ** Military (Take the US military as an example)**: - ** Command Platform Domain **: Various military services developed a large model based command and control platform for land, sea, air, and sky. It was used to read, understand, and summarize battlefield intelligence data, give suggestions to commanders, answer questions, assist in formulating battle plans, issue orders, and review battle plans and cases. - ** Cyber security **: On the one hand, it is equipped with an artificial intelligence active defense system to prevent unauthorized access; on the other hand, it develops an automated APT attack system to search for loopholes in combat opponents and attack them independently. At the same time, it can detect and prevent the operation of malicious software by analyzing the operating patterns of malicious software. - ** Target recognition **: With the development of big data, deep learning algorithms, and multi-model large models, improve the accuracy of target recognition in complex environments, combine GPS to enhance the ability to identify target locations, and predict and mark enemy attacks. For example, develop target recognition and tracking programs, and modify the Apache attack helicopter to achieve automatic classification of reconnaissance targets. - ** Intelligence processing field **: Large models are used to quickly process big data and extract valuable intelligence knowledge. For example, the US Army combines intelligence from different sources, intelligence departments analyze various forms of information to find potential threat targets, the US customs and border protection agency uses drones integrated with artificial intelligence to patrol the border, and the US Spatial Intelligence Agency speeds up the intelligence surveillance and reconnaissance department's automated processing. 5. ** Other Common Domains **: - ** Machine vision field **: It plays a role in parts identification and positioning, product inspection, mobile robot navigation, remote sensing image analysis, surveillance and tracking, national defense systems, and other scenarios. - ** Biomedicals **: For example, fingerprint recognition is widely used for identification; Face Recognition uses the visual features of the face to identify the identity, which is a hot research field; retina recognition is used to capture the unique features of the retina for identification, and the retina features are fixed and difficult to deceive; iris recognition is considered to be the most convenient and accurate biomedicals authentication technology, which has application prospects in security and national defense. - ** Intelligent Information Search Technology Field **: Solve the problem of intelligent search after the database information volume increases. - ** Intelligent Control Field **: Able to drive an intelligent machine to achieve a control target without human intervention. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
Four Fields of Artificial Intelligence
The four major fields of artificial intelligence were machine learning, machine vision, natural language processing, and robots. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
Five Fields of Artificial Intelligence
Artificial intelligence included the following five fields: 1. ** Core Technology Area **: - Machine learning: It was one of the most active and important fields in the field of artificial intelligence. By establishing mathematical models and using a large amount of data to train machines, they could learn and predict based on the input and output relationship of data. It was the core foundation of large models such as GMT. - ** Natural Language Processing **: Giving computers the ability to understand and generate human natural language, enabling effective communication between humans and computers using natural language. For example, the Hunyuan model implements human-computer interaction by transforming natural language. Its applications include text generation, dialogue system, machine translation, and sematic analysis. - ** Computer vision **: The application of machine learning, pattern recognition, image processing, and other technologies in the field of computer vision, allowing machines to " understand " pictures, videos, and information in the real environment, and to identify, analyze, and infer. For example, the Style mobile phone software is an application for image generation. - ** Speech recognition **: It is an important branch of machine learning. It uses computer technology and algorithms to train a large amount of data to enable computers to recognize and understand human speech and convert it into text. For example, the voice recognition services provided by the voice recognition platform of Tencent Cloud Platform. 2. ** Smart Terminal Domain **: - ** Artificial Intelligence Service Platform **: Build a platform to make AI technology available to enterprises or individuals in various industries. For example, the AI Open Platform of the company gathers multiple AI technology capabilities and opens the interface. - ** Smart home terminal **: Based on the automaton and intelligence of home products, the smart home experience can be realized through the network according to the needs of personification. For example, Xiaomi Mijia is a closed-loop experience composed of a variety of smart hardware products. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
The development and application of artificial intelligence
The ultimate goal of artificial intelligence was to explore the basic mechanism of the formation of intelligence and study the use of automata to simulate human thinking processes. The short-term goal was to study how to make computers do the work that usually required human intelligence. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
The application of artificial intelligence in agriculture
Artificial intelligence had many applications in agriculture: 1. ** In terms of improving production efficiency ** - ** Intelligent Farm Machinery Operation **: The intelligent farm machinery can automatically adjust the operation mode according to the soil moisture, crop growth status, and other parameters. For example, in the fields of plowing, sowing, harvesting, etc., it could reduce labor costs and time consumption, greatly improving the level of agricultural production's automaton and intelligence. - Precise irrigation system: It can precisely control the amount of irrigation water, avoid water waste, and ensure that crops grow under optimal conditions, thereby improving overall production efficiency. 2. ** To solve the problem of agricultural standards ** - By collecting and analyzing a large amount of agricultural data, a standardized production model was established. These models could guide farmers to carry out planting, fertilizing, irrigation and other operations according to unified standards, ensure the consistent quality of agricultural products, and promote the development of agricultural production in the direction of standards. 3. ** In terms of solving the problem of agricultural product quality ** - ** Intelligent pest and disease monitoring system **: It can monitor the growing environment of crops in real time and detect pests and diseases in time so that they can be dealt with quickly to avoid the impact of pests and diseases on the quality of agricultural products. - ** Agricultural product quality inspection equipment **: It can detect the quality of agricultural products in real time to ensure the output of high-quality agricultural products. Moreover, artificial intelligence could accurately adjust planting strategies according to market demand and consumer preferences to produce agricultural products that better met market demand. 4. ** Helping farmers increase their income ** - To improve agricultural production efficiency, improve the quality of agricultural products, and directly increase farmers 'income. On the one hand, the improvement of production efficiency reduced production costs and increased the output per unit area. On the other hand, high-quality agricultural products were more competitive in the market and could obtain higher sales prices. At the same time, it also gave birth to new agricultural services and business models, providing more income-generating channels for farmers. 5. ** In crop and livestock management ** - ** Increase crop yield **: For example, through deep learning algorithms, analyze soil, weather, aerial/satellite images and other data related to agriculture to help farmers make decisions on planting time, irrigation, fertilizer, etc., increase the productivity of land, equipment, and people, and reduce yield losses. - ** Early Disease Detection **: It helps in early detection of crop or livestock diseases. For example, in animal husbandry, it could prevent diseases such as lameness in cows in advance and reduce the losses caused by animal diseases. - ** Reduce labor costs **: Reduce labor costs related to sorting during the post-harvest sorting process. For example, some robots could be used to sort agricultural products and replace human labor. - ** Increase product quality **: Increase the quality of agricultural products and protein in the market. 6. ** Robot applications ** - **Root AI harvesting robot **: It can operate in a complex growing environment, detect whether fruits and vegetables are ripe in real time, and pick them. It used visual algorithms to distinguish ripe fruits and accurately locate them. There was also a patent holder that could gently pick soft fruits. - **FarmWise robot **: Used to solve the problem of weeds in farmland. Through computer sensors and vision technology to perceive plant information, combined with learning algorithms, it could distinguish between crops and weeds. It could also adjust the precision of the blade according to the field conditions, suitable for a variety of crops. - ** TerraSenta Crop-monitoring Robot **: A combination of multiple sensors, wheels, and shock-absorbing chassis suitable for a variety of terrains. It could navigate through plants and analyze data and traits based on machine vision, such as recording corn plant height, ear height, stem width, and leaf status, to provide suggestions for planting. - [Vegebot harvesting robot: suitable for harvesting easily damaged crops such as cabbage and lettuce.] There was a vision and cutting system. The vision system recognized the ripeness and disease of the vegetables. The camera of the cutting system ensured smooth cutting. The gripping force of the robotic arm could be adjusted according to different crops. - **Blue River Fully Automatic Agriculture Equipment **: It has computer vision technology that recognizes weeds and selectively kills unwanted plants. It is more effective than traditional weeding methods. It improves production efficiency while reducing the use of chemicals, optimises conventional processes, and reduces the impact on the environment. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
The application of artificial intelligence on campus
The application of artificial intelligence on campus was reflected in many aspects: 1. ** Education Management **: Realization of intelligence in student information management, teaching resource allocation, campus security monitoring, and other work. 2. ** Identification of high-risk students **: High schools can introduce artificial intelligence data analysis driven systems to comprehensively consider students 'academic achievements, attendance trajectories, and behavior data, accurately predict the difficulties that students may face, and automatically generate targeted intervention plans. This changes the traditional process of manually identifying problematic students by teachers and instructors. 3. ** Personalized education **: The primary school district can use artificial intelligence tools to customize learning plans and educational resources for students according to their individual learning trajectories and styles (the "Student Portrait" system). This will reduce the burden of teachers preparing lessons and differentiated teaching, making education more accurate and efficient. 4. ** Administrative management **: Secondary schools can introduce artificial intelligence systems to take over administrative tasks such as schedules, budget management, and home-school communication. This will free up the time and energy of the school leadership, allowing them to focus more on core matters such as teaching guidance, strategic planning, and community interaction. 5. ** Teacher evaluation **: You can also carry out intelligent transformation (the reference materials did not describe the specific method in detail. It can be speculated that it is to use artificial intelligence to achieve a more objective and comprehensive evaluation of teachers 'teaching effects, etc.). 6. ** In terms of improving teachers 'quality **: Teachers can learn AI + teaching philosophy (Including the development of artificial intelligence models, AIGC application cases, and the current situation and trends of AI enabling education and teaching), AI + integration practice (Selection and operation of mainstream AI models, integration of artificial intelligence technology into curriculum design, application in student assessment, teaching management, and curriculum design, etc.), curriculum design driven by AI (Exploring the construction method of AI general course, teaching design case analysis, and AI teaching assistant, student aid, and management practice, etc.), artificial intelligence general course setting and teaching resource construction (paying attention to the construction of artificial intelligence teaching resources, AI evaluation of teaching quality, and the development of AI enabling teachers, etc.) to improve their own AI literacy to better adapt to the educational needs of the AI era. 7. ** Sports facilities **: The smart campus track on campus can be used as a new sports tool combined with artificial intelligence technology. It can monitor the user's running data (such as speed, heart rate, step frequency, etc.) in real time, provide intelligent navigation functions, recommend customized training plans, and connect to social platforms to play music. It can provide a more intelligent and convenient running experience for running enthusiasts. 8. ** In terms of curriculum design **: Many universities across the country offered general courses on artificial intelligence for undergraduate students. For example, Beijing announced that the general courses on artificial intelligence for municipal public undergraduate universities would be fully covered. Tianjin opened the first batch of general courses on artificial intelligence for all universities in the city. This was also the application of artificial intelligence in campus education content. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
The application of artificial intelligence in education
Artificial intelligence had the following applications in the education industry: 1. ** Personalized learning **: With the data analysis ability of artificial intelligence, each student's learning progress, knowledge mastery status, learning habits, and other factors are analyzed in depth, and then a customized learning plan is customized for them. For example, the intelligent learning system could accurately push the learning content and practice questions that matched the students 'current level according to their performance and learning time, improving their learning efficiency. 2. ** Smart Tutoring Answer **: develop a smart tutoring tool that can answer students 'questions in real time. Using natural language processing technology to understand students 'intentions, giving precise and detailed answers and guidance, providing timely assistance in the process of independent learning, and cultivating their independent learning ability. 3. ** Virtual Reality and Augmented Reality Teaching **: Using Virtual Reality (VR) and Augmented Reality (AR) technology to build an immersive teaching environment. For example, in the teaching of history, geography, and other subjects, students could experience the scenes or geographical environment of historical events through VR equipment to increase the fun and intuition of learning, and improve students 'enthusiasm and participation in learning. 4. ** Education Resource recommendations **: Through artificial intelligence algorithms, according to students 'learning needs, interests, hobbies, and learning stages, recommend high-quality education resources, such as books, videos, online courses, etc., so that students can obtain suitable learning materials and broaden their learning horizons. 5. ** Intelligent homework correction feedback **: Using artificial intelligence technology to realize automatic homework correction, not only can it quickly and accurately determine whether the answer is right or wrong, but it can also give detailed feedback and suggestions based on the student's answer, helping the student to find problems in time and improve their learning methods. 6. ** Teacher's teaching assistance **: provides teaching assistance tools for teachers, such as intelligent lesson preparation system, classroom teaching analysis system, etc. The intelligent lesson preparation system could generate teaching plans and teaching resource suggestions according to the teaching outline and the characteristics of students. The classroom teaching analysis system could provide teachers with teaching effect evaluation and improvement suggestions by analyzing classroom teaching data to improve teaching quality. 7. ** Cultivate innovative practical ability **: Use artificial intelligence-related practical projects and competitions to stimulate students 'innovative thinking and practical ability. For example, organizing students to participate in artificial intelligence programming competitions, robot design competitions, etc., so that students could learn and apply artificial intelligence knowledge in practice, cultivate the ability to solve practical problems and team spirit. 8. ** Education management optimization **: In education management, artificial intelligence technology is applied to realize intelligent operations such as student information management, teaching resource allocation, and campus security monitoring. Through data analysis and prediction, we can rationally arrange teaching resources and improve the efficiency of school management and the scientific nature of decision-making. 9. ** Promotion of educational fairness **: Use artificial intelligence technology to spread high-quality educational resources to areas with scarce educational resources, providing students in remote areas with the same quality of educational opportunities as students in developed areas. For example, through online education platforms and intelligent learning systems, more students could enjoy high-quality courses and teaching services. 10. ** Education ethics and safety education **: Incorporate artificial intelligence ethics and safety education into education, let students recognize the potential impact and risks of artificial intelligence technology, cultivate their correct values and ethics, guide students to abide by laws, regulations, and ethics when using artificial intelligence technology, and ensure the reasonable application of technology. In addition, in the first application guide for artificial intelligence in the field of education issued Beijing City, six major applications of AI in education were identified, including student assistance, teaching assistant, evaluation assistant, education assistant, research assistant, and management assistant. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
Development and application of artificial intelligence
Since the concept of artificial intelligence (AI) was first proposed in the middle of the 20th century, its development process had experienced many fluctuations and changes. Its early development focused on basic research and theoretical exploration. The term "artificial intelligence" was officially used at the Dartmouth conference in 1956, marking that AI became an independent research field. At that time, it mainly focused on problem solving and symbol processing. The core was to let machines simulate human decision-making processes. In the 21st century, with the significant changes in computing power and data volume, machine learning became a key driving factor for the development of AI. Machine learning used algorithms and statistical models to identify data patterns and make decisions without explicit programming. As a branch, deep learning simulated the neural network structure of the human brain and made breakthroughs in the fields of image recognition, speech recognition, and natural language processing. AI was widely used. In the medical field, it could help doctors diagnose diseases and improve the accuracy and efficiency of diagnosis. In the financial industry, it was used for risk management, fraud detection, and algorithm trading. In the auto industry, autonomous driving technology was changing the way people traveled. It also showed great potential in education, retail, manufacturing, and other fields. The main types of AI at the current stage include machine learning, deep learning, natural language processing, computer vision, and so on. In life, AI was combined with finance, manufacturing, education, transportation, health, retail, and service industries to bring more convenience to people's lives. For example, smart medicine could create an information platform through relevant technologies to achieve multi-party interaction; smart finance could predict market trends, calculate, classify, design, and so on. With the rapid development of AI technology, it also faced ethical and legal challenges. Data privacy, algorithm bias, and the impact of automaton on employment were currently issues of concern. At the same time, the future development of AI was full of infinite possibilities. From dedicated intelligence to general intelligence, the application of intelligent entities was seen as one of the future development directions. As technology developed and applications deepened, it was expected to play a revolutionary role in more fields. International cooperation and policy formulation would also play an important role in its healthy development. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
Artificial intelligence technology and application
Artificial intelligence techniques included problem solving, logical reasoning, and theorem proving. In terms of problem solving, he developed techniques such as the Deep Blue computer's victory over the chess master, including search and problem reduction techniques such as looking forward a few steps and breaking down difficult problems into sub-problems. This technique was also applied to chess programs and some programs that could deal with mathematical formulas. Some programs could also improve their performance through experience. Logical reasoning was a persistent sub-field of artificial intelligence research. The focus was on focusing on relevant facts in large database, paying attention to credible proof and correcting it in time. This was crucial in intelligent tasks such as proving or disproving mathematical theorem. From the perspective of application, the field of application was constantly expanding. In terms of engineering applications, there were books that introduced its basic principles, control methods, and applications. The interaction model currently in use, such as Chat GPM, was only a basic application. The ultimate direction was the general artificial intelligence, AGI, which could learn and perform various tasks independently like humans. In actual work life, it could be used as psychological consultation, covering marriage guidance, emotional regulation, and other content. In terms of occupation, the talents trained by the relevant majors could be engaged in artificial intelligence trainers, artificial intelligence engineering technicians, etc. The employment positions included artificial intelligence data services, algorithm model training and testing, etc. In terms of enterprise applications, Tesla said that it was also an artificial intelligence company, and its automatic driving direction was visual recognition + machine learning; SuperMap software launched a geographical space AI technology base, including AI three-dimensional data processing and analysis, AI remote sensing image processing and other functional modules. Furthermore, in various industries, if one could skillfully use AI tools, one could improve work efficiency. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!