10 applications of artificial intelligence


liu, tempo Date: 2021-07-16 11:00:21 From:ozmca.com
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Artificial intelligence has gradually entered our lives and is applied in various fields. It not only brings huge economic benefits to many industries, but also brings many changes and conveniences to our lives. Below, we will separately introduce some of the main application scenarios of artificial intelligence.

 

1. Driverless cars

 

An unmanned car is a type of smart car, also called a wheeled mobile robot, which mainly relies on an intelligent driving controller based on a computer system in the car to realize unmanned driving. The technology involved in unmanned driving includes many aspects, such as computer vision, automatic control technology and so on.

 

Developed countries such as the United States, the United Kingdom, and Germany have been engaged in the research of driverless cars since the 1970s, and China has also started the research of driverless cars since the 1980s.
In 2005, a self-driving car named Stanley ran the wild terrain track in the Mojave Desert in the United States at an average speed of 40km/h. It took 6 hours, 53 minutes and 58 seconds to complete a driving of about 282 kilometers.

 

Stanley was modified from a Volkswagen Touareg. It was completed by the cooperation of Volkswagen Technology Research Department, Volkswagen Group’s Electronic Research Laboratory and Stanford University. It is equipped with cameras, radars, and laser rangefinders. Such devices can sense the surrounding environment, and an automatic driving control system is installed inside to complete operations such as command, navigation, braking, and acceleration.

 

In 2006, Carnegie Mellon University developed the driverless car Boss. The Boss can safely drive through the streets where there is an air force base in accordance with traffic rules and avoid other vehicles and pedestrians.

 

In recent years, with the rise of the artificial intelligence wave, unmanned driving has become a hot topic, and many domestic and foreign companies have invested in the research of autonomous driving and unmanned driving. For example, Google’s Google X Lab is actively developing the driverless car Google Driverless Car. Baidu has also launched the “Baidu Driverless Car” research and development program. Its self-developed driverless car Apollo also appeared in the 2018 CCTV Spring Festival Gala. .

 

However, in the past two years, it has been found that the complexity of driverless driving far exceeds what was expected a few years ago, and there is still a long way to go before it is truly commercialized.

 

2. Face recognition

 

Face recognition, also known as portrait recognition and facial recognition, is a kind of biometric recognition technology based on the facial feature information of people. The technologies involved in face recognition mainly include computer vision and image processing.

 

The research of face recognition system began in the 1960s, and then, with the development of computer technology and optical imaging technology, the level of face recognition technology has been continuously improved in the 1980s. In the late 1990s, face recognition technology entered the primary application stage. At present, face recognition technology has been widely used in many fields, such as finance, justice, public security, border inspection, aerospace, electric power, education, medical treatment, etc.

 

There is an interesting case about the application of facial recognition technology: Jacky Cheung was named “fugitive nemesis” because the police used facial recognition technology to catch fugitives at his concert many times.

 

On April 7, 2018, after Jacky Cheung’s Nanchang concert began, a fan in the stands was taken away by the police. In fact, he was a fugitive, and security personnel locked him in the stands through the face recognition system;
On May 20, 2018, at a concert by Jacky Cheung in Jiaxing, the suspect Yu was identified as a fugitive by the facial recognition system when he passed the security gate, and was subsequently arrested by the police. With the further maturity of face recognition technology and the improvement of social recognition, it will be applied in more fields and bring more changes to people’s lives.

 

3. Machine translation

 

Machine translation is a branch of computational linguistics, which is the process of using computers to convert one natural language into another. The technology used in machine translation is mainly Neural Machine Translation (NMT), which currently outperforms humans in many languages.

 

With the acceleration of economic globalization and the rapid development of the Internet, the value of machine translation technology in promoting political, economic, and cultural exchanges has become prominent, and it has also brought many conveniences to people’s lives. For example, when we read English documents, we can easily convert English to Chinese through websites such as Youdao Translation and Google Translation, which eliminates the trouble of looking up the dictionary and improves the efficiency of study and work.

 

4. Voiceprint recognition

 

There are many types of biometric recognition technologies. In addition to face recognition, voiceprint recognition is currently used more frequently. Voiceprint recognition is a biological authentication technology, also known as speaker recognition, including speaker identification and speaker confirmation.

 

The working process of voiceprint recognition is that the system collects the speaker’s voiceprint information and enters it into the database. When the speaker speaks again, the system will collect this voiceprint information and automatically compare it with the existing voiceprint information in the database. , So as to identify the identity of the speaker.

 

Compared with traditional identification methods (such as keys and certificates), voiceprint recognition has the characteristics of anti-forgetting and remote authentication. Under the existing algorithm optimization and random password technical means, voiceprint can also effectively prevent recording, Anti-synthesis, therefore high security, rapid response and accurate identification.

 

At the same time, compared with biometric recognition technologies such as face recognition and iris recognition, voiceprint recognition technology has the characteristics of collecting the user’s voiceprint characteristics through telephone channels, network channels, etc., so it has great advantages in remote identity verification.

 

At present, the voiceprint recognition technology has multiple application cases such as voiceprint core, voiceprint lock and blacklist voiceprint library, which can be widely used in finance, security, smart home and other fields, with rich landing scenarios.

 

applications of artificial intelligence

5. Intelligent customer service robots

 

Intelligent customer service robot is an artificial intelligence entity form that uses machines to simulate human behavior. It can realize speech recognition and natural semantic understanding, and has the capabilities of business reasoning and verbal response.

 

When a user visits a website and sends a conversation, the intelligent customer service robot will quickly analyze the user’s intentions based on the visitor’s address, IP, and access path obtained by the system, and respond to the user’s real needs. At the same time, the intelligent customer service robot has a large knowledge base of industry background, which can provide standard responses to regular questions consulted by users, and improve the accuracy of response.

 

Intelligent customer service robots are widely used in business services and marketing scenarios to solve problems and provide decision-making basis for customers. At the same time, the intelligent customer service robot can be adaptively trained with rich conversational data during the response process, so its response skills will become more and more accurate.

 

With the vertical development of intelligent customer service robots, it has been able to deeply solve the problems in many enterprise segmentation scenarios. For example, the pre-sales consultation problems faced by e-commerce companies.

 

For most e-commerce companies, the pre-sales problems that users consult generally revolve around topics such as prices, discounts, and sources of goods. Traditional manual customer service repeats these categories every day. To answer sexual questions, it is impossible to provide services to customer groups with more complex problems in a timely manner.

 

The intelligent customer service robot can answer all kinds of simple and repetitive questions from users, and can also provide users with round-the-clock consultation and answering and problem-solving services. Its wide application also greatly reduces the cost of manual customer service for enterprises.

 

6. Intelligent outbound robots

 

Intelligent outbound robot is a typical application of artificial intelligence in speech recognition. It can automatically initiate outbound phone calls and actively introduce products to user groups in the form of natural human voice synthesized by speech.

 

During an outbound call, it can use speech recognition and natural language processing technology to obtain customer intent, and then use targeted speech techniques to conduct multiple rounds of interactive conversations with users. Finally, it classifies users and automatically records the key points of each call. Successfully complete outbound work.

 

Since the beginning of 2018, smart outbound robots have shown a blowout-like rise. They can respond, sort, record and track automatically without emotional fluctuations during the interaction process, helping companies to complete some tedious, repetitive and costly tasks. Because of their timely operation, thereby liberating labor, reducing a large amount of labor costs and repetitive labor, they allow employees to focus on target customer groups, thereby creating higher business value. Of course, the intelligent outbound robot also brings another side, that is, it will cause frequent interruptions to users.

 

7. Smart speakers

 

Smart speakers are electronic product applications and carriers of artificial intelligence technologies such as speech recognition and natural language processing. With the rapid development of smart speakers, they are also regarded as the future entrance of smart homes. In its essence, smart speakers are machines with voice interaction capabilities that can complete conversations.

 

Through direct dialogue with it, home consumers can complete self-service song ordering, control home equipment and evoke life services and other operations.

 

Speaker support intelligent interactive features of the antecedent basis include converting voice to text ASR (Automatic Speech Recognition, ASR) technology to analyze the text of speech, syntax, semantics of natural language processing (Natural Language Processing, NLP ) technology, and converting the text into a voice stream of natural speech synthesis (text to speech, TTS) technology.

 

With the blessing of artificial intelligence technology, smart speakers have gradually created more applications in home scenarios with more natural voice interaction methods.

 

8. Personalized recommendation

 

Personalized recommendation is an artificial intelligence application based on clustering and collaborative filtering technology. It is based on massive data mining. It builds a recommendation model by analyzing users’ historical behaviors and actively provides users with information that matches their needs and interests such as product recommendations, news recommendations, etc.

 

Personalized recommendation can not only quickly locate the desired products for users, weaken users’ passive consumption awareness, increase user interest and retention, but also help businesses quickly attract traffic, identify user groups and positioning, and do a good job in product marketing.

 

Personalized recommendation systems widely exist in various websites and apps. In essence, it considers multiple factors such as the user’s browsing information, user basic information, and preference for items or content, and relies on recommendation engine algorithms to classify indicators. The information content consistent with the user’s target factors is clustered, and the collaborative filtering algorithm is used to achieve accurate personalized recommendation.

 

9. Medical image processing

 

Medical image processing is currently a typical application of artificial intelligence in the medical field. Its processing objects are medical images generated by various imaging mechanisms, such as magnetic resonance imaging and ultrasound imaging, which are widely used in clinical medicine.

 

Traditional medical imaging diagnosis mainly finds the diseased body by observing two-dimensional slice images, which often depends on the doctor’s experience to judge. The use of computer image processing technology can perform image segmentation, feature extraction, quantitative analysis, and comparative analysis of medical images, and then complete lesion identification and labeling, automatic delineation of the target area of the image for tumor radiotherapy, and three-dimensional surgery.

 

This application can assist doctors in qualitative or even quantitative analysis of lesions and other target areas, thereby greatly improving the accuracy and reliability of medical diagnosis. In addition, medical image processing also plays an important auxiliary role in medical teaching, surgical planning, surgical simulation, various medical research, and medical two-dimensional image reconstruction.

 

10. Image search

 

Image search is an information retrieval application that has been increasingly demanded by users in recent years. It is divided into two types of search methods: text-based and content-based. Traditional image search only recognizes the color, texture and other elements of the image itself. Image search based on deep learning also accounts for semantic features such as human face, posture, geographic location, and characters, and performs multi-dimensional analysis and matching on massive data.

 

The application and development of this technology is not only to meet the current needs of users using image matching search to find the same or similar objects smoothly, but also to analyze the needs and behaviors of users, such as searching for the same paragraph, comparing similar objects, etc. to ensure that the company’s product iterations and service upgrades are more focused in the follow-up work.

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