Key Artificial Intelligence Technologies Research Articles

Research on Key Technologies of Artificial Intelligence for Intelligent Manufacturing

Intelligent manufacturing, as the main form of future manufacturing, is the highland of a new round of competition in the global manufacturing industry, which will fundamentally change the production and manufacturing methods and technological and economic paradigms formed by human beings since the industrial revolution. At present, the intelligent manufacturing industry is still in the early stage of development, which is characterized by three typical characteristics: continuous growth of market size, strong promotion by national governments and diversification of competition subjects. At present, China's manufacturing industry is facing transformation, and the manufacturing industry is bound to need to transform to intelligent manufacturing. Industrial robots are the main content of current intelligent manufacturing, how to fully apply artificial intelligence technology to the manufacturing of industrial robots, so that its manufacturing efficiency, manufacturing accuracy and other product attributes are improved, to maximize its economic benefits, has a particularly important application value. The application of intelligent manufacturing and robots can not only promote the faster development of manufacturing, but also reconstruct new forms of manufacturing. Based on this, the paper first summarizes the application technology of intelligent manufacturing, and then analyzes the key technology of robot application for reference to relevant workers.

The key artificial intelligence technologies in early childhood education: a review

The key artificial intelligence technologies in early childhood education: a review

Artificial intelligence and autonomous driving

With the rapid development of science and technology, artificial intelligence and autonomous driving technology are becoming a hot topic in today's society. The integration of artificial intelligence and driving systems satisfies this requirement effectively and produces a new driving technology for the sake of traffic safety and a better driving experience. To increase driving safety and comfort, computer calculations are utilized to aid the driver or eliminate the interference of human variables. First, this study introduce the basic concepts and principles of artificial intelligence and autonomous driving, as well as their importance in practical applications. Secondly, this paper discuss in detail the key technologies of artificial intelligence in autonomous driving, including the application of perception, decision making, and control. Finally, his essay explore the challenges facing artificial intelligence and autonomous driving, including technical challenges, legal and ethical considerations. Through the research of this paper, we can better understand the relationship between artificial intelligence and autonomous driving, and provide reference and guidance for future development.

On the Development and Application of Computer Artificial Intelligence Technology

With artificial intelligence (AI) technology as the focus of present research and application, the new cross-technology formed by AI and multiple technologies has found wide application in mobile Internet, finance, medical care, education and other industries. Currently, countries around the world are constantly accelerating the development and application of AI technology by increasing policy support and funding for its development. This paper analyzes the development direction and application potential of AI technology by studying the development of AI technology and exploring the key technologies of AI.

Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase, more companies are attempting to apply artificial intelligence technology in the field of customer service, enabling intelligent customer service, reducing customer service pressure, and reducing operating costs. Currently, the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions, and complex user expressions are difficult to understand. To solve the problem of low accuracy of multi-round dialogue semantic understanding, this paper proposes a semantic understanding model based on the fusion of a convolutional neural network (CNN) and attention. The model builds an “intention-slot” joint model based on the “encoding–decoding” framework and uses hidden semantic information that combines intent recognition and slot filling, avoiding the problem of information loss in traditional isolated tasks, and achieving end-to-end semantic understanding. Additionally, an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text, thereby increasing the accuracy of semantic understanding. Finally, by applying the model to electric power intelligent customer service, we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

USE OF MACHINE LEARNING ALGORITHMS IN CYBER SECURITY

Artificial intelligence (AI) has made incredible progress, resulting in the most sophisticated software and standalone software. Meanwhile, the cyber domain has become a battleground for access, influence, security and control. This paper will discuss key AI technologies including machine learning in an effort to help understand their role in cyber security and the implications of this new technology. This paper discusses and highlights the different uses of machine learning in cyber security.

Research on the Application of Artificial Intelligence in Operation and Maintenance for Power Equipment

With the continuous deepening of the construction of the State Grid, more and more power grid equipment such as substations need to be repaired and maintained in a timely manner, which makes the workload and difficulty of the power grid equipment operation and maintenance personnel more and more difficult. Electricity is a necessity for our daily life and enterprise production. In order to ensure the rapid and stable economic development, reliable power supply must be provided, and the normal operation of power equipment is the key to safe and stable power supply. Due to the rapid development of cities, various problems have begun to appear in some power grid plans, coupled with the upgrading of power grid equipment and the implementation of new power grid plans, making the operation and maintenance of power grid equipment more and more difficult, and more and more challenges high. Therefore, this article first analyzes the process of intelligent state maintenance of power grids, analyzes the key technologies of artificial intelligence in the operation and maintenance of power equipment, and analyzes the application of artificial intelligence technology in the operation and maintenance of power equipment.

Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.

The main areas of artificial intelligence technologies in cybersecurity

The article examines the key technologies of artificial intelligence in order to use them to ensure the protection of information. It is shown that currently there is no general concept of artificial intelligence in cybersecurity, the most important methods of artificial intelligence that can be used in cybersecurity are not defined, and the role that these methods can play to protect organizations in cyberspace has not been established. As a key idea for the use of artificial intelligence in cybersecurity, the use of technologies and methods that facilitate the detection and response to threats using cyber attack statistics sets has been proposed. Priority areas for the use of artificial intelligence are network security and data protection.

A Review of Microsoft Academic Services for Science of Science Studies.

Since the relaunch of Microsoft Academic Services (MAS) 4 years ago, scholarly communications have undergone dramatic changes: more ideas are being exchanged online, more authors are sharing their data, and more software tools used to make discoveries and reproduce the results are being distributed openly. The sheer amount of information available is overwhelming for individual humans to keep up and digest. In the meantime, artificial intelligence (AI) technologies have made great strides and the cost of computing has plummeted to the extent that it has become practical to employ intelligent agents to comprehensively collect and analyze scholarly communications. MAS is one such effort and this paper describes its recent progresses since the last disclosure. As there are plenty of independent studies affirming the effectiveness of MAS, this paper focuses on the use of three key AI technologies that underlies its prowess in capturing scholarly communications with adequate quality and broad coverage: (1) natural language understanding in extracting factoids from individual articles at the web scale, (2) knowledge assisted inference and reasoning in assembling the factoids into a knowledge graph, and (3) a reinforcement learning approach to assessing scholarly importance for entities participating in scholarly communications, called the saliency, that serves both as an analytic and a predictive metric in MAS. These elements enhance the capabilities of MAS in supporting the studies of science of science based on the GOTO principle, i.e., good and open data with transparent and objective methodologies. The current direction of development and how to access the regularly updated data and tools from MAS, including the knowledge graph, a REST API and a website, are also described.

Artificial Intelligence for Cloud-Assisted Smart Factory

In the context of industry 4.0, the main way to realize the intelligent manufacturing is to build a smart factory integrated with the advanced technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI). With the aim to emphasize the role and potential of cloud computing and AI in improving the smart factories’ performances, such as system flexibility, efficiency, and intelligence, we comprehensively summarize and explain the AI application in a cloud-assisted smart factory (CaSF). In this paper, a vertically-integrated four-tier CaSF architecture is presented. Also, the key AI technologies involved in the CaSF are classified and described according to the logical relationships in the architecture hierarchy. Finally, the main issues and technical challenges of AI technologies in the CaSF systems are introduced, and some possible solutions are also given. The application of the AI in smart factories has accelerated the implementation of the industry 4.0 to the certain extent.

Business Process Innovation with Artificial Intelligence: Levering Benefits and Controlling Operational Risks

Artificial Intelligence (AI) is gaining a strong momentum in business leading to novel business models and triggering business process innovation. This article reviews key AI technologies such as machine learning, decision theory, and intelligent search and discusses their role in business process innovation. Besides discussing potential benefits, it also identifies sources of potential risks and discusses a blueprint for the quantification and control of AI-related operational risk.

CiteSeerX: AI in a Digital Library Search Engine

CiteSeerX is a digital library search engine that provides access to more than 4 million academic documents with nearly a million users and millions of hits per day. Artificial intelligence (AI) technologies are used in many components of CiteSeerX e.g. to accurately extract metadata, intelligently crawl the web, and ingest documents. We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We also show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.