Development Of Artificial Intelligence Technology Research Articles

Ethics and Law for the Use and Development of Artificial Intelligence Technology

The development and use of Artificial Intelligence is growing exponentially, resulting in various implications in multi-sectors such as misuse of personal data, assessments prone to bias, uncontrolled automation, etc.) There is a legal vacuum (absence of law) in the use of Artificial Intelligence, so ethical principles of use are needed as a basis for developing a legal framework for Artificial Intelligence in the form of international conventions (law making treaties). Ethics and Law for the Use and Development of Artificial Intelligence Technology is that the use of AI can be an alternative and new breakthrough as an effort to prevent and minimize violations of ethical codes such as by public auditors. In the audit process, AI is designed to collect and determine data dynamically and then process the data to detect fraud in a relatively shorter time and with a higher level of accuracy than auditors in general. However, in the audit process carried out by AI, if viewed from the law enforcement factor, it cannot be said to be a legal subject because the requirements for law enforcement factors, namely facilities and facilities, have not been fulfilled.

Искусственный интеллект в банках: первые результаты, препятствия и риски

The use of artificial intelligence is becoming one of the development trends in the financial industry. Many companies have already mastered the first directions of the application of artificial intelligence and are now looking for new ways to use this technology. The article analyzes the current state, development trends, prospects and risks of introducing artificial intelligence technologies into the work processes of Russian banks. The dynamics of the Russian artificial intelligence technology market for 2022 are presented, examples of practical business solutions using these technologies in marketing are given, and their advantages and disadvantages are highlighted. The purpose of the article is to identify the risks and prospects for the development of artificial intelligence technologies in the banking sector based on an assessment of the current results of using this technology in banks and the opinions of their clients. Based on the author's survey of respondents, a study of attitudes towards such changes was conducted. The survey results showed that the younger generation is ready to interact with artificial intelligence systems, but at the same time there is still distrust in the solutions and products offered on their basis. The first results of introducing artificial intelligence technologies into business processes are presented using the example of Sberbank, which, being the largest Russian bank, is actively developing and implementing this technology into its systems and products. Thus, the scope of artificial intelligence will expand and cover new directions, facilitating labor-intensive and routine tasks for personnel. Companies and banks without the introduction of artificial intelligence into business processes may lose their competitiveness in the future.

The use of artificial intelligence in interrogation: lies and truth

Following the development of artificial intelligence technology, a new trend has emerged in which this technology is increasingly used in case investigations. In this study, we developed a lie detection system that can instantly determine whether an interrogee is lying depending on their emotional responses to specific questions. Investigators then use these data, in addition to their personal experiences and case information, to adjust their interrogation strategies and techniques, thereby leading the interrogee to confess and accelerating the investigation process. Our system collects data using OpenFace and performs deep learning using gcForest. Deep learning training was performed using a real-life trial dataset, the Miami University Deception Detection Database, and a bag-of-lies dataset, and their corresponding trained systems achieved a detection accuracy of 95.11%, 90.83%, and 88.19%, respectively.

인공지능 이미지 생성 과정에서의 심미적 경험 고찰

Today, art is expanding in terms of economic efficiency with the development of artificial intelligence(AI) technology and opening up opportunities for creation to the general public, just as it has very effi ciently replaced human labor and time. Amid growing interest in copyright and execution subjects in images generated by AI behind the economic benefi ts of the breakthrough development of artificial intelligence technology, this study examines the difference between the aesthetic experience when the researcher drew an actual picture himself and when creating images through AI programs.
 With the development of ChatGPT utilizing AI, data information processing technology between language and images opens the horizon for new art creation. The implications of this study can be presented in three dimensions in terms of diff erentiated directions from existing art education. First, in relation to developing creativity through thought that can be made up of accumulation of knowledge and long experiential learning, image creation by AI lacks a process of humanistic thinking and needs to be reinforced in terms of learning. Second, the subject of education related to the creation of professional AI images and the setting of educational content and direction according to the subject are required through discussions on whether art AI utilization technology should be taught in engineering fi elds or in art fi elds as a matter of subjectivity and execution power. Finally, in art education, AI should drive a reconsideration of what kind of teaching methods are eff ective and what concepts are required for AI learning in cognitive science and pedagogical theories and methods.
 In conclusion;with the development of ChatGPT;artificial intelligence opens up the horizon for new art creation through data information processing between language and images;and the purpose and meaning of traditional art education need to accommodate this change and transform in the practice of liberal arts education at a time when the development of these technologies is required. The implications of this study show that by identifying the unique attributes of art regardless of the major area;understanding the functions of valuable works;and providing aesthetic experiences;a new horizon must be devised for liberal arts education that enables humanities;sociological;and engineering combinations as well as simple artificial intelligence technologies.

The Implications of Utilizing Artificial Intelligence-Based Parenting Technology on Children's Mental Health: A Literature Review

The study aims to look at the development of artificial intelligence to support positive parenting and help improve children's mental health. This research method uses the PRISMA approach through a search process using the keywords "Parenting, Child Mental Health, Artificial Intelligence, Communication, and Discipline Enforcement on the Scopus, Google Scholar, Science Direct, and Pubmed databases in the period 2018 to 2023 with a five-step process to obtain data. After elimination, 1,223 journal articles were obtained that met the inclusion and exclusion criteria of 11 journal articles. Based on the results of the literature review, information was identified regarding the history of the development of artificial intelligence technology, artificial intelligence significantly has a positive impact on children's mental health, through the use of artificial intelligence parents gain meaningful literacy in providing positive care for children. However, the use of artificial intelligence can lead to parents' dependence on artificial intelligence, which is feared to replace human figures. In conclusion, studying the development and adequate use of artificial intelligence technology is urgently needed to improve the ability and quality of parenting to support the optimization of children's mental health.

Research on the impact of artificial intelligence on the employment environment of labors in China

Purpose: The development of artificial intelligence technology can undoubtedly trigger profound changes in the labor market. Empirical analysis of the impact of artificial intelligence on the employment environment and its mechanism can propose effective paths to optimize the employment environment, which can effectively promote the improvement of the employment environment for Chinese labors and the stability of the employment situation. Based on the limitations of the development of artificial intelligence technology in China, this study focuses on studying its impact on the employment environment in the short term, and the long-term effects on the employment environment in China will become the author's future focus and research direction. Methodology: This study uses quantitative research methods, based on Panel data of 30 provinces in China from 2009 to 2019, and uses program computer software Stata 17.0 for data processing and empirical analysis. Conclusion: AI technology has a significant positive role in promoting the employment environment, and the new Information infrastructure represented by the Internet is conducive to promoting the positive moderating of AI on the employment environment. Through further research, it was found that the employment environment improvement effect of AI technology has regional differences, with a greater impact on the employment environment in the eastern region than in the western region, while the impact on the central region is not significant.

Chatbots in Foreign Language Teaching: Current Issues and Prospects for Forthcoming Research

The current stage is characterized by the dynamic development of artificial intelligence technologies and their introduction into foreign language teaching. Chatbots are one of such dialogical interactive teaching programs capable of developing foreign-language oral and written speech skills of a learner by maintaining a dialogue with them and imitating human speech on the basis of natural language and machine learning technologies and the algorithms of human speech embedded in the programme. The present paper is based on the analysis and synthesis of methodological studies devoted to the development of methods of teaching pupils and students foreign-language oral and written speech interaction based on chatbots. The most frequently discussed aspects in the problems of modern research are identified, and the prospects for further research related to the use of chatbots in teaching a foreign language are defined. The most commonly discussed aspects in methodological papers include: a) identifying learners’ attitudes towards the use of chatbots; b) identifying the possibility of developing learners’ written and oral speech skills based on chatbots; c) determining the level of learners’ proficiency in a foreign language for educational interaction with a chatbot. As prospective research, we identify studies aimed at a) developing step-by-step methods of teaching foreign language speech interaction based on chatbots; d) defining the nomenclature of foreign language speech interaction skills with a chatbot.

Экзистенциальные риски искусственного интеллекта в контексте аксиологии трансгуманизма (по материалам работ Н. Бострома)

The limitation on the development of artificial intelligence technologies which well-known developers of these technologies have recently been talking about, is due to fears of existential risks, one of which, according to the theory of the philosopher N. Bostrom, is Superintelligence as a result of the further development of artificial intelligence. Despite the fact that the problem of artificial intelligence has been widely studied in foreign philo-sophical literature, the issue of values underlying new technologies remains practically unstudied. The article substantiates that Bostrom's theory of risks of superintelligence is due to axiological reasons: artificial intelli-gence, according to the philosopher, can pose a threat to transhumanistic values. On the other hand, to prevent a catastrophe, it is these values that are supposed to be the basis for the development of artificial intelligence. The aim of the article is to analyze ethico-philosophical values of transhumanism and their role in the devel-opment of artificial intelligence technologies. It is noted that, in fact, current AI projects are developing precisely in the spirit of the philosophy of transhumanism. The scientific novelty of the article is due to the ethical and philosophical analysis of transhumanistic values, which N. Bostrom proposes to use in the context of the devel-opment of AI. As a result, the ethical meanings of the development of AI are assessed in the aspect of N. Bostrom’s concept of transhumanistic values.

Research on the Impact of ChatGPT on the Chinas Economy

In recent years, the rapid development of artificial intelligence technology has had a wide and profound impact on the global economy. As one of the important technologies in the field of artificial intelligence, ChatGPT has the ability to generate automated conversations and has attracted widespread attention. However, the impact of ChatGPT on the Chinese economy has not yet been comprehensively studied. This study aims to explore the impact of ChatGPT technology on the Chinese economy, and provide theoretical guidance and practical references for further understanding and addressing the challenges and opportunities brought about by the rapid development of artificial intelligence technology. This article adopts the methods of literature review and case analysis, combined with an overview of ChatGPT technology, to discuss the application of ChatGPT technology in the Chinese economic field from the perspectives of direct and indirect impacts. In terms of direct impact, the automated conversation generation ability of ChatGPT will greatly improve efficiency and experience in areas such as customer service, online education, and intelligent assistants. In terms of indirect impact, ChatGPT technology will change the industry landscape, promote economic structural upgrading, and enhance innovation capabilities.

MetaPINNs: Predicting soliton and rogue wave of nonlinear PDEs via the improved physics-informed neural networks based on meta-learned optimization

Efficiently solving partial differential equations (PDEs) is a long-standing challenge in mathematics and physics research. In recent years, the rapid development of artificial intelligence technology has brought deep learning-based methods to the forefront of research on numerical methods for partial differential equations. Among them, physics-informed neural networks (PINNs) are a new class of deep learning methods that show great potential in solving PDEs and predicting complex physical phenomena. In the field of nonlinear science, solitary waves and rogue waves have been important research topics. In this paper, we propose an improved PINN that enhances the physical constraints of the neural network model by adding gradient information constraints. In addition, we employ meta-learning optimization to speed up the training process. We apply the improved PINNs to the numerical simulation and prediction of solitary and rogue waves. We evaluate the accuracy of the prediction results by error analysis. The experimental results show that the improved PINNs can make more accurate predictions in less time than that of the original PINNs.

An Analysis of the Impact of Industrial Robots on Employment of College Students

With the development of artificial intelligence technology and the promotion and application of industrial robots, it has had a profound impact on economic behavior and reform, as well as on the job market. As an important part of the labor market, the impact of AI and industrial robots on the employment situation of college students is also a topic worth studying. This paper investigates the impact that AI and industrial robots will have on the overall employment number as well as the employment number of college students through the data of the employment market in 30 Chinese provinces from 2015 to 2019 with the OLS and fixed effect model. The empirical research results prove that the development of AI technology and the promotion and application of industrial robots will have the impact of the promotion effect on the overall employment number as well as the employment number of college students.

DeepSA: a deep-learning driven predictor of compound synthesis accessibility

With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being developed. However, we are often confronted with the question of whether these compounds are easy or difficult to synthesize, which refers to synthetic accessibility of compounds. In this study, a deep learning based computational model called DeepSA, was proposed to predict the synthesis accessibility of compounds, which provides a useful tool to choose molecules. DeepSA is a chemical language model that was developed by training on a dataset of 3,593,053 molecules using various natural language processing (NLP) algorithms, offering advantages over state-of-the-art methods and having a much higher area under the receiver operating characteristic curve (AUROC), i.e., 89.6%, in discriminating those molecules that are difficult to synthesize. This helps users select less expensive molecules for synthesis, reducing the time and cost required for drug discovery and development. Interestingly, a comparison of DeepSA with a Graph Attention-based method shows that using SMILES alone can also efficiently visualize and extract compound’s informative features. DeepSA is available online on the below web server (https://bailab.siais.shanghaitech.edu.cn/services/deepsa/) of our group, and the code is available at https://github.com/Shihang-Wang-58/DeepSA.

An endogenous intelligent architecture for wireless communication networks

AbstractThe challenges posed by the future wireless communication network, which will be a huge system with more complex structures, diverse functions, and massive communication ends, will be addressed by intelligent wireless communication technologies. These technologies are playing an increasingly important role in network architecture, computing architecture, resource allocation algorithm design, etc., thanks to the rapid development of artificial intelligence technologies, particularly the deep learning technologies, and their extensive application in various domains. In this paper, an endogenous intelligent architecture is developed to effectively clarify and understand in-depth the relationship among the factors by constructing wireless knowledge graph for the air interface transmission, the core network, as well as the network environment, and so on. Furthermore, the knowledge graph simultaneously reveals the structure and operation mechanism of the whole wireless communication networks. Cause tracing, intelligent optimization, and performance evaluation are sequentially implemented based on the knowledge graph, thus forming a complete closed-loop for endogenous intelligent wireless communication networks.

Analysis of the Implementation of AI Techniques in Valuation for Science and Technology Industry

In the wave of science and technology in the 21st century, artificial intelligence (AI) has become a force that cannot be ignored. This paper mainly studies the application of artificial intelligence (AI) technology in the valuation analysis of the technology industry. First, it discusses the basic concepts and development of AI technology, as well as the basic theories and methods of valuation analysis in the technology industry. It then analyzes the application of AI technology to valuation analysis in the technology industry, including improving the efficiency and accuracy of valuation analysis, and as a new source of value. It also explores the challenges and prospects of AI technology in technology industry valuation analysis, including the collection, processing and protection of data, as well as legal and ethical issues. Finally, it analyzes the practical application of AI technology in the valuation analysis of science and technology industry, including the case analysis of practical application, the effect evaluation, and the suggestions and prospects of practical application. These results indicate that the application of AI technology in the valuation analysis of the technology industry has great potential and value, but it also faces some challenges and problems. Therefore, the application of AI technology in the valuation analysis of the science and technology industry should be actively explored and utilized to achieve the sustainable development and value enhancement of the science and technology industry.

The concept of deepfake in Russian law, classification of deepfake and issues of their legal regulation

The article deals with the issues of legal regulation of deepfake in the Russian Federation. Legal regulation of deepfake does not keep up with the pace of development of artificial intelligence technologies. The authors emphasize that there is no definition of deepfake in the current legislation, and the existing formulations in scientific works are extremely contradictory in nature. Taking into account the pace of development of artificial intelligence technologies, it is necessary to legislate the definition of deepfake. The authors note that the classification of deepfakes is fundamentally important for the legal regulation of these technologies. According to the results of the analysis of modern neural networks the species classification of deepfakes is offered. Taking into account the authors' proposed definition of the concept of "deepfake" and taking into account the lack of legal mechanisms to regulate social relations in the sphere of use and distribution of deepfake, which cause the development of digital transformation, it is important to form mechanisms to adapt the legal system to the challenges associated with the development of deepfake technologies.

Carbon Emission Performance of Robot Application: Influencing Mechanisms and Heterogeneity Characteristics

With the new round of technological revolution and industrial change, industrial robots have an important role to play in the fight against climate change and in achieving the goal of “carbon peaking and carbon neutrality.” Based on the panel data of the application level of industrial robots in Shanghai and Shenzhen A-share listed companies from 2011 to 2019, this study examines the impact of industrial robots on carbon emission performance and discusses specific ways industrial robots can affect carbon emission performance. The results show that industrial robots can significantly improve carbon emission performance. Mechanism analysis shows that industrial robots can improve carbon emission performance through productivity and competition effects. Heterogeneity analysis shows that the application effect of industrial robots varies based on enterprise nature, regional location, and carbon emission intensity. The study can make potential contributions. First, this study systematically analyzes the impact of artificial intelligence technology on carbon emissions from the perspective of carbon emission performance, which can supplement the research on carbon emission performance. Second, this study calculates application levels of artificial intelligence technology at the enterprise level and uses panel and linear intermediary effect models to analyze the transmission mechanism between the application of artificial intelligence technology and carbon emission performance. Third, the heterogeneity analysis results can provide empirical support for formulating differentiated artificial intelligence carbon reduction strategies and be used as a reference to further promote the green development of artificial intelligence technology.

Development of a Traffic Speed Limit Sign Detection System Based on Yolov4 Network

Recent developments in artificial intelligence technologies have accelerated the transition to smart systems in the automotive industry. By anticipating driving conditions, these technologies enable the prevention of driver-related errors and accidents as well as the provision of crucial information to the driver. In this study, an artificial intelligence-based system is designed to provide information to drivers about speed signs on the road in order to support traffic safety. In this system, Yolov4 model is used to achieve high speed and accuracy levels. After the model training, the model was validated and the test results were found to be 98%.

Research on the Declining Trend of Shale Gas Production Based on Transfer Learning Methods

With the development of artificial intelligence technology, machine learning-based production forecasting models can achieve the rapid prediction and analysis of production. However, these models need to be built on a large dataset, and having only a small amount of data may result in a decrease in prediction accuracy. Therefore, this paper proposes a transfer learning prediction method based on the hierarchical interpolation model. It uses data from over 2000 shale gas wells in 22 blocks of the Marcellus Shale formation in Pennsylvania to train the transfer learning model. The knowledge obtained from blocks with sufficient sample data is transferred and applied to adjacent blocks with limited sample data. Compared to classical production decline models and mainstream time-series prediction models, the proposed method can achieve an accurate production decline trend prediction in blocks with limited sample data, providing new ideas and methods for studying the declining production trends in shale gas.

Chatbot Deteksi Awal Gangguan Kecemasan Menggunakan Dialogflow

Nowadays, anxiety disorders are experienced by many individuals, making a significant impact on one's quality of life. Some people are unaware of the symptoms of anxiety disorders, making anxiety disorders trivial. This situation can cause serious physical and emotional discomfort, in some cases, leading to more severe impacts if not treated appropriately. One of the first steps in overcoming anxiety disorders is early detection. The earlier the disorder is detected, the better the chances of providing effective treatment and reducing its impact. The development of artificial intelligence technology has opened up new opportunities to address this problem. This research proposes an innovation in the form of a chatbot. The purpose of this study is to determine the feasibility and acceptability of a chatbot to identify and provide information related to symptoms of anxiety disorders. The research methodology includes Data Collection, conversation formation, model formation, implementation using Dialogflow, testing and results. The results of UAT testing on respondents consisting of students and psychologists obtained results of 84% and 74%, respectively.

A Visual Fault Detection Algorithm of Substation Equipment Based on Improved YOLOv5

The development of artificial intelligence technology provides a new model for substation inspection in the power industry, and effective defect diagnosis can avoid the impact of substation equipment defects on the power grid and improve the reliability and stability of power grid operation. Aiming to combat the problem of poor recognition of small targets due to large differences in equipment morphology in complex substation scenarios, a visual fault detection algorithm of substation equipment based on improved YOLOv5 is proposed. Firstly, a deformable convolution module is introduced into the backbone network to achieve adaptive learning of scale and receptive field size. Secondly, in the neck of the network, a simple and effective BiFPN structure is used instead of PANet. The multi-level feature combination of the network is adjusted by a floating adaptive weighted fusion strategy. Lastly, an additional small object detection layer is added to detect shallower feature maps. Experimental results demonstrate that the improved algorithm effectively enhances the performance of power equipment and defect recognition. The overall recall rate has increased by 7.7%, precision rate has increased by nearly 6.3%, and mAP@0.5 has improved by 4.6%. The improved model exhibits superior performance.