How Does Artificial Intelligence Technology Influence Labor Share: The Role of Labor Structure Upgrading

Advances in Artificial Intelligence (AI) technologies and Autonomous Unmanned Vehicles are shaping our daily lives, society, and will continue to transform how we will fight future wars. Advances in AI technologies have fueled an explosion of interest in the military and political domain. As AI technologies evolve, there will be increased reliance on these systems to maintain global security. For the individual and society, AI presents challenges related to surveillance, personal freedom, and privacy. For the military, we will need to exploit advances in AI technologies to support the warfighter and ensure global security. The integration of AI technologies in the battlespace presents advantages, costs, and risks in the future battlespace. This chapter will examine the issues related to advances in AI technologies, as we examine the benefits, costs, and risks associated with integrating AI and autonomous systems in society and in the future battlespace.

Purpose: The purpose of the study is to examine the challenges faced by businesses in integrating and effectively utilizing artificial intelligence (AI) technology. It aims to provide a comprehensive understanding of how AI technologies generate business value and the anticipated benefits they offer. The study also seeks to identify the facilitators and inhibitors of AI adoption and usage, explore different types of AI use in the organizational environment, and analyze their first- and second-order impacts. Methodology: The study employed the comprehensive literature review research design. The researchers conducted a systematic search using predefined criteria in databases such as Scopus and Web of Science. The search yielded 21 relevant papers that were analyzed and synthesized for this study. The data collection method relied on the examination of existing literature. Data analysis involved identifying key themes, trends, and insights from the selected papers. The researchers conducted a qualitative analysis to extract relevant findings and synthesized the information to derive meaningful conclusions. Findings: The study revealed several insights regarding the integration and use of AI in businesses. This indicated that organizations struggle with understanding how AI technologies can generate value and how to effectively incorporate them into their operations. Lack of comprehensive knowledge about AI and its value generation processes was identified as a major barrier. Additionally, the study highlighted the facilitators and inhibitors of AI adoption and usage. It identified various types of AI applications in the organizational environment and explored their impacts on business operations. The findings shed light on the challenges businesses face in leveraging AI technology and suggested areas for further research. Recommendations: To practitioners: The study emphasizes the importance of acquiring comprehensive knowledge about AI technologies and their potential value generation processes. To policy makers: The study highlights the need for supportive policies and regulations to foster AI adoption. It suggests creating an enabling environment that promotes AI research and development. Theory and Validation: The study may have been informed by existing theories related to AI adoption, organizational change, or innovation. Practice: To practitioners, the study underscores the importance of understanding the value and potential of AI technologies. Policy: To policy makers, the study emphasizes the need for policy frameworks that promote AI adoption and address associated challenges.

Artificial intelligence (AI) technology has become one of the most frequently discussed subjects in the development of technology in recent years. Due to its incredible pattern recognition, it can help humans complete work much faster than before with little to no monetary cost. Despite the widespread impact that AI technologies have on various fields, acceptance and adoption of AI lag behind because of a wide range of factors among users. This paper outlines the results of a large literature review that attempts to tease out some of these factors by examining individual differences that may impact the acceptance and adoption of AI. This goal was achieved through an exploration of individual differences that play a role in the acceptance and adoption of new technologies more broadly, as well as AI technologies, to gain a more holistic understanding of the factors contributing to the lack of acceptance and adoption of AI. The main goal of this literature review was to find the individual differences (IDs) associated with the acceptance and adoption of AI technology and general technology. A secondary goal was to create a model based on the acceptance of general technology that could assist in future AI technology research, development, and implementation. This paper identifies several IDs that were found to play a role in the adoption and acceptance of AI technology, as well as 15 specific IDs that were commonly shown to play a role in the adoption and acceptance of general technology. Because of the rapid development of AI technologies in recent years, there is a lack of research examining the acceptance and adoption of AI technologies; however, there is a great deal of research examining the broader acceptance and adoption of technology, and there is significant overlap between the studies that examined general technology acceptance and adoption and those that examined AI-specific technology acceptance and adoption. Because of this, we believe that the research on general technology acceptance and adoption can be used as a foundation and inspiration for future research on AI technology in this area.

The development of artificial intelligence (AI) technology has become a major catalyst for digital transformation in Indonesia. However, the accelerated adoption of AI has not been matched by regulatory readiness, especially in the legal and cybersecurity aspects. The national legal framework is still sectoral and has not been able to address the complexity of risks from AI systems implemented in various public and private sectors. This research aims to: (1) identify regulatory weaknesses in monitoring the use of AI in Indonesia; (2) formulate an integrative legal framework between AI regulation and cyber law that is adaptive to technological developments; and (3) provide policy recommendations based on international practices. This research method uses a normative-empirical legal approach with a combination of documentation studies, comparative analysis of international regulations, and semi-structured interviews with experts. The results show that Indonesia experiences significant regulatory gaps, particularly in the application of the principles of transparency, accountability, and AI risk management. Compared to the European Union and the United States, AI regulations in Indonesia are still at the declarative stage without adequate enforcement mechanisms. This study recommends the establishment of a risk-based national legal framework accompanied by the strengthening of independent oversight institutions, AI technical standards, and multi-stakeholder involvement in the regulatory process. These findings are expected to serve as the basis for the development of legal policies that are more adaptive, responsive, and secure to advances in AI technology and the dynamics of cyber threats in Indonesia.

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

Masters or Slaves? AI and the Future of Humanity

Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology. Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy. Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences. Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology. Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.

Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology. Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy. Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences. Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology. Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.

Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology. Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy. Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences. Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology. Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.

Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology. Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy. Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences. Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology. Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.

Artificial intelligence in elderly healthcare: A scoping review.

With the rapid development of modern social economy, big data, artificial intelligence, block chain, cloud technology and other science and technology industries have penetrated into all sectors of society. The application of big data, cloud computing, artificial intelligence and other information technologies in enterprises has changed traditional enterprise models and promoted the formation of new business management models. Based on this background, the purpose of this paper is to provide a new thinking mode for enterprises with artificial intelligence technology, and to promote enterprises to transform from quantitative development to qualitative development. On the basis of traditional enterprises, this paper introduces advanced artificial intelligence technology to enhance the innovation ability of enterprises, and combines literature research method and case analysis method to design the scheme of promoting enterprise innovation by artificial intelligence technology from the perspective of theory and practice. The research results of this paper show that artificial intelligence is a strategic opportunity brought by the technological revolution. Under the guidance of artificial intelligence technology, the production efficiency of enterprises can be increased by 56% and the business opportunity of 30% increment can be brought, thus achieving the expected goal of bringing innovation and vitality to enterprises.

PurposeThe introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.Design/methodology/approachThe evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.FindingsThe results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.Originality/valueThe authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

The continuous goal of discovering extraterrestrial life has driven scientific interest in exoplanets—celestial bodies orbiting stars outside our solar system. Traditional methods of exoplanet detection, reliant on manual analysis and prone to human error, presented unavoidable challenges given the vastness of the universe. This paper aims to discuss the benefits and limitations of AI within the exoplanet detection field, and determine whether the highly-regarded artificial intelligence is as beneficial to astronomical fields as we think. With the introduction of the first ever fully-robotic exoplanet detector which takes high-precision radial velocity measurements to measure the gravitational reflex motion, and advancing computer algorithms that avoid human errors in data analysis, modern advancements in artificial intelligence (AI) technology have not only transformed the efficiency and accuracy of exoplanet detection, but also extended our understanding of these distant worlds. While the use of AI does have its benefits, there are several drawbacks that could potentially hinder further advancement in the field of exoplanet detection.

The recent development of artificial intelligence (AI) technology is bringing about changes at a faster pace and on a larger scale than any other period in human history. With technological advancements overcoming the limitations of medical AI through training with databases, AI technology has made remarkable progress since the inception of deep learning for image processing with convolutional neural networks (CNN) in 2012. The recent advancements in natural language processing (NLP) have accelerated the utilization of AI through sophisticated natural language processing, enabling machines to identify and understand data regardless of the complexity of the language. This has laid the foundation for the rapid and precise development of generative AI. In the era where generative AI is being utilized without pausing in its developmental speed, we considered the civil liability of AI in our civil law principles, taking into account the inherent characteristics of AI such as unpredictability, opacity, and the black box effect. To do this, we first examined the legal liability considering the stages of AI technology development in discussing the tort liability caused by AI. Even “Weak AI,” created by AI developers, may fall under “Gefahr,” and while not all types, some may apply to strict liability in terms of risk liability. Furthermore, while reviewing civil liability applicable to AI under fault-based and no-fault liability, we also looked at the trends in the EU comparatively. In discussing no-fault liability, particularly under the Product Liability Act, we examined the possibility and implications of applying risk liability to pharmaceutical manufacturing using generative AI technology as a representative example to overcome the limitations of the existing Product Liability Act. Humanity currently lives in an era of rapid technological development and exploding big data, enjoying numerous benefits due to these advancements. As user convenience improves and massive added value is created through technological progress, the meaning of risk liability in the realm of civil liability can gain more significance. Generative AI has already drastically reduced the costs and time required for new drug development, providing substantial profits to pharmaceutical companies. However, even if the existing Product Liability Act is applied, it may be difficult to adequately remedy the harm to victims due to the reasonable alternative possibility defense regarding design defects. In the era of generative AI, we examined the possibility of applying enhanced risk liability by assuming the case of pharmaceutical manufacturing.