Which enterprises are more competitive in AI-field from the perspective of patentometrics?

This study investigates the role and impact of artificial intelligence (AI) in the electric power industry through a thematic analysis of corporate communications. As AI technologies proliferate, industries—such as the electric power industry—are undergoing significant transformations. The research problem addressed in this study involves understanding how electric power companies perceive, adopt, and implement AI, as well as the implications of these developments. By employing a qualitative thematic analysis approach, we examined a corpus of corporate communications from innovation leaders, including annual reports and sustainability reports, in the electric power sector. The data spanned 2020 to 2023, capturing a crucial period of AI integration in the industry. Our analysis reveals several key findings. Firstly, there is a clear trend toward increased utilization of AI in various facets of the electric power sector, including grid management, predictive maintenance, and customer service. Companies actively invest in AI technologies to enhance operational efficiency, reduce costs, and improve service quality. Secondly, the corporate discourse has shifted significantly, with companies emphasizing AI’s role in sustainability efforts. Moreover, our analysis identified challenges and concerns associated with AI adoption in the electric power industry. In conclusion, the thematic analysis of corporate communications provides valuable insights into the evolving landscape of AI in the electric power industry. The findings underscore the transformative potential of AI technologies, highlighting opportunities for enhanced efficiency and sustainability. However, they also emphasize addressing challenges to ensure responsible and beneficial AI integration. This study contributes to the growing literature on AI in industries, offering practical implications for electric power companies, policymakers, and stakeholders navigating the AI-driven future of the sector.

A power plant converts energy from a non-electric form to an electric one. Depending on the energy conversion, power plants are classified as minerals, nuclear, solar, geothermal, hydroelectric, and so on. The main goal is to perform this transformation as best as possible. Criteria of safety, efficiency, reliability and affordability are taken into account as benchmarks. The station consists of several units that generate and work together to meet electricity needs. For a fossil fuel power plant, each unit consists of three main components: a boiler, a turbine and a generator. The complexity of the operation is due to the variability of the load and high efficiency required in a wide range of operations. The main difficulties for the management task then arise due to the strong link between process variables and process nonlinearity. The purpose of this article is to analyze and develop a proposal for the introduction of artificial intelligence in the power industry of Ukraine. To achieve this goal, we will offer the following tasks: - analyze existing systems for the use of artificial intelligence in industry; - develop proposals for the introduction of artificial intelligence in the electricity system of Ukraine. In this study, methods of statistical, factorial, historical, comparative, logical, economic-mathematical and systems analysis, the method of expert assessments were used, which allowed to formulate proposals for the introduction of artificial intelligence in the power industry. Artificial neural networks are the first step towards a fundamentally new system of information analysis. They are mathematical and computer models that simulate the work of biological neurons, ie a system of interacting processes, built on the principle of connecting nerve cells of the human brain. It should be noted that they differ from conventional machine algorithms in the ability to learn, memorize and reproduce images, determine patterns, memorize and analyze information and obtain results. Output signals that arrive at the next processor and continue to be converted. Thus, artificial neurons form networks and layers in which connections are created, restored, maintained and destroyed by special signals. With regard to artificial intelligence, there is no generally accepted definition of artificial intelligence, therefore, by artificial intelligence we mean a machine system capable of learning using objective knowledge and experience, to solve creative problems like the human brain and inventive tasks, not going through options, build strategies and apply abstract concepts. Digitalization and artificial intelligence are penetrating most sectors of the economy, including the electricity sector. The digitalization of energy requires the development and widespread use of end-to-end technologies, including industrial Internet, components of robotics, wireless communications, artificial intelligence and others. Thus, artificial intelligence technologies have prospects for development. Of course, significant targeted funding is needed to achieve significant results. The interaction of research institutes and universities with business is very important, where artificial intelligence technologies are also being developed. Attention of the authorities and society, their understanding of the importance of the tasks facing countries to achieve the goal of implementing artificial intelligence. With regard to electricity, the integration of artificial intelligence into the industry will help optimize and increase efficiency in all aspects of energy production, transmission and consumption. It should be noted that the development of electricity is a step towards the development of other industries. That is why the transition to the digital industry is impossible without the digitalization and intellectualization of the power industry

The introduction of artificial intelligence (AI) technologies, the construction industry is on track for a technological revolution. In order to investigate the potential of artificial intelligence (AI) to improve sustainability, safety, and efficiency in the construction industry, this research paper offers a thorough examination of these applications. The study looks at several AI methods, including robots, computer vision, machine learning, and natural language processing, and how they are used in the design, planning, scheduling, monitoring, and maintenance phases of the building lifespan. In order to show the concrete advantages of AI in maximizing resource allocation, cutting project delays, enhancing quality control, and minimizing risks, it also looks at case studies and real-world applications. The study also discusses ethical issues and addresses issues like security of data, workforce upskilling, and interaction with current systems. This report offers useful insights for practitioners, policymakers, and researchers interested in maximizing the revolutionary potential of artificial intelligence (AI) in the construction industry by integrating existing research and industry trends. In the construction industry, any error, miscalculation, or misinterpretation can result in claims, delays in projects, and large cost overruns. The documentation and construction contracting processes are very complex and time-consuming. This research is done to make the process of documentation easy using the AI tools. The respondent’s opinion is consistent (Cronbach alpha is greater than 0.80). Educational qualification is influencing application of AI in construction industry by stating that construction industry gets benefitted from AI-powered construction simulation tools helps in accurate 3D modelling for monitoring the progress of the project and also influencing the application of AI in construction industry through Workers are resistant to adopt AI technology due to their lack of skill & awareness in using this technology as a barrier/challenge and also influencing application of AI in construction industry by proving the phenomenal level of acceptance for Collaboration with AI technology developers helps in adopting the AI technologies in construction industry as an enabler to the challenges of application of AI in construction industry.

With the breakthroughs in artificial intelligence technology and the rapid development of intelligent manufacturing, industry and artificial intelligence (AI) are gradually being deeply integrated. On the basis of artificial intelligence, we systematically expounded the generation, definition, characteristics, classification, technical system, and current situation of industrial artificial intelligence (I-AI). Combining existing research and industrial projects, we propose a detailed framework and a reference model for I-AI in industry. The framework contains seven dimensions: objects of I-AI, domain of I-AI, application stages of I-AI, application requirements of I-AI, intelligent technology of I-AI, intelligent function of I-AI, and solutions of I-AI. Secondly, based on the application scenarios of artificial intelligence and industrial convergence, we propose a detailed overall planning for I-AI. Finally, five typical industrial fields are selected, and the I-AI solutions based on TFV (technology and function integration in industrial value chain) unit and 6W1H method are used for new application scenarios of the proposed framework. In addition, a detailed case of implementing for I-AI in port equipment industry is given. The research results of this paper have achieved good results in the related industrial field and can provide some reference for other industrial enterprises to plan, design, implement, and apply artificial intelligence.

Masters or Slaves? AI and the Future of Humanity

PurposeThis paper examines the relationship between Artificial Intelligence (AI) technology development and firm growth. Specifically, it aims to explore how the availability of AI influences firm growth and whether larger firms benefit more from AI-driven technological advancements compared to smaller firms.Design/methodology/approachUsing a dataset from CRSP-Compustat covering public firms from 1975 to 2023, this study employs price per memory (PPM) as a proxy for AI technology accessibility to assess its impact on firm growth. The analysis focuses on three key growth metrics: total assets, tangible assets and market capitalization. By examining how data processing capacity influences these growth rates, the study compares the performance of large firms to small firms. A panel data regression is conducted, controlling for macroeconomic trends and industry-specific effects on firm growth. Additionally, the study investigates the heterogeneous impacts of AI technology accessibility across firms of different sizes.FindingsThe findings reveal that PPM, as a proxy for AI technology availability, significantly affects firm growth. Specifically, larger firms experience faster growth, especially in recent years, as AI technology becomes more accessible and cost-effective. These results suggest that large firms gain the most substantial benefits from AI advancements, further widening the growth gap between large and small firms.Originality/valueThis research extends prior studies on the impact of AI on firm growth by introducing PPM as a novel proxy for AI availability. It provides new insights into how AI technologies disproportionately benefit larger firms and offers important policy implications regarding firm financing and information regulation. This study also highlights areas for future empirical research on the role of AI in the financial industry.

Technology based on artificial intelligence (AI) is a revolutionary force that is changing economies, civilizations, and industries all over the world. AI, which has its roots in computer science and cognitive psychology, is a wide range of tools and methods designed to make robots capable of doing activities that have historically required human intellect. This abstract examines the many facets of artificial intelligence (AI) technology, including its fundamentals, uses, difficulties, and ramifications. Artificial Intelligence (AI) technology comprises several subfields such as robotics, computer vision, natural language processing, machine learning, and expert systems. Particularly, machine learning techniques have propelled incredible progress by allowing computers to learn from data and make judgments or predictions without the need for explicit programming. Natural language processing allows machines to comprehend, interpret, and produce human language, hence facilitating human-computer interaction. Machines can now see, analyze, and interpret visual data from the real world thanks to computer vision technology. Applications of AI technology may be found in a wide range of industries, including manufacturing, healthcare, finance, transportation, agriculture, education, and entertainment. AI-powered solutions help in drug discovery, medical imaging analysis, diagnosis, and customized therapy in the healthcare industry. AI algorithms are used in finance to power automated trading, fraud detection, risk assessment, and customer support. AI makes it possible for transportation to include predictive maintenance, traffic management, and driverless cars. Artificial Intelligence enhances supply chain management, quality assurance, and production processes in manufacturing. AI technology has the potential to revolutionize many industries, but it also comes with dangers and problems. These include privacy concerns, security hazards, ethical dilemmas, issues with prejudice and fairness, and effects on society and employment. Responsible AI methods, legal frameworks, multidisciplinary cooperation, and ethical standards are all necessary to meet these issues. Future prospects for AI technology development include the ability to solve challenging issues, spur creativity, increase productivity, and improve quality of life. But to fully utilize AI, one must take a comprehensive strategy that strikes a balance between the advancement of technology and ethical issues, human values, and social well-being. In summary, artificial intelligence (AI) technology is at the vanguard of innovation, presenting never-before-seen possibilities to transform whole sectors, spur economic expansion, and tackle global issues. AI has the ability to usher in a future of greater human-machine collaboration, innovation, and wealth through the promotion of collaboration, transparency, and ethical stewardship. the Ranking of the Artificial Intelligence using the TOPSIS Method . Interpretable Models is got the first rank whereas is the Ethical AI is having the Lowest rank. Keywords: Explainable AI (XAI), Interpretable Models, Ethical AI ,Responsible AI, Robustness and Adversarial Defense, Continual Learning, Federated Learning, Human-Centric AI, AI Governance and Policy

In this article, we engage critically with the idea of promoting artificial intelligence (AI) technologies in recruitment as tools to eliminate discrimination in decision-making. We show that the arguments for using AI technologies to eliminate discrimination in personnel selection depend on presuming specific meanings of the concepts of rationality, bias, fairness, objectivity and AI, which the AI industry and other proponents of AI-based recruitment accept as self-evident. Our critical analysis of the arguments for relying on AI to decrease discrimination in recruitment is informed by insights gleaned from philosophy and methodology of science, legal and political philosophy, and critical discussions on AI, discrimination and recruitment. We scrutinize the role of the research on cognitive biases and implicit bias in justifying these arguments – a topic overlooked thus far in the debates about practical applications of AI. Furthermore, we argue that the recent use of AI in personnel selection can be understood as the latest trend in the long history of psychometric-based recruitment. This historical continuum has not been fully recognized in current debates either, as they focus mainly on the seemingly novel and disruptive character of AI technologies.

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

The rapid advancement of artificial intelligence (AI) technology is driving transformative changes across society. However, this progress also entails significant resource and energy demand, posing substantial new challenges to the Earth’s ecosystems. Specifically, the environmental impacts arising from AI model training and inference, data center operations, and the manufacturing and disposal of electronic devices threaten the balance of ecosystem material cycles and could exacerbate climate change. Therefore, it is urgently needed to understand the effects of generative AI technology growth on ecosystem material cycles and to identify sustainable AI technology development and application strategies. This study aims to quantitatively assess the resource consumption (including metals, plastics, and water), exergy use (primarily through electricity demand and fossil fuels), and greenhouse gas emissions associated with the anticipated growth of generative AI technology and its consequent impacts on ecosystem material cycles. First, we analyze resource and exergy use within the generative AI industry, encompassing AI model training and inference, data center operations, and the production of AI chips and devices. We quantify the consumption of key elements and water, alongside the exergy demand for electricity and fossil fuels. We employ a Life Cycle Assessment (LCA) methodology to evaluate the comprehensive environmental footprint of AI technology. Second, we examine the environmental impact of AI-related waste by evaluating the generation, treatment processes, and ecosystem effects of electronic waste (including AI chips, devices, and data center equipment). This analysis focuses on the environmental leakage pathways of hazardous and plastic waste and the patterns of material movement within the ecosystem, particularly with regards to soil and water pollution and biodiversity loss. Third, we model the impact of generative AI technology on key ecosystem material cycles, such as carbon, nitrogen, and phosphorus. We estimate changes in resource use, exergy consumption, and waste generation under multiple AI technology growth scenarios. Finally, we propose strategies for the sustainable development and application of AI technologies. Based on our findings, we will formulate concrete policy and technical recommendations for developing and implementing resource-efficient and low-exergy-consuming AI technologies.

With the development of science and technology, artificial intelligence technology has received more and more attention and attention. Under the background of the rapid development of big data and cloud computing, the artificial intelligence industry broke out. There is a huge amount of research on artificial intelligence and the artificial intelligence industry is huge. As far as the artificial intelligence industry in China is concerned, even the start is relatively late, but the industry scale, industrial layout, and technology research are all in a continuous improvement stage. Especially after the deepening of the layout of science and technology and manufacturing industries, the scale of artificial intelligence industry is further developed. More artificial intelligence products will appear at the same time. From the perspective of the concept, development history and new progress of artificial intelligence, this paper combines China’s artificial intelligence market and the development of artificial intelligence companies to analyze the current major application areas, and then further explore the future development trend of artificial intelligence.

The brave new world of artificial intelligence: dawn of a new era

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts.

The software industry's history is also its future. Its history has been defined by both abundance and scarcity, and its future will be, too. In the 1970s and 80s, perceived software scarcity led U.S. legislators to formally grant intellectual property protections to software creators. Later, a different kind of scarcity—a lack of access to source code—led the founders of the free and open source software movement to flip intellectual property protections on their head in an effort to better promote abundance. That movement proved wildly successful, with today's software industry based on vast amounts of freely available open source software resources that both organizations and individuals collaboratively build. Abundance and scarcity will also define software's future, but in different ways. The abundance that the open source software movement spawned is in the midst of a significant commercial phase. That sometimes means that commercial competitors bring to the table a scarcity mindset that conflicts with the norms that made that movement so successful. Intellectual property concerns at times derail what may otherwise be even greater software abundance. And because so much software is moving into the Cloud, trade secrecy may become the software industry's most important form of intellectual property to the extent the industry abandons open models of innovation. The software industry's growing dependence on artificial intelligence (AI) is likely to contribute to these trends. The software industry is increasingly becoming synonymous with the AI industry, as more and more software companies either rely on AI in running their services or provide AI products to the public. As with all software, these AI technologies are increasingly provided from the Cloud, where trade secrecy is not only possible, but often preferable. But trade secrecy may be even more likely in the AI context because much of the magic in implementing AI systems lies in the know-how to piece them together from available open source software resources, decades-old AI techniques, and data. Hence, to the extent that software and AI technologists spurn open innovation in favor of a scarcity mindset, trade secrecy is likely to become its dominant form of legal protection. The advent of web3 technologies may eventually change some of these trends. But for now, increasing secrecy seems the most likely outcome. I conclude by arguing that this shift to secrecy is likely preferable to other forms of intellectual property.

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored. The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare? A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice. Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships. There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships. In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.