Cross-country gaps in artificial intelligence: Factors explaining digital inequality

This study examines the factors influencing the adoption of Artificial Intelligence (AI) by enterprises across European countries, with a particular focus on the role of digital infrastructure and human capital. Using a linear regression model, the analysis explores the relationship between AI adoption and several key indicators, including the Digital Economy and Society Index (DESI), Human Development Index (HDI), Global Innovation Index (GII), Technology Readiness Index (TRI), and GDP per capita. The results reveal that DESI and HDI are the most significant drivers of AI adoption, highlighting the importance of digital ecosystems and educated workforces in facilitating AI integration. The model explains 64.9% of the variance in AI adoption, with DESI contributing to a 0.26% increase in AI adoption for every unit of improvement. HDI, representing the quality of human capital, plays an even larger role, suggesting that countries with higher levels of education and social development are better equipped to integrate AI into their industries. While innovation and technological readiness contribute to AI adoption, their effects are less pronounced compared to infrastructure and workforce readiness. GDP per capita, though positive, has only a marginal impact on AI adoption, indicating that economic strength alone does not guarantee widespread use of AI technologies. The study also provides a country-specific analysis, identifying Germany, Finland, and Ireland as leaders in AI adoption due to their strong digital and human resource foundations. Conversely, Bulgaria and Greece lag behind, primarily due to weaker digital infrastructure and lower levels of workforce readiness. Policy recommendations for these countries include targeted investments in digital infrastructure, education, and workforce training programs, as well as fostering public-private partnerships and supportive regulatory environments for AI development. The findings emphasize the critical role of digital infrastructure and human capital in driving AI adoption. Countries aiming to enhance their AI capabilities should focus on these key areas to remain competitive in the rapidly evolving global economy. JEL: A11, C45, O33, O52

The article is devoted to the history of the development of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), their current and probable future achievements, and the problems (which have already arisen, but will become even more acute in the future) associated with the development of these technologies and their active introduction in society. The close connection between the development of AI and cognitive science, the penetration of ICT and AI into various fields, in particular the field of health care, is shown. A significant part of the article is devoted to the analysis of the concept of ‘artificial intelligence’, including the definition of generative AI. We analyze recent achievements in the field of Artificial Intelligence, describe the basic models, in particular the Large Linguistic Models (LLM), and forecast the development of AI and the dangers that await us in the coming decades. We identify the forces behind the aspiration to create artificial intelligence, which is increasingly approaching the capabilities of the so-called general/universal AI, and also suggest desirable measures to limit and channel the development of artificial intelligence. The authors emphasize that the threats and dangers of the development of ICT and AI are particularly aggravated by the monopolization of their development by the state, intelligence services, large corporations and those often referred to as globalists. The article forecasts the development of computers, ICT and AI in the coming decades, and also shows the changes in society that will be associated with them. The study consists of two articles. The first, presented below, provides a brief historical overview and characterizes the current situation in the field of ICT and AI, it also analyzes the concepts of artificial intelligence, including generative AI, changes in the understanding of AI related to the emergence of the so-called large language models and related new types of AI programs (ChatGPT). The article discusses the serious problems and dangers associated with the rapid and uncontrolled development of artificial intelligence. The second article, to be published in the next issue of the journal, describes and comments on current assessments of breakthroughs in the field of AI, analyzes various forecasts, and the authors give their own assessments and forecasts of future developments. Particular attention is given to the problems and dangers associated with the rapid and uncontrolled development of AI, the fact that achievements in the field of AI are becoming a powerful means of controlling the population, imposing ideology and choice, influencing the results of elections, and a weapon for undermining security and geopolitical struggle.

Artificial intelligence is defined as the totality of systems and programs that imitate human intelligence and can eventually surpass this intelligence over time. The rapid development of these technologies has raised various ethical debates such as moral responsibility, privacy, bias, respect for human rights, and social impacts. This study examines the technical infrastructure of artificial intelligence, the differences between weak and strong artificial intelligence, ethical issues, and theological dimensions in detail, providing a comprehensive perspective on the role of artificial intelligence in human life and the problems it brings. The historical development of artificial intelligence has been shaped by the contributions of various disciplines such as mathematical logic, cognitive science, philosophy, and engineering. From the ancient Greek philosophers to the present day, thoughts on artificial intelligence have raised deep philosophical questions such as human nature, consciousness, and responsibility. The algorithms developed by Alan Turing have contributed to the modern shaping of artificial intelligence and have put forward the first models to assess whether machines have human-like intelligence, such as the “Turing Test”. The study first analyzes the technical infrastructure of artificial intelligence in detail and discusses the current limits and potential of the technology through the distinction between weak and strong artificial intelligence. Weak artificial intelligence includes systems designed to perform specific tasks and do not exhibit general intelligence outside of those tasks, while strong artificial intelligence refers to systems with human-like general intelligence and flexible thinking capacity. Most of the widely used artificial intelligence applications today fall into the category of weak artificial intelligence. However, the development of strong artificial intelligence brings various ethical and theological consequences for humanity. The ethical issues of artificial intelligence include fundamental topics such as autonomy, responsibility, transparency, fairness, and privacy. The decision-making processes of autonomous systems raise serious ethical questions at the societal level. Especially autonomous weapons and artificial intelligence-managed justice systems raise concerns in terms of human rights and individual freedoms. In this context, the ethical framework of artificial intelligence has deep impacts on the future of humanity and human-machine interaction, not just limited to technological boundaries. From a theological perspective, the ability of artificial intelligence to imitate the human mind and creative processes raises deep theological issues such as the creativity of God, the place of human beings in the universe, and consciousness. The questions of whether artificial intelligence systems can gain consciousness and whether these conscious systems can have a spiritual status have led to new debates in theology and philosophy. The ethical principles of artificial intelligence are shaped around principles such as transparency, accountability, autonomy, human control, and data management. In conclusion, determining the ethical and theological principles that need to be considered in the development and application of artificial intelligence is critical for the future of humanity. A comprehensive examination of the ethical and theological dimensions of artificial intelligence technologies is necessary to understand and manage the social impacts of this technology. This study emphasizes the necessity of an interdisciplinary approach for the development of artificial intelligence in harmony with social values and for the benefit of humanity. The study provides an important theoretical framework for future research by shedding light on the complex ethical and theological issues arising from the development and widespread use of artificial intelligence.

The article is devoted to the history of development of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), their current and probable future achievements and the problems (which have already arisen, but will become even more acute in the future) associated with the development of these technologies and their active introduction in society. The close connection between the development of AI and cognitive science, the penetration of ICT and AI into various fields, in particular the field of health care, is shown. A significant part of the article is devoted to the analysis of the concept of “artificial intelligence”, including the definition of generative AI. There is performed the analysis of recent achievements in the field of Artificial Intelligence, and there are given descriptions of the basic models, in particular Large Linguistic Models (LLM), and forecasts of the development of AI and the dangers that will await us in the coming decades. We identify the forces behind the aspiration to create artificial intelligence, which is increasingly approaching the capabilities of the so-called general/universal AI, and also suggest desirable measures to limit and channel the development of artificial intelligence. The authors emphasize that the threats and dangers of the development of ICT and AI are partuclarly aggrevated by the monopolization of their development by the state, intelligence services, major corporations and those often referred to as globalists. The article forecasts the development of computers, ICT and AI in the coming decades, and also shows the changes in society that will be associated with them. The study consists of two articles. The first, presented below, provides a brief historical overview and characterizes the current situation in the field of ICT and AI, it also analyzes the concepts of artificial intelligence, including generative AI, changes in the understanding of AI in connection with the emergence of the so-called large language models and related new types of AI programs (ChatGPT). The article discusses the serious problems and dangers associated with the rapid and uncontrolled development of artificial intelligence. The second article, to be published in the next issue of the journal, describes and comments on current assessments of breakthroughs in the field of AI, analyzes various forecasts, and the authors give their own assessments and forecasts of future developments. Particular attention is given to the problems and dangers associated with the rapid and uncontrolled development of AI, the fact that achievements in the field of AI are becoming a powerful means of control over the population, imposing ideology and choice, influencing the results of elections, and a weapon for undermining security and geopolitical struggle.

This study examined the role of Artificial Intelligence (AI) as a catalyst for economic and financial development in emerging Asian economies using a dual-method framework that combined econometric estimation with machine-learning prediction. Drawing on longitudinal data from 2000 to 2024, the research analyzed how AI adoption—measured through innovation intensity, patents, digital infrastructure, and investment—shaped GDP growth, total factor productivity, and financial inclusion. Panel cointegration results confirmed stable long-run relationships between AI and key macroeconomic indicators, while FMOLS and DOLS estimations demonstrated that AI adoption exerted strong and positive long-run effects on growth, productivity, and digital financial access. Granger causality tests indicated bidirectional causality between AI and financial development, highlighting the centrality of fintech-enabled inclusion channels. Complementing econometric results, machine-learning models (Random Forest, Gradient Boosting, LSTM) revealed high predictive accuracy, with LSTM emerging as the strongest performer (R² = 0.89). Feature-importance analysis showed that digital infrastructure, fintech usage, and institutional quality were the most influential predictors of economic outcomes. The findings suggested that AI reshaped development pathways by enhancing forecasting precision, improving decision-making efficiency, and enabling broader access to financial services. However, disparities in digital readiness and governance limited the uniform diffusion of benefits across countries. The study concluded that AI represents a transformative driver of structural growth in emerging Asia, provided that complementary investments in digital infrastructure, regulatory modernization, and human capital development are strengthened.

The whirlwind advent of ChatGPT in 2022 has marked a new age of artificial intelligence (AI), the general name for the technology that combines computer technology, big data bases and machines. This AI technology quickly makes its presence felt with hundreds of popular programs and chatbots such as the portrait-making AI diffusion art and the thesis-writing ChatGPT. This paper investigates the conceptual metaphors representing AI and AI development in The Guardian, a UK-based newspaper, to figure out how this technology and its growth have been introduced to ordinary people via mass media. Employing the Conceptual Metaphor Theory proposed by Lakoff & Johnson (1980), this study found three AI-related conceptual metaphors, namely, AI IS A HUMAN BEING, AI IS AN ANIMAL and AI IS A NATURAL FORCE, which are realized by more than 100 linguistic expressions across 33 news articles. Also, this research found five conceptual metaphors related to AI development, namely AI DEVELOPMENT IS WAR, AI DEVELOPMENT IS A RACE, AI DEVELOPMENT IS A CONVERSATION, AI DEVELOPMENT IS A DANCE, AI DEVELOPMENT IS A GAME and these metaphors are manifested by approximately 40 linguistic expressions. This paper discusses the way that these metaphors could influence the way people and technology companies think about AI and AI development.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

This paper examines the trajectory of artificial intelligence (AI) development, focusing on three key stages: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI). Recent advancements in AI architectures, particularly the evolution of transformer-based models, have significantly accelerated progress across these stages, enabling more sophisticated and scalable AI systems. This paper explores the architectural foundations of ANI, AGI, and ASI, highlighting recent modifications and their implications for future AI development. Additionally, the societal, ethical, and geopolitical implications of AI are discussed, emphasizing the need for robust safeguards and governance frameworks to ensure that AI serves as a force for human advancement rather than a source of existential risk. By integrating historical comparisons, current trends, and future projections, this paper provides a comprehensive analysis of the transformative potential of AI and its impact on humanity.

This paper investigates the relationship between artificial intelligence (AI) adoption and economic growth across a global panel of sixty countries from 2015 to 2024. Using indicators such as AI adoption intensity, digital infrastructure, human capital, research and development (R&D) expenditure, and foreign direct investment (FDI), the study employs a fixed-effects regression framework to control for unobserved heterogeneity. The results reveal that AI adoption and digital infrastructure significantly enhance GDP growth, with human capital acting as a strong mediating factor. Although R&D and FDI contribute positively, their effects are less pronounced in developing economies. The findings underscore the importance of complementary investments in digital skills and infrastructure to fully capture the benefits of AI technologies. Policy recommendations include fostering AI capacity-building programs, expanding broadband connectivity, and promoting ethical and inclusive AI diffusion.

PurposeThis study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).Design/methodology/approachThe global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).FindingsThe key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.Research limitations/implicationsResistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.Practical implicationsRevision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.Originality/valueThe novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

The article considers a qualitatively new stage in the development of artificial intelligence (AI), associated with the development of artificial general intelligence (abbreviated as AGI in the international nomenclature – from Artificial General Intelligence). Unlike traditional AI, AGI is significantly closer in its functions to natural intelligence (EI), it will be able to self-learn, solve a wide range of tasks in different environments, i.e. be integral and autonomous. Such a level of “independence” of AGI opens up fundamentally new prospects for the development of information technologies, but at the same time poses many acute socio-humanitarian problems associated with the risks and threats of losing control over the development of AI. The successful development of AGI requires new theoretical and methodological approaches based on the principles of post-nonclassical epistemology and the results of neuroscientific and phenomenological studies of consciousness. It is very important to consider these issues from the angle of the extreme aggravation of the global crisis of world civilization, due to its consumer dominance and efforts to preserve its monopolar structure from the part of the United States and its Western allies. In this regard, a broader, philosophical-anthropological approach is also required to understand the current state of our civilization and the possibilities for its transformation. It involves taking into account what is called the nature of man, as a stable complex of his mental and bodily properties. They were reproduced among all peoples, in all historical epochs, under all social structures, which indicates their biological conditionality. Among them, along with altruistic properties, a number of negative properties can be distinguished (such as unlimited consumerism, aggressiveness towards one’s own kind, excessive egoistic self-will). These characteristic properties of mass consciousness were actively exploited adherents of monopolarity in their interests. Overcoming the principles and practices of monopolarity and thereby changing the global social self-organization is a necessary condition for a truly humanistic stage of anthropotechnological evolution, capable of opening up new existential prospects for the transformation of man and mankind.

The concept paper provides a detailed analysis of how strategic policy frameworks can facilitate the adoption and integration of artificial intelligence (AI) to drive economic and social development in Nigeria. This executive summary outlines the paper's key objectives, strategic frameworks, and anticipated outcomes, emphasizing the need for robust policies to harness the transformative power of AI. The primary objective of this paper is to identify and propose policy frameworks that can support the widespread adoption of AI across various sectors in Nigeria. It recognizes the potential of AI to revolutionize industries such as healthcare, agriculture, finance, and education, thereby significantly contributing to national development. The paper underscores the necessity for a structured approach to AI implementation, addressing the unique challenges and opportunities within the Nigerian context. Central to the paper is the exploration of policy frameworks that can facilitate AI adoption. It discusses the importance of establishing clear regulatory guidelines that ensure ethical AI use, protect data privacy, and promote transparency. The paper also highlights the need for policies that encourage investment in AI research and development, support startups and innovation hubs, and foster collaboration between the public and private sectors. The concept paper examines successful AI adoption models from other countries, drawing lessons that can be tailored to Nigeria's specific needs. It emphasizes the significance of creating a conducive environment for AI innovation, which includes investing in digital infrastructure, enhancing internet connectivity, and ensuring access to high-quality data. Moreover, it proposes the establishment of AI regulatory bodies to oversee the development and deployment of AI technologies, ensuring they align with national priorities and ethical standards. Addressing the practical challenges of AI adoption, the paper highlights issues such as the digital divide, lack of skilled workforce, and potential job displacement. It proposes strategies to overcome these challenges, including implementing educational reforms to incorporate AI and digital literacy into the curriculum, providing incentives for continuous professional development, and promoting AI awareness and literacy among the general population. The anticipated outcomes of implementing robust AI policy frameworks include improved efficiency and productivity across various sectors, enhanced service delivery, and the creation of new economic opportunities. These outcomes are expected to drive sustainable economic growth, improve the quality of life, and position Nigeria as a competitive player in the global AI landscape. The paper provides a comprehensive roadmap for integrating AI into the national development agenda. By establishing robust policies, investing in infrastructure, and fostering a culture of innovation, Nigeria can successfully leverage AI to achieve significant socio-economic progress. The paper calls for a collaborative effort from government, industry stakeholders, academia, and civil society to create an enabling environment for AI adoption and growth.

The article analyzes the rapid progress in the artificial intelligence sector as one of the most promising areas of information and communication technologies. There is observed an increase in the use of the generative artificial intelligence (GenAI) system in various business areas and a significant increase in investment. The authors focus on the need to implement GenAI in Ukrainian business. At the same time, they emphasize the emergence of negative consequences that arise from the development of artificial intelligence (AI), that in particular can be either accidental or malicious. The importance of risk management in the context of the use of GenAI for effective application in business is emphasized. An analysis of scientific publications in the field of artificial intelligence shows an increasing interest in understanding and analyzing the risks of the development and use of AI. The need for continuous monitoring and development of institutional frameworks for effective AI risk management is underlined, including integrating the efforts of all stakeholders and differentiating efforts at different stages of the development and use of AI. It is noted that the development and use of AI have probable negative consequences, which range from random to deliberately mixed. Sometimes the information generated by AI systems may not be accurate, and sometimes it is biased in nature based on gender, race, and other stereotypes and can be used to facilitate unethical or criminal activities. Some of the inherent risks of AI have already been explored, while others remain unknown. This situation necessitates systematic monitoring of the possible implications of AI development and adoption, as well as the development of appropriate institutional frameworks to assess progress in this area.

Digital transformation and utilization of artificial intelligence (AI) in the electric grid are fundamentally changing the industry’s approach to common problems and enabling a broader paradigm shift in grid planning and operations. The change in approach is circularly both enabling and driving modernization, with load growth and reliable management of data center and AI infrastructure shifting away from planning approaches with relatively predictable behaviors and toward a mix of consumer and industrial choices that surpass human cognitive abilities to process. This movement has potential to condition humans to not understand the system on which the AI depends, while requiring it for development of the necessary infrastructure. Approaches which would address most likely grid conditions and events, such as faults, aging of equipment, and weather, now must also account for large loads which shift not based upon weather or time of day, but the computational load. Quantifying computational load is independent of the traditional grid forecasting variables, where a data center’s aggregate load is determined by user and AI system behavior and decoupled from normal grid planning and operations. AI is both the cause and solution for these challenges, with new grid planning tools integrating massive amounts of decisions into frameworks. The race to dominate the AI field between the United States and China has significant positive and negative implications for both technological advancement and national security. Domination of the field is not an isolated function of AI technology; instead, the U.S. must grow its capacity to handle large AI-driven electronic loads, develop new models, and drive adoption. As America invests heavily in AI research and development, the country’s goal is to achieve breakthroughs that could redefine industries, economies, and military capabilities. However, the intense competition from adversarial nations also heightens security risks, including the potential for cyber espionage, intellectual property theft, and the misuse of AI technologies from the increased digital attack surface. Ensuring the responsible appropriate use and robust security of AI systems is paramount to preventing malicious exploitation and maintaining global stability. The U.S. must navigate this AI race carefully, balancing innovation with international cooperation to address critical security concerns, specifically when it comes to the integration of AI with critical functions such as the electrical power grid. The electric grid is evolving into a complex cyber-physical system where AI is increasingly applied to enhance monitoring, control, and optimization. Grid operators are already using AI to monitor transmission lines and isolate faults, and to predict fluctuations in electricity supply and demand. As they look to grow and thrive in this new reality, utilities must also consider their approach to a secure and responsible use of the load on which the AI is driven along with the AI itself. Many AI applications, will be integrated into these tools, along with new requirements to implement cloud computing into operational technology (OT) operations, which also increases utility use of data centers that house cloud computing platforms. Therefore, risk frameworks must also address these interdependent concerns to enable their responsible and secure use as rapidly as AI adoption. Energy vendors and service providers are racing to deploy AI within their tools, products, and services to demonstrate their acceptance and enriched modernization capabilities. Though acting fast (and even failing fast) are qualities needed to help win the AI race, this comes at a cost to early adopters of these technologies, who will bear the burden of poor implementations, evolving models, and nascent application spaces. When these applications are in critical infrastructure sectors like the grid, the costs of these challenges could be significant (e.g., safety or reliability impacts). This paper focuses on key aspects of AI adoption in the electric energy sector through three modalities: AI model integration inside the organization, purchase of commercial tools with AI integrated, and edge device integration. The authors summarize key risk-informed insights for AI use in electric grid operations and planning, which includes a review of the current use cases for the electric grid and a simple analysis framework to understand AI impact, coupled with application decomposition and consequence-driven analyses of the application’s security and impact and in which case security controls can be applied. By doing this style of analysis, risk-informed controls can be placed around technologies, enabling their use and reducing the risk of the highest consequence repercussions. Like cloud data models, AI introduces new shared risk boundaries among utilities, vendors, regulators, AI developers, maintainers and support. Similar to other d

The development of artificial intelligence (AI) is progressing through stages: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial super intelligence (ASI). This article aims to map out recent literature in Islamic philosophy which discusses and explores AI with respect to those divisions. In other words, this is a baseline study on the potential discourse of AI within the various schools of Islamic philosophy such as masha’ī (peripatetic), ishraqī (illuminationist), and sadranī (transcendental). Our inquiry concerns with how do contemporary scholars in Islamic philosophy give response to the recent development of AI? We seek various open access English references which discuss AI and Islamic philosophy, and we discover 19 English references published in between 2014-2024. We take the initiative to broaden our investigation to include 12 Arabic references. Although classical Islamic philosophy contains a significant number of discussions on intellect and mind, this has not been sufficient to attract more research on AI and Islamic philosophy. Therefore, we present and identify some questions to stimulate further academic research on AI within Islamic philosophy.