Artificial Intelligence Research Research Articles

Arms Race or Innovation Race? Geopolitical AI Development

ABSTRACT China, the United States, and the European Union have spoken of a global competition surrounding Artificial Intelligence (AI). There is widespread talk of an ‘AI Arms Race’. But what is the nature of this race? We argue that the arms race metaphor does not capture the dynamics of global competition in the AI sector. Instead, we propose the notion of a ‘geopolitical innovation race’ for technological leadership in a networked global economy. Based on an analysis of government documents, we find that actors (1) are open to both zero-sum and positive-sum approaches in AI development, (2) organise actor networks differently based on national innovation cultures, (3) prioritise economics and status next to security concerns, and (4) are open to how AI should be interpreted. Referring to the competitive race of AI research and development, the three technopoles perpetuate the geopoliticisation of innovation and intertwine security and economic interests.

Design and Psychometric Evaluation of the Artificial Intelligence Acceptance and Usage in Research Creativity Scale Among Faculty Members: Insights From the Network Analysis Perspective

ABSTRACTThe acceptance of artificial intelligence (AI) in academic settings, particularly in the context of research creativity, is a growing area of interest. This study aimed to design and validate the AI Acceptance and Research Creativity Scale (AIA&RCS) among faculty members. This exploratory mixed‐method was conducted among 720 faculty members. A literature review and participant interviews were conducted in the qualitative phase to generate and develop items. In the quantitative phase, face validity, content validity, construct validity, convergent validity and reliability (internal consistency and stability) were used. Exploratory factor analysis (EFA) indicated a 4‐factor model of the scale with ‘perceived usefulness and effectiveness of AI in research creativity’, ‘ethical issues in research’, ‘trusted in AI capabilities’ and ‘willingness to use AI’ accounting for 51.6% of the variance. This arrangement was verified by confirmatory factor analysis (CFA), with fit indices that were at suitable levels. Then, the network analysis took into account the four‐factor structure of AIA&RCS further. Similarly, the exploratory graph analysis (EGA) indicated the four‐factor configuration of the AIA&RCS. The 25‐item scale is well‐suited for measuring AI acceptance and research innovation among faculty because of its psychometrics.

“Everybody knows what a pothole is”: representations of work and intelligence in AI practice and governance

Abstract In this paper, we empirically and conceptually examine how distributed human–machine networks of labour comprise a form of underlying intelligence within Artificial Intelligence (AI), considering the implications of this for Responsible Artificial Intelligence (R-AI) innovation. R-AI aims to guide AI research, development and deployment in line with certain normative principles, for example fairness, privacy, and explainability; notions implicitly shaped by comparisons of AI with individualised notions of human intelligence. However, as critical scholarship on AI demonstrates, this is a limited framing of the nature of intelligence, both of humans and AI. Furthermore, it dismisses the skills and labour central to developing AI systems, involving a distributed network of human-directed practices and reasoning. We argue that inequities in the agency and recognition of different types of practitioners across these networks of AI development have implications beyond RAI, with narrow framings concealing considerations which are important within broader discussions of AI intelligence. Drawing from interactive workshops conducted with AI practitioners, we explore practices of data acquisition, cleaning, and annotation, as the point where practitioners interface with domain experts and data annotators. Despite forming a crucial part of AI design and development, this type of data work is frequently framed as a tedious, unskilled, and low-value process. In exploring these practices, we examine the political role of the epistemic framings that underpin AI development and how these framings can shape understandings of distributed intelligence, labour practices, and annotators’ agency within data structures. Finally, we reflect on the implications of our findings for developing more participatory and equitable approaches to machine learning applications in the service of R-AI.

Intelligent Cinematography: a review of AI research for cinematographic production

This paper offers the first comprehensive review of artificial intelligence (AI) research in the context of real camera content acquisition for entertainment purposes and is aimed at both researchers and cinematographers. Addressing the lack of review papers in the field of intelligent cinematography (IC) and the breadth of related computer vision research, we present a holistic view of the IC landscape while providing technical insight, important for experts across disciplines. We provide technical background on generative AI, object detection, automated camera calibration and 3-D content acquisition, with references to assist non-technical readers. The application sections categorize work in terms of four production types: General Production, Virtual Production, Live Production and Aerial Production. Within each application section, we (1) sub-classify work according to research topic and (2) describe the trends and challenges relevant to each type of production. In the final chapter, we address the greater scope of IC research and summarize the significant potential of this area to influence the creative industries sector. We suggest that work relating to virtual production has the greatest potential to impact other mediums of production, driven by the growing interest in LED volumes/stages for in-camera virtual effects (ICVFX) and automated 3-D capture for virtual modeling of real world scenes and actors. We also address ethical and legal concerns regarding the use of creative AI that impact on artists, actors, technologists and the general public.

Masked autoencoder of multi-scale convolution strategy combined with knowledge distillation for facial beauty prediction

Facial beauty prediction (FBP) is a leading area of research in artificial intelligence. Currently, there is a small amount of labeled data and a large amount of unlabeled data in the FBP database. The features extracted by the model based on supervised training are limited, resulting in low prediction accuracy. Masked autoencoder (MAE) is a self-supervised learning method that outperforms supervised learning methods without relying on large-scale databases. The MAE can improve the feature extraction ability of the model effectively. The multi-scale convolution strategy can expand the receptive field and combine the attention mechanism of the MAE to capture the dependency between distant pixels and acquire shallow and deep image features. Knowledge distillation can take the abundant knowledge from the teacher net to the student net, reduce the number of parameters, and compress the model. In this paper, the MAE of the multi-scale convolution strategy is combined with knowledge distillation for FBP. First, the MAE model with a multi-scale convolution strategy is constructed and used in the teacher net for pretraining. Second, the MAE model is constructed for the student net. Finally, the teacher net performs knowledge distillation, and the student net receives the loss function transmitted from the teacher net for optimization. The experimental results show that the proposed method outperforms other methods on the FBP task, improves FBP accuracy, and can be widely applied in tasks such as image classification.

A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals

This study analyzes artificial intelligence (AI) research in communication scholarship through a content analysis of published articles between 2006 and 2022. It aims to understand the status of AI research between 2006 and 2022 and identify directions for future inquiry. Findings indicate that the number of articles about AI has increased over the years and scholars should continue applying existing theoretical frameworks or proposing new ones to investigate diverse topics across cultural and sociopolitical contexts.

SAT-GATv2: A Dynamic Attention-Based Graph Neural Network for Solving Boolean Satisfiability Problem

We propose SAT-GATv2, a graph neural network (GNN)-based model designed to solve the Boolean satisfiability problem (SAT) through graph-based deep learning techniques. SAT-GATv2 transforms SAT formulas into graph structures, leveraging message-passing neural networks (MPNNs) to propagate local information and dynamic attention mechanisms (GATv2) to accurately capture inter-node dependencies and enhance node feature representations. Unlike traditional heuristic-driven SAT solvers, SAT-GATv2 adopts a data-driven approach, learning structural patterns directly from graph representations and providing a complementary framework to existing methods. Experimental results demonstrate that SAT-GATv2 achieves an accuracy improvement of 1.75–5.51% over NeuroSAT on challenging random 3-SAT(n) instances, highlighting its effectiveness in handling difficult problem distributions, and outperforms other GNN-based models on SR(n) datasets, showcasing its scalability and adaptability. Ablation studies validate the critical roles of MPNNs and GATv2 in improving prediction accuracy and scalability. While SAT-GATv2 does not yet surpass CDCL-based solvers in overall performance, it addresses their limitations in scalability and adaptability to complex instances, offering an efficient graph-based alternative for tackling larger and more complex SAT problems. This study establishes a foundation for integrating deep learning with combinatorial optimization, emphasizing its potential for applications in artificial intelligence and operations research.

Artificial Intelligence In Health And Health Care: Priorities For Action.

The field of artificial intelligence (AI) has entered a new cycle of intense opportunity, fueled by advances in deep learning, including generative AI. Applications of recent advances affect many aspects of everyday life, yet nowhere is it more important to use this technology safely, effectively, and equitably than in health and health care. Here, as part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2025 initiative, which is designed to provide guidance on pressing health care issues for the incoming presidential administration, we describe the steps needed to achieve these goals. We focus on four strategic areas: ensuring safe, effective, and trustworthy use of AI; promotion and development of an AI-competent health care workforce; investing in AI research to support the science, practice, and delivery of health and health care; and promotion of policies and procedures to clarify AI liability and responsibilities.

Development of secure infrastructure for advancing generative artificial intelligence research in healthcare at an academic medical center.

Generative AI, particularly large language models (LLMs), holds great potential for improving patient care and operational efficiency in healthcare. However, the use of LLMs is complicated by regulatory concerns around data security and patient privacy. This study aimed to develop and evaluate a secure infrastructure that allows researchers to safely leverage LLMs in healthcare while ensuring HIPAA compliance and promoting equitable AI. We implemented a private Azure OpenAI Studio deployment with secure API-enabled endpoints for researchers. Two use cases were explored, detecting falls from electronic health records (EHR) notes and evaluating bias in mental health prediction using fairness-aware prompts. The framework provided secure, HIPAA-compliant API access to LLMs, allowing researchers to handle sensitive data safely. Both use cases highlighted the secure infrastructure's capacity to protect sensitive patient data while supporting innovation. This centralized platform presents a scalable, secure, and HIPAA-compliant solution for healthcare institutions aiming to integrate LLMs into clinical research.

Artificial Intelligence in Energy Economics Research: A Bibliometric Review

Artificial intelligence (AI) is gaining attention in energy economics due to its ability to process large-scale data as well as to make non-linear predictions and is providing new development opportunities and research subjects for energy economics research. The aim of this paper is to explore the trends in the application of AI in energy economics over the decade spanning 2014–2024 through a systematic literature review, bibliometrics, and network analysis. The analysis of the literature shows that the prominent research themes are energy price forecasting, AI innovations in energy systems, socio-economic impacts, energy transition, and climate change. Potential future research directions include energy supply-chain resilience and security, social acceptance and public participation, economic inequality and the technology gap, automated methods for energy policy assessment, the circular economy, and the digital economy. This innovative study contributes to a systematic understanding of AI and energy economics research from the perspective of bibliometrics and inspires researchers to think comprehensively about the research challenges and hotspots.

Evolution of artificial intelligence in healthcare: a 30-year bibliometric study

IntroductionIn recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. Concurrently, the integration of artificial intelligence with the medical field has garnered increasing attention from medical experts. This study undertakes a dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades to investigate the current status and trends of the fusion between medicine and artificial intelligence.MethodsFollowing a search on the Web of Science, researchers retrieved all reviews and original articles concerning artificial intelligence in healthcare published between January 1993 and December 2023. The analysis employed Bibliometrix, Biblioshiny, and Microsoft Excel, incorporating the bibliometrix R package for data mining and analysis, and visualized the observed trends in bibliometrics.ResultsA total of 22,950 documents were collected in this study. From 1993 to 2023, there was a discernible upward trajectory in scientific output within bibliometrics. The United States and China emerged as primary contributors to medical artificial intelligence research, with Harvard University leading in publication volume among institutions. Notably, the rapid expansion of emerging topics such as COVID-19 and new drug discovery in recent years is noteworthy. Furthermore, the top five most cited papers in 2023 were all pertinent to the theme of ChatGPT.ConclusionThis study reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine. Additionally, medical artificial intelligence research is dynamically evolving with the advent of new technologies. Moving forward, concerted efforts to bolster international collaboration and enhance comprehension and utilization of AI technologies are imperative for fostering novel innovations in healthcare.

AI, Digital Trade, and Data Protection: Overcoming Regulatory Fragmentation for a Multilateral Regulatory Framework

Artificial Intelligence (AI), digital trade, and data-driven technologies have significantly transformed global trade and the world economy, shifting from a physical economy to a digital economy. However, the existing norms, led by the World Trade Organization (WTO) three conventional trade agreements, have fallen behind this reality. States and governments are still bound by these outdated norms while seeking new regulatory frameworks for digital trade and emerging technologies, particularly AI. This article, through a comprehensive comparison of academic literature, domestic laws, and strategic policies, highlights the varied approaches of the international community: the European Union (EU) emphasizes human-concentric regulation, the United States prioritizes AI research and development over unified legislation, and China strikes a balance between conservative regulation and AI innovation. Furthermore, the article examines efforts like the emergence of AI regulatory sandboxes, the United Nations (UN) draft resolution on AI for sustainable development, and the first binding AI convention, which indicate progress towards multilateral AI regulation.

Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!

Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging. A total of 49 responses were obtained and analysed to identify trends and patterns. While TGs valued transparency and traceability the most, CGs pointed out the importance of explainability. Among the topics where TGs may benefit from further exposure are stability and robustness checks, and mitigation of fairness issues. On the other hand, CGs seemed more reluctant towards synthetic data for validation and would benefit from exposure to cross-validation techniques, or segmentation metrics. Topics emerging from the open questions were utility, capability, adoption and trustworthiness. These findings on current trends in AI validation strategies may guide the creation of guidelines necessary for training the next generation of professionals working with AI in healthcare and contribute to bridging any technical-clinical gap in AI validation. RELEVANCE STATEMENT: This study recognised current gaps in understanding and applying AI validation strategies in cancer imaging and helped promote trust and adoption for interdisciplinary teams of technical and clinical researchers. KEY POINTS: Clinical and technical researchers emphasise interpretability, externalvalidation with diverse data, and biasawarenessin AI validation for cancer imaging. In cancer imaging AI research, clinical researchers prioritise explainability, whiletechnical researchers focus ontransparency and traceability, and see potential insynthetic datasets. Researchers advocate for greater homogenisation of AI validation practices in cancer imaging.

Intelligence is not deception: from the Turing test to community-based ascriptions

Abstract The Turing test has a peculiar status in the artificial intelligence (AI) research community. On the one hand, it is presented as an important topic in virtually every AI textbook, and the research direction focused on developing AI systems that behave in human-like fashion is standardly called the “Turing test approach”. On the other hand, reports of computer programs passing the Turing test have had relatively little effect. Does this mean that the Turing test is no longer relevant as a test, doomed to be a theoretical notion with little connection to AI practice? In this paper, I argue that there is one problem in particular with common traditional versions of the Turing test, namely their focus on deception. The criterion for passing the Turing test is standardly connected to an AI system’s ability to deceive the interrogator about its identity. But why should we connect intelligence to the ability deceive? Here I present a revised version of an intelligence test that is not based on deception. In what I call the Community-based intelligence test (CBIT), an AI is introduced to a community of human subjects. If after a sufficient number of interactions within that community the humans are not able to identify the AI system as a computer, it is considered to have passed CBIT. I discuss whether that should be enough to ascribe intelligence to the AI, and if not, what more would be needed?

Artificial Intelligence as a Catalyst for Management System Adaptability, Agility and Resilience: Mapping the Research Agenda

Artificial intelligence (AI) is an increasingly notable presence in society, industries, and organizations, making its necessity felt more in managerial decisions and practices. This paper aims to outline the importance of the topic related to the increase in the adaptability, agility, and resilience of the management system as a result of AI integration, resorting to a bibliometric type of research. A total of 107 papers from the period 2007–2024 exported from the Web of Science Core Collection database were analyzed, with support of Biblioshiny software. This topic is proving to be one of heightened global interest, being comprehensively addressed by world leaders in AI research and technologies such as the United States, China, Great Britain, France, India, and beyond. Collaborative relationships established between geographic regions are captured, noting the power and expansion of the theme on all continents of the globe. Likewise, its thematic and strategic evolution is characterized as a surprising one, managing to incorporate and relate concepts with a strong technical and IT character such as feature extraction, machine learning, reinforcement learning with concepts of a managerial nature as supporting customer-tailored interaction, employee skills development, company productivity, and innovation.

Artificial intelligence versus human researcher performance for systematic literature searches: a study focusing on the surgical management of base of thumb arthritis

Aim: In the digital age, artificial intelligence (AI) platforms have gradually replaced traditional manual techniques for information retrieval. However, their effectiveness in conducting academic literature searches remains unclear, necessitating a comparative assessment. This study examined the efficacy of AI search engines (Elicit, Consensus, ChatGPT) vs. manual search for literature retrieval, focusing on the surgical management of trapeziometacarpal osteoarthritis. Methods: The study was executed per the Cochrane Handbook for Systematic Reviews and PRISMA guidelines. AI platforms were given relevant keywords and prompts, while manual searches used PubMed, Cochrane CENTRAL, Web of Science, and Scopus databases from January 1901 to April 2024. The study focused on English-language randomized controlled trials (RCTs) comparing surgical management of trapeziometacarpal osteoarthritis (TMCJ OA). Two independent evaluators screened and extracted data from the studies. Primary outcomes involved the quality and relevancy of studies chosen by both search methods, evaluated by false positive rates and number of studies, including outcomes of interest. Results: The manual search yielded the most results (6,018), followed by Elicit (4,980), Consensus (3,436), and ChatGPT (6). Elicit identified the highest number of RCTs (205) but also had the greatest false positive rate (94%). Ultimately, the manual search identified 23 suitable studies, Elicit found 10, Consensus found 9, and ChatGPT identified only 1. No additional studies were found by AI search engines that were not discovered in the manual search. Conclusion: The findings highlight the potential advantages and drawbacks of AI search engines for literature searches. While Elicit was prone to error, Consensus and ChatGPT were less comprehensive. Significant enhancements in the precision and thoroughness of AI search engines are required before they can be effectively utilized in academia.

The Impact on International Law and Response Strategies of Artificial Intelligence Technology Development in the Context of Digital Economy: Taking WTO as a Research Perspective

The rapid development of digital technology has made international interactions more convenient and frequent, and nowadays, the rapid change and development of artificial intelligence has brought unprecedented challenges to the international law. The international community has put forward an urgent demand for the improvement and development of the international law system. On the one hand, the wide application of AI in international trade has had a great impact and influence on the global value management; on the other hand, the development of AI has put forward new challenges to the relevant rules of the WTO, which include, but are not limited to, whether the existing World Trade Organisation (WTO) treaties are applicable to AI in the context of the digital economy, such as the GATT, GATS; whether existing WTO rules are formulated with the application of AI, such as GATT, GATS; whether emerging technologies are taken into account in the formulation of existing WTO rules; and the challenge of AI to the existing legal system system. The World Trade Organisation (WTO), as the most important intergovernmental trade organisation in the world and the international community today, still plays an important role in regulating and deploying the development of AI technologies in the international trading system. Based on this, this paper takes the WTO as the research perspective to explore the impact of the digital economy and the development of artificial intelligence technology on international law, combining the existing rules system of the WTO and the characteristics of the development of artificial intelligence technology, research and argumentation to deal with the impact of the strategy, with a view to guaranteeing better development of artificial intelligence technology for the WTO, better service to mankind to offer advice.

Driving Business Growth and Market Expansion: AI and Market Research Strategies in Financial Institutions and SMEs

This paper explores the integration of artificial intelligence (AI) and market research strategies to drive business growth and market expansion in financial institutions and small and medium-sized enterprises (SMEs). It highlights the significance of AI technologies in enhancing data analysis, generating customer insights, and predicting market trends, which are crucial for informed decision-making. Specific AI and market research strategies tailored for financial institutions are discussed, demonstrating how these tools can improve decision-making, customer segmentation, and personalized marketing. The paper also delves into the benefits of AI and market research for SMEs, emphasizing cost-efficiency, market penetration, and competitive advantage. Despite SMEs’ challenges in adopting AI, such as limited resources and technical expertise, practical solutions like AI-as-a-Service platforms and strategic partnerships are proposed. The paper concludes with recommendations for financial institutions and SMEs to effectively leverage AI and market research, focusing on investment in AI technologies, workforce training, robust data security measures, and continuous monitoring and evaluation of AI systems. By embracing these strategies, financial institutions and SMEs can achieve sustainable business growth and maintain a competitive edge in the digital economy.

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science.

Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.

Artificial Intelligence in Urban Traffic Noise Prediction: A Systematic Review

The present work discusses the traffic noise prediction model based on Artificial Intelligence using Artificial Neural Networks (ANN), AI robot technology, and Intelligent Transportation Systems (ITS). This survey paper used a methodical review of the body of research on traffic prediction, with a particular emphasis on highlighting the most recent developments and untapped research opportunities in AI-based traffic prediction techniques which were used to look up papers in the IEEE Xplore, Web of Science, ACM Digital Library, and Scopus technology libraries. Taking into consideration a variety of input variables, such as traffic volumes, speed, and road conditions, these models are mainly designed with the goal of estimating road traffic noise levels. When compared to analytical techniques, machine learning algorithms have demonstrated good performance when used to model and predict a variety of variables. Artificial intelligence-based models have outperformed traditional and empirical models in traffic noise prediction due to its adaptive nature and capacity to manage non-linear properties. As a result, in contrast to nations with well-established empirical models, the application of AI-based methodologies provides an alternate strategy in nations with distinct traffic compositions and features. With the review, the explosive expansion of artificial intelligence (AI) offers potential for previously unheard-of productivity and creativity in controlling and organizing urban procedures and to serve as a solid resource for future urban traffic prediction using artificial intelligence (AI) research as well as an appropriate resource for readers to catch up on the state of the art rapidly.