Artificial Intelligence Theory Research Articles

Implications of using AI in Translation Studies: Trends, Challenges, and Future Direction

This review paper provides an overview of the use of artificial intelligence (AI) in translation studies. The paper discusses the various AI techniques that have been used in translation, including statistical machine translation, rule-based machine translation, neural machine translation, and hybrid machine translation. The paper also explores the advantages and limitations of each model, as well as their applications in translation studies. Additionally, the paper reviews the various techniques for evaluating the effectiveness of AI models in translation and their advantages and limitations. The challenges and limitations of AI in translation, such as the handling of idioms, metaphors, and cultural nuances, are also discussed, along with research directions for improving AI-based translation. The review also discusses the representation of AI in literature and the arts, delves into the academic opportunities, and investigates its impact on human lives. Furthermore, AI's ethical and social implications in translation, such as job displacement, data privacy, and bias and fairness, are examined. Finally, the paper summarizes the main findings, implications, and recommendations for future research directions in AI-based translation studies. Overall, this review paper provides some insights into the current state of AI in translation and its potential for improving the field.

Opportunities and challenges of integrating artificial intelligence in China's elderly care services

The challenge of elderly care presents a formidable task, demanding the collective attention of governmental bodies and diverse sectors of society. The integration of Artificial Intelligence (AI) into the research and development of Social Elderly Care Service (ECS) has emerged as a dominant trend, holding substantial importance in the establishment of an efficient ECS system. This study aims to serve as a comprehensive reference for the advancement of China's ECS system, achieved through the harmonious integration of a social ECS system with AI capabilities. This paper introduces the fundamental theory of AI, delving into the intricacies of the greyscale model of AI. Furthermore, it provides an overview of the current landscape of elderly care and elder care institutions, offering scientific data and insights to propel further research on AI development and system construction. Through an analysis of the existing research status, the study identifies prevalent issues within the AI-ECS integration, emphasizing pivotal factors influencing the construction of a robust social ECS system. To address these concerns, the study puts forth specific and viable policy recommendations. Notably, the questionnaire's statistics underscore that 83% of the elderly populace would opt for AI-driven solutions in selecting intelligent products, thereby underscoring the pivotal role of AI within the social ECS system. The challenges facing elderly care systems, including demographic shifts, resource constraints, and evolving societal norms, demand innovative solutions for providing efficient and effective care. This study addresses these challenges by exploring the integration of Artificial Intelligence (AI) into Social Elderly Care Services (ECS) in China. By delving into the theory of AI and assessing the existing research status, the study identifies key issues in AI-ECS integration and proposes viable policy recommendations. Insights from stakeholder surveys further highlight the importance of AI-driven solutions in meeting the needs of the elderly population.

A decolonial critical theory of artificial intelligence

In this paper, I argue for a normative reconstruction, from a decolonial perspective of critical theory in Brazil and Latin America, of a democratic ethos that despite its weaknesses and normative deficits is capable of fostering an increasingly deliberative, participatory, and egalitarian democracy by making extensive use of new digital technologies (comprising both AI systems and digital governance). Its argumentative core boils down to the promotion of intersectional egalitarianism (socio-economic, gender, racial-ethnic, environmental) through digital inclusion, which seems only feasible to us from a perspective capable of accommodating the normative claims of a critical decolonial theory combined with a naturalistic view of sustainability, within a research program that I dubbed “mitigated social constructionism” in response to the phenomenological deficit of normative and naturalistic theories (including critical theory and neurophilosophy). If what matters is normativity, then to avoid the divide between naturalism and non-naturalist normativity one nonfoundationalist alternative is to resort to hermeneutical and procedural accounts of normativity as helpful clues to making sense of the naturalism-normativity problem, avoiding reductionist interpretations of both naturalism (Churchland) and normativism (Parfit).

Artificial Intelligence and the cyber utopianism of justice. Why AI is not intelligence and man’s struggle to survive himself

Objective: to show the ontological differences between human and artificial intelligence and address structural divergences at the definitional level.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: a cross-cutting analysis was applied to the phenomenon of AI between cyber utopianism and cyber realism. Starting from a quote by Max Tegmark, the theory of artificial intelligence is reconstructed by the theorists who founded the discipline (Turing, Minsky, Bernstein, von Neumann) and it is discussed why – in light of the discoveries and assumptions of neuroscience – it is not possible to define it as intelligence according to human criteria. Three short notes are included in the appendix that complete the discussion: 1. on the consciousness of machines 2. on the theory of utopian cyber employment and remuneration 3. “The hungry judge is more cruel” (discussion on an Israeli study).Scientific novelty: through the examination of multiple types of intelligence (Gardner) and social intelligence (Thorndike, Goleman), a more complex definition of intelligence is proposed than that which can be replicated by artificial neural networks, especially in relation to the interaction between animal and environment. Three short messages highlight the uncertainty and risks that may arise from the rampant use of artificial intelligence as judges.Practical significance: starting from a correct definition of human intelligence, the author comes to the definition of artificial intelligence. Beyond the myth of AI, we discover its limits and the objective limitations we must provide for in order to save the most precious asset we have: mankind.

Study on Micro-Pressure Drive in the KKM Low-Permeability Reservoir

Kazakhstan has abundant resources of low-permeability oil reservoirs, among which the KKM low-permeability oil reservoir has geological reserves of 3844 × 104 t and a determined recoverable reserve of 1670 × 104 t. However, the water flooding efficiency is only 68%, and the recovery efficiency is as low as 32%. The development of the reservoir faces challenges such as water injection difficulties and low oil production from wells. In order to further improve the oil recovery rate of this reservoir, our team developed micro-pressure-driven development technology based on pressure-driven techniques by integrating theories of fluid mechanics and artificial intelligence. We also combined this with subsequent artificial lift schemes, resulting in a complete set of micro-pressure-driven process technology. The predicted results indicate that after implementing micro-pressure-driven techniques, a single well group in the KKM oilfield can achieve a daily oil production increase of 32.08 t, demonstrating a good development effect.

Advancements in AI for Oncology: Developing an Enhanced YOLOv5-based Cancer Cell Detection System

As artificial intelligence (AI) theory becomes more sophisticated and its utilization spreads across daily life, education, and professional settings, the adoption of AI for medical diagnostic and service purposes stands as a logical progression in the evolution of medical technologies. This document outlines a novel approach to detecting cancer cell targets using a deep learning-based system, marking a critical step towards integrating AI into cancer diagnostics. The process of detecting cancer cell targets entails the localization of cell types within images of cells. By capitalizing on the strengths of the YOLOv5 model—a deep learning-driven, end-to-end, real-time object detection framework known for its efficiency, superior performance, adaptability, and user-friendly PyTorch integration—this research presents an enhanced YOLOv5 model incorporating both a feature pyramid network and the original YOLOv5 architecture. The ultimate aim is to facilitate precise detection of targets in cancer cell images. The experimental data demonstrate the system's negligible error rate in detection, swift processing capabilities, and exceptional reliability.

Study of medium and long-term free flow capacity and queue discharge rates on roads.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city's main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning.

Towards the net zero carbon future: A review of blockchain‐enabled peer‐to‐peer carbon trading

AbstractThe increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem.

Enhancing a star algorithm for robot path planning

This paper describes the importance of robot path planning in artificial intelligence and control theory, and proposes three improvements to the A-algorithm: bi-directional A-search, improved heuristic functions and pruning strategies. The performance of the different algorithms in terms of computation time, path length and number of nodes is compared through experiments. Moreover, it is emphasised in the article that in practical applications suitable algorithms and their improvements are selected according to the characteristics of the specific problem and reasonable evaluation criteria are used to measure the performance of the algorithms.

The Role of Artificial Intelligence on the Public Energy Sector Performance in the United Arab Emirates: The Mediation Role of Organizational Agility

Purpose: This paper presents an in-depth analysis of the interaction between Artificial Intelligence (AI), organizational agility, and performance within the UAE's public energy sector. It explores the transformative role of AI in this context and the critical importance of organizational agility in determining outcomes in the energy field. Design/methodology/data analysis: The methodology employed in this study is a cross-sectional survey design, with data collected from 245 managers across various public energy companies in the UAE. The survey instrument measured variables pertaining to AI, such as Customer Relationship Management and Cost-efficient IS Operations, and facets of organizational agility, including Responsiveness and Competency, as well as overall Organizational Performance. Findings: The study's findings reveal a significant direct impact of AI on organizational performance, which is further enhanced by the presence of organizational agility. The data indicates that AI's integration within Customer Relationship Management and Cost-efficient IS Operations positively affects performance. Additionally, organizational agility through its components of Responsiveness and Competency serves as a significant intermediary, amplifying the influence of AI on performance. Originality/value: The research is grounded in the Process Theory of Change, the Diffusion of AI Theory, and the Resource-Based View Theory, providing a solid theoretical base for its exploration. It offers a nuanced understanding of the combined impact of AI and organizational agility on the public energy sector's performance. Practical implications: The paper concludes with a conceptual framework that encapsulates these relationships, providing stakeholders with a comprehensive view of the interdependencies between AI, agility, and performance. It stresses the imperative for a strategic embrace of AI and organizational agility to foster resilience, adaptability, and sustainable advancement in the UAE's public energy sector. The insights from this paper guide future strategic orientations, emphasizing the integration of technological innovation with agile organizational practices as a pathway to enhanced performance and sectoral leadership.

Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach

Although the empowerment of technology is of great value to society, little is known about its downstream effects on consumers' decisions. This research draws on the expectation–confirmation theory and autonomy in artificial intelligence (AI) and investigates how AI (vs. autonomous choice) has detrimental effects on consumer outcomes, creating an autonomy-technology tension — i.e., the conflict arising from AI technology diminishing consumers' autonomy in their choices. Four studies using a mixed-method approach reveal that the use of AI recommendations in streaming platforms creates an autonomy-technology tension that reduces consumers' performance expectancy, thus lowering their satisfaction. However, such effects are contingent on the nature of the AI recommendations. While a mismatch between AI recommendations and consumer preferences might backfire, AI's negative effect is mitigated when choices match consumers' preferences. We make significant theoretical and practical contributions to empirical research on consumers' sense of autonomy while interacting with AI.

Situating artificial intelligence in organization: A human-machine relationship perspective? A commentary on Kemp's (2023) paper “competitive advantages through artificial intelligence: Toward a theory of situated AI”

The increasing advancements in artificial intelligence (AI) technology have resulted in greater adoption of intelligent devices, such as industrial and service robots. Such context substantially influences the routine processes of operations, thereby promoting the ongoing evolutionary development of human-machine interaction. Here, we analyze an interesting article published in the Academy of Management Review (AMR) by Ayenda Kemp, who proposes the concept of situated AI for discussing AI-driven competitive advantages. In our alternative framework of situating AI in the organization, we identify three aspects of the human-machine relationship—cohesion, autonomy, and equality—and associate them with three redefined situating activities—anchoring, bounding, and calibrating, to bring the full potential of AI to an organization. We believe this defined framework can further contribute to relevant literature in the field of digital economy.

Broad sense and narrow sense perspectives on the metaverse in education: roles of virtual reality, augmented reality, artificial intelligence and pedagogical theories

Broad sense and narrow sense perspectives on the metaverse in education: roles of virtual reality, augmented reality, artificial intelligence and pedagogical theories

Broad sense and narrow sense perspectives on the metaverse in education: Roles of virtual reality, augmented reality, artificial intelligence and pedagogical theories

Broad sense and narrow sense perspectives on the metaverse in education: Roles of virtual reality, augmented reality, artificial intelligence and pedagogical theories

Representation of artificial intelligence (AI) in film and media

Representation of artificial intelligence (AI) in film and media

Person of Interest as Media Technology of Surveillance: A Cautionary Tale for the Future of the National Security State With Diegetic Big Data Surveillance, Algorithmic Security, and Artificial Intelligence

In this article, we examine the television series about global surveillance Person of Interest ( POI) (aired on CBS, 2011–2016) to reflect on popular understandings of surveillance and account for its representation of artificial intelligence (AI) and algorithmic surveillance. Drawing on television, media, and surveillance studies, we focus on the power of representations of social relations and diegetic technologies in possible and imagined futures to explore the role of cultural representations in shaping social order. Through the character of the AI Machine representing algorithmic surveillance, we evaluate the show’s critique of algorithmic autonomy and contend that, as a media technology of surveillance, the show participates in the hype of big data as a panacea while banalizing surveillance. We argue that POI could facilitate a comprehensive analysis of the politics of algorithmic surveillance but fails to do so due to its uncritical representation of artificial intelligence agencies in detecting security risks.

The gendered lens of AI: examining news imagery across digital spaces

Abstract This study investigates gender representation in artificial intelligence (AI)-related images across various digital spaces to understand potential biases and visual narratives in the AI domain. We analyzed a dataset of 28,199 images from news media, technology news websites, social media, knowledge-sharing platforms, and other digital spaces. Our findings revealed the prevalence of male faces and the consistent underrepresentation of women across digital spaces. We also found distinct patterns in the visual framing of men and women, with women often portrayed as being disempowered and adhering to traditional gender stereotypes. Furthermore, our cluster analysis demonstrated consistent patterns of gender representation across various visual themes, reinforcing the pervasive nature of gender biases in AI news imagery. In conclusion, our study underscores the need for conscious efforts to promote a more balanced and inclusive portrayal of gender in AI news reporting, calling for a broad societal effort toward advancing gender equality and diversity.

Revolutionizing family businesses with artificial intelligence: a perspective article

PurposeThis perspective article addresses the essential need to comprehend what artificial intelligence (AI) entails and how it can revolutionize the family business sector.Design/methodology/approachThis article presents a literature review and the authors' perspectives to unravel the potential benefits of AI in family businesses.FindingsIt is crucial to understand what AI is and its various types before the authors can discern which AI tools can be beneficial or tailored to family businesses. A promising area for future research is the use of Theory of Mind AI, which can help minimize conflicts of interest among family business members and assist in clear decision-making and succession planning. However, integrating AI brings about ethical implications, such as data privacy concerns, the need for transparency in AI decision-making and the necessity to ensure fair AI practices. These are all critical issues that the future research will need to tackle.Originality/valueThis article emphasizes the potential of AI to be customized and incorporated to tackle the distinct dynamics, obstacles and opportunities prevalent in family-owned businesses. It also proposes future research areas that could enhance the application of AI in such enterprises.

Linguocultural Specifics of Artificial Intelligence Representation in the English Language Media Discourse: Corpus-Based Approach

The article deals with the discursive construction of artificial intelligence (AI) in the English-language media discourse. Corpus methods establish semantic, figurative-perceptual and axiological specificity in the media representation of AI in various media segments, such as tabloids and broadsheet papers. Corpus-assisted discourse analysis includes: 1) identification of unique semantic domains that determine discourse interpretations of this socially significant phenomenon, followed by an analysis of their dispersion in narratives about AI; 2) analysis of the figurative-evaluative and value content of its media representation; 3) investigation of the specifics of broadsheet media and tabloid media representations of the AI concept. It was found that AI receives multiple media representations, in which its conceptual features represented by the lexical items of the semantic fields "Knowledge", "Ability", "Information and Computer Technologies" are supplemented and enriched by the lexical units of the semantic fields "Safety/Danger", "Transformation", "Ethics". In media narratives about AI, the concept of "imitation of human abilities" is often associated with fake news, plagiarism, warfare, crime, climate change, and unemployment. Corpus data revealed that in the narratives about AI in broadsheet media, the lexical items of the semantic field "Robots" emphasize the similarity of AI with anthropomorphic entities; the semantic field "Competition" represents the idea of a rivalry between a human and a machine, as well as between countries, governments and companies; the semantic field "Warfare" actualizes the pragmatics of warning related to collective security and expressing public concern about the possible negative consequences of the development of AI. Popular media emphasize the androgenic nature of AI, its entertainment potential ("Games"), practical values related to health care ("Medicines and Treatment"); and the pragmatics of warning related to individual safety ("Crime"). The evaluative media representation of AI is formed mainly by ethical and practical, often negative, evaluative features transmitted through metaphoric images, intertextual references, and cultural models that determine the behavioural patterns of the discursive community members.

‘What they’re not telling you about ChatGPT’: exploring the discourse of AI in UK news media headlines

This study analyses the discursive representation of Artificial Intelligence (AI) and ChatGPT in UK news media headlines from January to May 2023. A total of 671 headlines were collected and analysed using inductive thematic analysis, theoretically informed by Agenda-Setting theory and Framing theory. The results offer an initial picture of how recent technological advances in the fields of AI have been communicated to the public. The results show that there is a complex and at times paradoxical portrayal of AI in general and ChatGPT as well as other Large Language Models (LLMs), oscillating between promising potential for solving societal challenges while simultaneously warning of imminent and systemic dangers. Further to this, the analysis provides evidence for the claim that media representations of AI are often sensationalised and tend to focus more on warnings and caution to readers, as only a minority of headlines were related to helpful, useful, or otherwise positive applications of AI, ChatGPT, and other Large Language Models (LLMs). These findings underscore the pivotal role of media discourse in shaping public perceptions of AI. The study prompts reflections on news media practices in the United Kingdom and encourages future research to further examine the influence of social, cultural, and political contexts on AI representation during a period of technological change. This research provides relevant insights for policymakers, AI developers, and educators to support public engagement with AI technologies.