Artificial Intelligence Research Research Articles

Evolving Client–Vendor Relationship: As Manifested Through an Artificial Intelligence Research Unit of an Indian IT Organization

The Indian information technology (IT) sector is traditionally known for delivering IT and business process management (BPM) services to clients across the globe. To ensure the longevity of this client and service provider relationship, Indian IT organizations have made significant client-specific investments and actively engaged them in offshore development activities. One of the outcomes from such investments can be seen in terms of the very high maturity standards that Indian IT organizations currently exhibit in the context of IT–BPM service delivery. In recent times, these organizations are also undertaking offshore projects around emerging technologies. Such projects, especially those related to artificial intelligence (AI), are largely centred around the automation of client-focused BPM services. Given their relative nascency, such projects are primarily led by the AI research units that are rapidly diffusing across Indian IT organizations. In this study, we rely on a four-month-long ethnography to explore a client and vendor (or service-provider) relationship as manifested through one such AI research unit of an Indian IT organization situated in Bengaluru, India. Our objective from this study is to understand how these new offshore AI projects affect the longevity of the relationship between clients and the Indian IT service providers. Given the nature of emerging technology workflows, our findings indicate a tilt more towards meeting client-side expectations than that of vendors in these projects. We portend that this can potentially disrupt the longevity of the client–vendor relationship in the Indian IT services sector, and, therefore, both the clients and vendors must attend to the peculiarities of offshore emerging technology projects to mitigate such disruptions.

Bibliometric Analysis of AI Technology Developments in Education: Trends, Collaborations, and Future Impact

This study employs bibliometric analysis to explore the landscape of artificial intelligence (AI) applications in education. Through an extensive review of thematic clusters, research trends, author collaborations, and potential future research topics, the study maps the evolution and current state of AI in educational settings. Key findings indicate a robust focus on integrating AI within higher education and a significant emphasis on educational technology across various specialized domains. Temporal analysis reveals a progressive shift toward exploring advanced topics such as generative AI, reflecting the field's rapid adaptation to new technological advancements. Additionally, the author collaboration network illustrates a vibrant and interconnected academic community, while also identifying less explored areas like language education and AI in art education as promising fields for future research. The study highlights the dynamic nature of AI research in education, showcasing both the depth of current integrations and the potential for innovative applications that could further transform educational practices.

Awareness and perceptions of artificial intelligence in dentistry: A cross-sectional survey among Indian dental professionals

Introduction Artificial intelligence (AI) is inevitably going to impact healthcare including dentistry and will become an essential tool in medical diagnosis and decision-making. Dental professionals must be familiar with growing trends in dentistry such as AI and its future scope. Despite the positive developments in AI research, there are divergent perspectives on its benefits and risks among stakeholders. We intended to understand the knowledge, awareness, and perceptions of dental professionals towards AI and its applications in dentistry. Methods and Material A semi-structured, 25-item Google form questionnaire consisting of closed and open-ended questions was made and the link to answer the survey was circulated among postgraduate students, dental academicians, and practitioners across India in an online mode, and the responses were collected and analyzed. Results 83.3% of participants were aware of AI and its applications. Most of the participants understood the attributes, advantages, and disadvantages of AI. Interestingly 72% of participants agreed that they have witnessed AI being used in clinical practice and 92.7% agreed to use AI for diagnosis. 65.3% expressed concern over unemployment due to AI and 85% agreed that AI has ethical issues. Over 85% of participants agreed AI should be a part of the postgraduate dental curriculum. Conclusions We found that dental professionals are updated with AI technology and showed a willingness to adopt AI into dental practice. The participants lacked a deeper understanding of AI and concerned about the potential risk of unemployment resulting from AI and trusting AI alone in dental diagnosis. Keywords: Artificial intelligence, Cross-sectional survey, Dentist, Knowledge, Perceptions.

P.074 Assessing the emergence and evolution of artificial intelligence and machine learning research in neuroradiology

Background: Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and the field’s trends, challenges, and future directions. Methods: The American Journal of Neuroradiology was queried for original research articles published since inception (Jan. 1, 1980) to Sept. 19, 2022 that contained any of the following key terms: “machine learning”, “artificial intelligence”, or “radiomics”. Articles were screened, categorized into Statistical Modelling (Type 1), AI/ML Development (Type 2), or End-user Application (Type 3) and then bibliometrically analyzed. Results: A total of 124 articles were identified with 85% being non-integration focused (Type 1 n = 41, Type 2 n = 65) and the remaining (n = 18) being Type 3. The total number of articles published grew two-fold in the last five years, with Type 2 articles mainly driving this growth. While most (66%) Type 2 articles were led by a radiologist with 55% possessing a postgraduate degree, a minority of Type 2 articles addressed bias (15%) and explainability (20%). Conclusions: The results of this study highlight areas for improvement but also strengths that stakeholders can consider when promoting the shift towards integrating practical AI/ML solutions in neuroradiology.

Construction of Accounting Fraud and Its Audit Countermeasure Model Based on Computer Technology

A prediction model of financial fraud of listed companies based on machine learning method is proposed to predict financial fraud of listed companies. Using the data set of Chinese listed companies from 2000 to 2020 as observation samples, Benford’s Law, LOF local anomaly method and SMOTE oversample were adopted, grey samples were excluded, and characteristic variables were selected from five aspects: fraud motivation, solvency, profitability, cash flow and operating capacity. The financial fraud identification model Xscore is established based on the XGBoost method. The Xscore model can improve the accuracy of model prediction, and is superior to the Fscore model and Cscore model in accuracy, recall rate, AUC index, KS value, PSI stability, etc. It is more suitable for predicting the financial fraud of listed companies in China. The results of this study are helpful in promoting the research and application of artificial intelligence and machine learning in accounting, and provide references for promoting the disclosure of high-quality financial information by listed companies and maintaining the order of the capital market.

A Human Rights Framework for AI Research Worthy of Public Trust

Artificial intelligence researchers regularly conduct social experiments relying on data from participants who haven’t agreed to take part. To earn public trust, researchers need to reorient computational research toward respect for human rights by adopting a robust AI ethics protocol.

Philosophy of cognitive science in the age of deep learning.

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the center stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful. This article is categorized under: Philosophy > Artificial Intelligence Computer Science and Robotics > Machine Learning.

Artificial Intelligence and an Anthropological Ethics of Work: Implications on the Social Teaching of the Church

It is the contention of this paper that ethics of work ought to be anthropological, and artificial intelligence (AI) research and development, which is the focus of work today, should be anthropological, that is, human-centered. This paper discusses the philosophical and theological implications of the development of AI research on the intrinsic nature of work and the nature of the human person. AI research and the implications of its development and advancement, being a relatively new phenomenon, have not been comprehensively interrogated in the social and ethical teachings of the Catholic Church. This paper, therefore, proposes a path for this interrogation by expounding a discourse which is believed to be epistemically helpful in the developing discourse of AI in the ethical and social teachings of the Church. The advancement in the research on AI is not only redefining the meaning of work, but, even more so, it is questioning the metaphysical notion of the human person and the theological notion of work as an intrinsic part in the selfhood and dignity of the human person.

Transfer learning for anatomical structure segmentation in otorhinolaryngology microsurgery.

Reducing the annotation burden is an active and meaningful area of artificial intelligence (AI) research. Multiple datasets for the segmentation of two landmarks were constructed based on 41257 labelled images and 6 different microsurgical scenarios. These datasets were trained using the multi-stage transfer learning (TL) methodology. The multi-stage TL enhanced segmentation performance over baseline (mIOU 0.6892 vs. 0.8869). Besides, Convolutional Neural Networks (CNNs) achieved a robust performance (mIOU 0.8917 vs. 0.8603) even when the training dataset size was reduced from 90% (30078 images) to 10% (3342 images). When directly applying the weight from one certain surgical scenario to recognise the same target in images of other scenarios without training, CNNs still obtained an optimal mIOU of 0.6190±0.0789. Model performance can be improved with TL in datasets with reduced size and increased complexity. It is feasible for data-based domain adaptation among different microsurgical fields.

A possible worlds semantics for trustworthy non-deterministic computations

The notion of trustworthiness, central to many fields of human inquiry, has recently attracted the attention of various researchers in logic, computer science, and artificial intelligence (AI). Both conceptual and formal approaches for modeling trustworthiness as a (desirable) property of AI systems are emerging in the literature. To develop logics fit for this aim means to analyze both the non-deterministic aspect of AI systems and to offer a formalization of the intended meaning of their trustworthiness. In this work we take a semantic perspective on representing such processes, and provide a measure on possible worlds for evaluating them as trustworthy. In particular, we intend trustworthiness as the correspondence within acceptable limits between a model in which the theoretical probability of a process to produce a given output is expressed and a model in which the frequency of showing such output as established during a relevant number of tests is measured. From a technical perspective, we show that our semantics characterizes the probabilistic typed natural deduction calculus introduced in D'Asaro and Primiero (2021)[12] and further extended in D'Asaro et al. (2023) [13]. This contribution connects those results on trustworthy probabilistic processes with the mainstream method in modal logic, thereby facilitating the understanding of this field of research for a larger audience of logicians, as well as setting the stage for an epistemic logic appropriate to the task.

Managing and controlling digital role-playing game elements: A current state of affairs

Role-playing games (RPGs) are interactive gaming experiences designed and driven by the intricate development of narratives and characters. However, these games also highlight elements, such as combat, exploration, creativity, and crafting. Game developers helped to shape the market by incorporating RPGs into digital media; however, they suffered restrictions in their game design and storytelling caused by technological limitations. Fortunately, academia has studied these challenges and mitigated these latter issues to better understand and connect digital RPGs with their analog versions. Our research offers a comprehensive overview of studies centered on technologies for managing, generating, and controlling digital RPG elements. We aim to characterize Artificial Intelligence research within this domain and elucidate the diverse techniques employed. In this context, we examined 72 papers about Procedural Content Generation (PCG) in RPG, which were identified by mixing a database search with a snowballing method. In this paper, we provide an overview of PCG techniques investigated within the realm of RPGs and present the trending approaches for future developments in the field.

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Background and Objective:Artificial Intelligence (AI) has shown significant advancements across several industries, including healthcare, using better fusion methodologies, improved data accessibility, and heightened processing capabilities. Although deep learning algorithms excel in challenging scenarios, their widespread adoption presents a challenge due to their need for more transparency. The advent of Explainable AI (XAI) has addressed this limitation by offering AI-driven systems with attributes such as transparency, interpretability, and reliability. The need for transparency in critical sectors like healthcare has prompted an increased academic focus on investigating and understanding these frameworks. The present paper thoroughly examines the latest research and advancements in Explainable Artificial Intelligence (XAI), specifically focusing on its integration into the Internet of Medical Things (IoMT) in healthcare settings. The last section tackles outstanding issues concerning XAI and IoMT and outlines prospective directions for further research. Methods:A thorough investigation was carried out across many scholarly databases and online digital libraries, such as IEEE Xplore, ACM Digital Library, Wiley Interscience, Taylor and Francis, ScienceDirect, Scopus, Springer, Google Scholar, Citeseer Library, Semantic Scholar, and other relevant sources. An analysis was conducted on articles published from March 2004 to February 2024, specifically focusing on AI models that explain different healthcare issues. The search query included “Explainable AI” in conjunction with “Open Black Box, Healthcare, Transparent Models, Interpretable Deep Learning, Machine Learning, Medical Information System, Accountability, Smart Healthcare, and Responsible AI”, indicating the field’s dynamic nature. The authors also examined various techniques and datasets to elucidate healthcare difficulties, including incorporating the Internet of Medical Things (IoMT). Results:The evaluation included more than 105 published models that used clinical data input to diagnose diverse disorders, focusing on incorporating Internet of Medical Things (IoMT) components. In addition to illness diagnosis, these models also elucidate the decisions made. The models were classified according to the input data, used methodologies, and integration of the Internet of Medical Things (IoMT). The categorization included machine learning and deep learning methodologies, particularly on explainability models connected to the Internet of Medical Things (IoMT). These models were classified as model-agnostic, model-specific, global, local, ante-hoc, and post-hoc. Conclusion:This extensive study examines machine learning, and deep learning models developed based on clinical data and associated with the Internet of Medical Things (IoMT) for illness detection. These models improve efficiency and accuracy and provide essential assistance to medical personnel. Explaining each choice made by individuals contributes to the acceleration of sickness detection, resulting in decreased medical expenses for patients and healthcare systems. The integration of Explainable Artificial Intelligence (XAI) and the Internet of Medical Things (IoMT) highlights a fundamental change in healthcare towards more transparent and linked systems, leading to significant breakthroughs in medical decision-making.

Artificial Intelligence in Water Management for Sustainable Farming: A Review

Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.

FER-BHARAT: a lightweight deep learning network for efficient unimodal facial emotion recognition in Indian context

Humans' ability to manage their emotions has a big impact on their ability to plan and make decisions. In order to better understand people and improve human–machine interaction, researchers in affective computing and artificial intelligence are investigating the detection and recognition of emotions. However, different cultures have distinct ways of expressing emotions, and the existing emotion recognition datasets and models may not effectively capture the nuances of the Indian population. To address this gap, this study proposes custom-built lightweight Convolutional Neural Network (CNN) models that are optimized for accuracy and computational efficiency. These models are trained and evaluated on two Indian emotion datasets: The Indian Spontaneous Expression Dataset (ISED) and the Indian Semi Acted Facial Expression Database (iSAFE). The proposed CNN model with manual feature extraction provides remarkable accuracy improvement of 11.14% for ISED and 4.72% for iSAFE datasets as compared to baseline, while reducing the training time. The proposed model also surpasses the accuracy produced by pre-trained ResNet-50 model by 0.27% ISED and by 0.24% for the iSAFE dataset with significant improvement in training time of approximately 320 s for ISED and 60 s for iSAFE dataset. The suggested lightweight CNN model with manual feature extraction offers the advantage of being computationally efficient and more accurate compared to pre-trained model making it a more practical and efficient solution for emotion recognition among Indians.

Can Large Language Models Be Good Companions?

Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical solution for endowing chatbots with anthropomorphic language capabilities. However, it takes more than LLMs to enable chatbots that can act as companions. Humans use their understanding of individual personalities to drive conversations. Chatbots also require this capability to enable human-like companionship. They should act based on personalized, real-time, and time-evolving knowledge of their users. We define such essential knowledge as the common ground between chatbots and their users, and we propose to build a common-ground-aware dialogue system from an LLM-based module, named OS-1, to enable chatbot companionship. Hosted by eyewear, OS-1 can sense the visual and audio signals the user receives and extract real-time contextual semantics. Those semantics are categorized and recorded to formulate historical contexts from which the user's profile is distilled and evolves over time, i.e., OS-1 gradually learns about its user. OS-1 combines knowledge from real-time semantics, historical contexts, and user-specific profiles to produce a common-ground-aware prompt input into the LLM module. The LLM's output is converted to audio, spoken to the wearer when appropriate. We conduct laboratory and in-field studies to assess OS-1's ability to build common ground between the chatbot and its user. The technical feasibility and capabilities of the system are also evaluated. Our results show that by utilizing personal context, OS-1 progressively develops a better understanding of its users. This enhances user satisfaction and potentially leads to various personal service scenarios, such as emotional support and assistance.

A Scoping Review of Artificial Intelligence Detection of Voice Pathology: Challenges and Opportunities.

Survey the current literature on artificial intelligence (AI) applications for detecting and classifying vocal pathology using voice recordings, and identify challenges and opportunities for advancing the field forward. PubMed, EMBASE, CINAHL, and Scopus databases. A comprehensive literature search was performed following the Preferred Reporting Items for Systematic Reviews and Meta-analysesExtension for Scoping Reviews guidelines. Peer-reviewed journal articles in the English language were included if they used an AI approach to detect or classify pathological voices using voice recordings from patients diagnosed with vocal pathologies. Eighty-two studies were included in the review between the years 2000 and 2023, with an increase in publication rate from one study per year in 2012 to 10 per year in 2022. Seventy-two studies (88%) were aimed at detecting the presence of voice pathology, 24 (29%) at classifying the type of voice pathology present, and 4 (5%) at assessing pathological voice using the Grade, Roughness, Breathiness, Asthenia, and Strainscale. Thirty-six databases were used to collect and analyze speech samples. Fourteen articles (17%) did not provide information about their AI model validation methodology. Zero studies moved beyond the preclinical and offline AI model development stages. Zero studies specified following a reporting guideline for AI research. There is rising interest in the potential of AI technology to aid the detection and classification of voice pathology. Three challenges-and areas of opportunities-for advancing this research are heterogeneity of databases, lack of clinical validation studies, and inconsistent reporting.

A Scoping Review of Transparency and Explainability in AI Ethics Guidelines

Transparency and explainability are crucial tenets of ethical Artificial Intelligence (AI) and are often classified as technical components of AI Ethics. Many countries and international governing bodies have developed AI guidelines and principles that are made public for respective civilians with a diverse range of expertise and knowledge. This short paper compares how explainability and transparency are presented and discussed in AI ethics guidelines developed by the top ten countries leading in AI research and development according to the AI Global Index Report in 2023 and leading efforts from governing bodies such as the EU. Methodologically, this paper presents a thematic analysis focusing on the presence and acknowledgment of various dimensions of explainability and transparency, as well as the level of detail and examples in the guidelines. The aim is to uncover how various aspects of AI ethics are presented in global guideline documents, highlight areas in which the documents converge, and discuss implications for AI stakeholders.

Against the singularity hypothesis

AbstractThe singularity hypothesis is a hypothesis about the future of artificial intelligence on which self-improving artificial agents will quickly become orders of magnitude more intelligent than the average human. Despite the ambitiousness of its claims, the singularity hypothesis has been defended at length by leading philosophers and artificial intelligence researchers. In this paper, I argue that the singularity hypothesis rests on undersupported growth assumptions. I show how leading philosophical defenses of the singularity hypothesis fail to overcome the case for skepticism. I conclude by drawing out philosophical and policy implications of this discussion.

AI EOS using Wolframe Alpha

Artificial intelligence is crucial to day-to-day life. science of computers defines AI research as the study of intelligent agents. Today, most people, intentionally or not, have turned to some form of computerized information processing technology. Artificial Intelligence (AI) is already changing our lifestyle. A device that senses its surroundings and performs actions that maximize the likelihood of achieving a goal. The input to a database can be a choose to of users and articles, and the output is a ruthless recommendation. Input can be verbally or textually submitted by the user within the system. This paper presents a new approach to intelligent search. Overall, there are many people around the world who use assistants. This paper describes the provocation of applying virtual assistant technology. The paper also introduces the application of virtual assistants that can help open opportunities for humanity in various fields. Voice control is an important growing feature that will change people's lives. Voice assistants are available for laptops, desktops, and mobile phones. Assistant is now available on all electronic devices. A voice assistant is a software agent that can interpret human speech and respond in machine language

A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.

Level IV.