Abstract Artificial Intelligence Applications Research Articles

Regulating Gatekeeper Artificial Intelligence and Data: Transparency, Access and Fairness under the Digital Markets Act, the General Data Protection Regulation and Beyond

AbstractArtificial intelligence (AI) is not only increasingly being used in business and administration contexts, but a race for its regulation is also underway, with the European Union (EU) spearheading the efforts. Contrary to existing literature, this article suggests that the most far-reaching and effective EU rules for AI applications in the digital economy will not be contained in the proposed AI Act, but in the Digital Markets Act (DMA). We analyse the impact of the DMA and related EU acts on AI models and underlying data across four key areas: disclosure requirements; the regulation of AI training data; access rules; and the regime for fair rankings. We demonstrate that fairness, under the DMA, goes beyond traditionally protected categories of non-discrimination law on which scholarship at the intersection of AI and law has focused on. Rather, we draw on competition law and the FRAND criteria known from intellectual property law to interpret and refine the DMA provisions on fair rankings. Moreover, we show how, based on Court of Justice of the European Union jurisprudence, a coherent interpretation of the concept of non-discrimination in both traditional non-discrimination and competition law may be found. The final section sketches out proposals for a comprehensive framework of transparency, access and fairness under the DMA and beyond.

Artificial intelligence technologies and applications for language learning and teaching

Abstract Artificial intelligence (AI) is changing many aspects of education and is gradually being introduced to language education. This article reviews the literature to examine main trends and common findings in relation to AI technologies and applications for second and foreign language learning and teaching. With special reference to computer-assisted language learning (CALL), the article explores natural language processing (NLP), data-driven learning (DDL), automated writing evaluation (AWE), computerized dynamic assessment (CDA), intelligent tutoring systems (ITSs), automatic speech recognition (ASR), and chatbots. It contributes to discussions on understanding and using AI-supported language learning and teaching. It suggests that AI will be continuously integrated into language education and AI technologies and applications will have a profound impact on language learning and teaching. Language educators need to ensure that AI is effectively used to support language learning and teaching in AI-powered contexts. More rigorous research on AI-supported language learning and teaching is recommended to maximise second and foreign language learning and teaching with AI.

Künstliche Intelligenz in der deutschen Wirtschaft: Ohne Digitalisierung und Daten geht nichts

Abstract Artificial Intelligence (AI) holds immense potential for enhancing prosperity. However, the adoption of AI in German businesses remains limited, with only 19 % of companies utilizing AI in 2022. The successful implementation of AI relies on two key prerequisites: a company’s digitalisation and data economy readiness. The Digitalisation Index reveals slow progress in digitalisation across sectors, indicating a need for increased efforts. Additionally, companies must enhance their data economy readiness to efficiently utilize data for AI applications. Failing to tap into the potential of AI may result in significant competitive disadvantages in the future.

Role of Artificial Intelligence in Transforming Public Health Dentistry: A narrative review

Abstract Artificial intelligence (AI) has the ability to process huge datasets, disclose human essence computationally, and perform tasks like humans as technology advances. Because of the necessity for precise diagnosis and improved patient care, AI technology has greatly influenced the health-care industry and dental public health. This narrative review summarizes the important applications of AI in the specialty of dentistry and public health dentistry. A literature review through various databases such as Web of Science, PubMed, Scopus, Cinahl, Google Scholar, Embase, Science Direct, and Directory of Open Access Journals was done. Results of the review highlight the scope of AI from the patient observation, examination, and collection of patient information to the interpretation of the information obtained from the patient on the basis of the clinician’s knowledge and experience, formulation of a diagnosis and treatment plan for various clinical conditions, telemedicine and remote patient monitoring, Dental Therapeutics, Care navigation for self-triage of patients, Maintaining Electronic health records and also in Evidence synthesis for policy, clinical decision making and public health literacy. However, its flaws include system complexity, Costly setup, need for adequate training, and data snooping bias. Still, there exists a scope for AI to come a long way in the domains of public health dentistry nevertheless, the human element must never be dismissed.

The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research

Abstract Artificial intelligence (AI) represents a technological upheaval with the potential to change human society. Because of its transformative potential, AI is increasingly becoming subject to regulatory initiatives at the global level. Yet, so far, scholarship in political science and international relations has focused more on AI applications than on the emerging architecture of global AI regulation. The purpose of this article is to outline an agenda for research into the global governance of AI. The article distinguishes between two broad perspectives: an empirical approach, aimed at mapping and explaining global AI governance; and a normative approach, aimed at developing and applying standards for appropriate global AI governance. The two approaches offer questions, concepts, and theories that are helpful in gaining an understanding of the emerging global governance of AI. Conversely, exploring AI as a regulatory issue offers a critical opportunity to refine existing general approaches to the study of global governance.

Artificial intelligence and civil liability—do we need a new regime?

Abstract Artificial intelligence (AI) is almost ubiquitous, featuring innumerable facets of daily life. For all its advantages, however, it carries risks of harm. In this article, we discuss how the law of tort should deal with these risks. We take account of the need for any proposed scheme of liability to protect the existing values of tort law without acting as a barrier to innovation. To this end, we propose a strict liability regime in respect of personal injury and death, and a bespoke fault-based regime for dignitary or reputational injuries. For other losses, we take the view that there is no justification for introducing any new regime, on the basis that AI applications do not introduce substantial added degrees of risk that would justify departing from the existing scheme of liability arising under the current law of tort.

The role of artificial intelligence in shaping the future of Agile fashion industry

Artificial intelligence (AI) has become an integral part of every industry. With the emergence of big data, the industries, and more especially textile and apparel (T&A) industry, are on the brink of relationships with consumers, suppliers, and competitors. They need to handle different scenarios with a multitude of complex correlations and dependencies between them and uncertainties arising from human interaction. It has become imperative for them to manage huge amounts of data for the optimization of decision-making processes. In such circumstances, AI techniques have shown promise in every segment of the T&A value chain, from product discovery to robotic manufacturing. The potential wide-ranging applications of AI in T&A industry have found their ways into design support systems to T&A recommendation systems, intelligent tracking systems, quality control, T&A forecasting, predictive analytics in supply chain management or social networks and T&A e-commerce. The research recourses to the qualitative method in the form of systematic literature review and in-depth interviews from senior management people and industry experts. Findings identify the dimensions of AI to develop dynamic capability along with its potential impact and probable challenges. As such, the findings contribute to relevant literature and offer useful insights for academia and practitioners.

Dual-Use and Trustworthy? A Mixed Methods Analysis of AI Diffusion Between Civilian and Defense R&D

Artificial Intelligence (AI) seems to be impacting all industry sectors, while becoming a motor for innovation. The diffusion of AI from the civilian sector to the defense sector, and AI’s dual-use potential has drawn attention from security and ethics scholars. With the publication of the ethical guideline Trustworthy AI by the European Union (EU), normative questions on the application of AI have been further evaluated. In order to draw conclusions on Trustworthy AI as a point of reference for responsible research and development (R&D), we approach the diffusion of AI across both civilian and military spheres in the EU. We capture the extent of technological diffusion and derive European and German patent citation networks. Both networks indicate a low degree of diffusion of AI between civilian and defense sectors. A qualitative investigation of project descriptions of a research institute’s work in both civilian and military fields shows that military AI applications stress accuracy or robustness, while civilian AI reflects a focus on human-centric values. Our work represents a first approach by linking processes of technology diffusion with normative evaluations of R&D.

Artificial Intelligence or the Ultimate Tool for Conservatism

Abstract Artificial intelligence (AI) is foremost viewed as a technologically revolutionary tool, however, the author discusses here whether it is in fact a tool for socio-economic and legal conservatism, because its training data is always embedded in the past. The aim of this paper is to explain, exemplify and predict – whether and how – AI could cause discrimination, stagnation and uniformization by conserving what is relayed even by the most representative data. Furthermore, the author aims to propose possible legal barriers to these phenomena. The presented hypotheses are based upon empirical research and socioeconomic or legal mechanisms, aiming to predict possible results of AI applications under specific conditions. Results indicate that the inherent AI conservatism could indeed cause severe discrimination, stagnation and uniformization, especially if its applications were to remain unquestioned and unregulated. Hopefully, the proposed legal solutions could limit the scope and effectiveness of AI conservatism, encouraging AI-related solutions.

Artificial Intelligence : The Supermind - A Review

Artificial intelligence (AI) is one of the most important technologies in the world today. In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial Intelligence is impacting the future of virtually every industry and every human being. AI has acted as the main driver of emerging technologies like big data, robotics, and IoT, and it will continue to act as a technological innovator for the foreseeable future. AI is simply the study of how to make computer do things which at the moment people do the better. There are many ways to define AI, but one simple definition is “intelligence demonstrated by machines”. Primary goal of AI is to improve computer behaviour so that it can be called intelligent. AI is ubiquitous and is not only limited to computer science but has evolved to include other areas like health, security, education, music, art, and business application. This paper gives an overview of how the AI actually works, its scopes , the different applications of AI, its advantages and disadvantages and many more topics which will give a clear understanding inspite of the boundlessness of AI.

Rule-following robots? Transitional legal disruption through regulatee design and engineering

ABSTRACT Artificial Intelligence (AI) and Extended Reality (XR) technologies present regulators with powerful tools to manipulate human behaviour, but more perniciously, human desire and indeed human being. This is because AI applications can interfere with our internal decision-making processes, and XR applications can affect our sensations by creating and mediating our experiences of the external world. When these technologically driven affordances are strained through contemporary cognitive science research to ground the notion that perceptions are processes for prediction error minimisation, such interferences can amount to designing and engineering the regulatee herself. Effectively, regulation no longer needs to be signalled in a normative regulatory environment, nor must it hardcoded into the architecture or technologically managed. Instead, AI and XR make it possible to design and create regulatees that embody the desired regulatory outcome. Paradoxically, however, such regulatory incorporation looks very much like the exercise of the agency of an agent and therefore is not recognised as a problem by contemporary legal principles and processes which seek to push back against obviously external influences or pressures. As a result, it is immensely difficult to articulate the legal or regulatory challenges posed by these developments, and to identify the harms through these doctrinal lenses.

Harnessing the Power of Artificial Intelligence and Machine Learning in Mineral Exploration—Opportunities and Cautionary Notes

Editor’s note: The aim of the Geology and Mining series is to introduce early-career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract Artificial intelligence (AI), and machine learning (ML) have emerged in the last few years from relative obscurity in the mineral exploration sector and they now attract significant attention from people in both industry and academia. However, due to the novelty of AI and ML applications, their practical use and potential remain enigmatic to many beyond a relatively few expert practitioners. We introduce this subject for the nonexpert and review some of the current applications and evolving uses. For the most traditionally minded geologist, we argue that ML can be an invaluable new tool, contributing to topics that range from exploratory data analysis to automated core logging and mineral prospectivity mapping, such that it will have a substantial impact on how exploration is conducted in the future. However, ML algorithms perform best with a large amount of homogeneously distributed clean data for a well-constrained objective. For this reason, the application to exploration strategy, especially for optimizing target selection, will be a challenge where data are heterogeneous, multiscale, amorphous, and discontinuous. For the more tech-savvy geologist and data scientist, we provide notes of caution regarding the limitations of ML applied to geoscience data, and reasons to temper expectations. Nonetheless, we project that such technologies, if used in an appropriate manner, will eventually be part of the full range of exploration tasks, allowing explorers to do more with their data in less time. However, whether this will tip the scales in favor of higher discovery rates remains to be demonstrated.

Travellers’ Satisfaction on the Applications of Artificial Intelligence in Malaysia’s Tourism and Hospitality Industry.

Artificial intelligence (AI) is perceived as being able to transform tourism and hospitality industry’s operations into a greater efficiency and cost-effectiveness while offering travellers unique experiences. This study examines travellers’ satisfaction of AI applications, specifically through e-Hailing, e-Wallet, e-Gate and e-Visa in the tourism and hospitality industry in Malaysia. Quantitative research approach was adopted in the current study. Data was gathered from 200 respondents using self-administrative questionnaires. Local and foreign travellers of age 18 and above who had past travel experience were chosen as samples. The findings indicated that e-Hailing, e-Gate and e-Wallet significantly influence travellers’ satisfaction. However, the outcome shows that e-Visa application has no relationship with travellers’ satisfaction. This study helps to strengthen the tourism ministry’s current initiatives in boosting the tourism and hospitality industry in Malaysia. The outcome of the study might be of interest to the policy makers and regulators to improve on the applications of AI in tourism and hospitality in Malaysia.

Computers That Look Like the Brain

Artificial intelligence applications have been developing rapidly over the past few years, allowing computers to perform complex actions, such as driving without a driver, making decisions, and recognizing faces. These applications require that many calculations be performed in parallel and immense amounts of information are needed. This article demonstrates how inefficient today’s computer structure is for performing artificial intelligence applications. To deal with this challenge and improve artificial intelligence applications, we will see how inspiration from the way the human brain works will allow us to build completely new computers, which will rock the way computers have been built for many years.

Artificial Intelligence in the post-COVID-19 era

Artificial Intelligence (AI) dominates the media and has been the focus of public discourse in the last few years. Since last March, however, COVID-19 makes almost everyday headlines because of its huge impact on our highly globalized world. This article has a dual objective. First, it aims to dissolve common misunderstandings and fears regarding AI. Second, by putting AI’s and COVID-19 societal and economic impacts into perspective, it discusses AI applications and research in the post-COVID-19 era. COVID-19 has played a catalytic role in accelerating the use of AI to mitigate the pandemic’s devastating effects. It is up to us to use AI in a responsible and ethical way that includes and benefits the whole humanity.

Artificial Intelligence in cardiovascular medical imaging

Artificial Intelligence (AI) has become an important tool for computer-aided diagnosis of medical imaging. This review aims to provide an overview for clinicians, explaining the relevant aspects of artificial intelligence and machine learning (ML) and presenting up-to-date applications of AI and ML techniques to medical imaging methods such as angiography, magnetic resonance, and echocardiography. For each imaging method, we present the acquisition process, the types of diagnostic test interpretation, and the challenges related to them, as well as how AI/ML techniques, have improved the process of decision making. A summary of selected works applying AI/ML techniques to medical imaging is organized into a table, which highlights the scope of the study, the dataset used, the details of each approach as well as the measured results, including objectives and criteria. The overall benefits of AI in medical imaging are extracted based on the diverse applications and high evaluation scores. In the end, cardiologists should have an advanced understanding of using AI to integrate clinical data and making the final decision in diagnosis.

Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology

Artificial intelligence (AI) is an aspect of computer technology that imitates the ability of the human mind to analyze data. Over the last few years, there has been a paradigm shift in the utilization of AI in clinical practice. It is imperative for the clinical electrophysiologist to understand the basics of AI, and its potential applications in the field as new applications are developed and implemented. Multiple investigators have demonstrated various AI algorithms that can be utilized in clinical care. These include applications such as electronic stethoscopes and electrocardiographic prediction of atrial fibrillation or congestive heart failure. AI may also be used in cardiovascular imaging, to identify disease patterns and even compose preliminary reports. Herein, we seek to familiarize readers with terms associated with AI, such as machine learning and neural networks. Further, we review the applications of AI in bedside clinical calculators, electrocardiography, and the field of cardiovascular imaging. A critical appraisal of AI is provided with specific review of hurdles in the integration of AI in clinical practice.

Flourishing Financial Operations Through Smarter Intelligence

Artificial Intelligence (AI) has penetrated every aspect of our lives in this era of Big Data. Nowadays application of AI has become very common. It is also playing a vital role in the field of finance. AI has been helping in every aspect of finance, from banking to investment. AI has given the world of banking and the financial industry as a whole a way to meet the demands of customers who want smarter, more convenient, safer ways to access, spend, save and invest their money. Especially during pandemic situations, AI is playing the most important role in the field of finance by helping society to do financial activities digitally more securely, accurately but in a very handy way using data mining operations. This paper will attempt to focus on the emerging applications of AI to optimize different financial services.

L’intelligence artificielle en santé, un potentiel majeur d’innovations pour notre système de santé

Artificial intelligence (AI) is rapidly being extended across health systems with multiple cases of its use already reported. The most operational technique is machine learning with image recognition in imaging. Solutions derived from this approach, as well as other applications of AI, are presented in two major fields: cancer management and geriatric care.

A Dependency-Aware Storage Schema Selection Mechanism for In-Memory Big Data Computing Frameworks

Artificial intelligence applications that greatly depend on deep learning and compute vision processing becomes popular. Their strong demands for low-latency or real-time services make Spark, an in-memory big data computing framework, the best choice in taking place of previous disk-based big data computing. As an in-memory framework, reasonable data arrangement in storage is the key factor of performance. However, the existing cache replacement strategy and storage selection mechanism based optimizations all rely on an imprecise available memory model and will lead to negative decision. To address this issue, we propose an available memory model to capture the accurate information of to be freed memory space by sensing the dependencies between the data. And we also propose a maximum memory requirement model for execution prediction to exclude the redundancy from inactive blocks. With such two models, we build DASS, a dependency-aware storage selection mechanism for Spark to make dynamic and fine-grained storage decision. Our experiments show that compared with previous methods the DASS could effectively reduce the cost of garbage collection and RDD blocks re-computing, give better computing performance by 77.4%.