Artificial Intelligence Concepts Research Articles

Explore the Development Status of Artificial Intelligence and the Application Analysis of Specific Fields

In the wave of scientific and technological development in the new era, artificial intelligence technology has increasingly become one of the hot scientific and technological fields of social development. Since the concept was proposed, the development of artificial intelligence has also virtually stimulated the market and business strategies in a variety of industry fields. Starting from the actual situation, this paper now divides the four development stages of artificial intelligence from the past to the future in detail and uses the small-range questionnaire survey method to understand the current Chinese society's understanding of and interest in artificial intelligence and its products. Then, literature analysis is used to search and read relevant materials and literature, and the development status, capabilities, and prospects of artificial intelligence in the field of natural language processing and machine learning large models and image and face recognition are analyzed and summarized. In the field of face recognition selected popular processing methods and material library, simple simulation of mainstream recognition options and means, through further examples to help readers understand the development of artificial intelligence status and direction. Understanding the information and relevant viewpoints and attitudes in the specific field, detailing the logic behind the inability of artificial intelligence to replace human beings and the technical difficulties and challenges faced, is of great value to improving the popularization of the concept of artificial intelligence and its development and progress in the future society.

DECODING THE EU ARTIFICIAL INTELLIGENCE ACT: AN ANALYSIS OF KEY CONCEPTS AND PROVISIONS

Artificial Intelligence (AI) is a contemporary phenomenon of vast scale, surpassing the uses and opportunities brought by the advent of the Internet, but which generates fundamental rights risks and legal challenges at an exponentially higher level. In the present study, we aim to explore the concept of artificial intelligence, the types of AI technologies, and the requirements for market introduction, with direct reference to the provisions of the new European Union AI Act. For a better understanding of the concept of Artificial Intelligence, we will classify AI systems considering the following criteria: the degree of risk, the algorithms used, the capabilities and functions, and the level of autonomy of the system. The paper highlights key aspects of AI Act regulation, focusing on high-risk and prohibited AI systems. Finally, the penalties for noncompliance with AI Act provisions are briefly outlined

Concept of Artificial Intelligence (AI) and Its Use in Orthopaedic Practice: Applications and Pitfalls: A Narrative Review

Concept of Artificial Intelligence (AI) and Its Use in Orthopaedic Practice: Applications and Pitfalls: A Narrative Review

ОСОБЛИВОСТІ СТВОРЕННЯ ШТУЧНОГО ІНТЕЛЕКТУ ЗАСОБАМИ ІНФОРМАТИКИ ТА КІБЕРНЕТИКИ

Peculiarities are determined, and scopes of application of the methods of informatics and cybernetics are specified based on V.M. Glushkov’s definition of the concept of information. The concept of artificial intelligence is noted to belong to these branches of science. The impossibility of using the traditional theoretical and practical foundations of discretization, quantization, and signal transmission in real-time systems and the need to develop new theoretical foundations based on the measure of changes for cybernetic systems have been proved. A tracking model of analog-incremental transformation of continuous signals with increments, multiple of the degree of 2, and methods of their coordination with the information processing means are proposed. The basis of the dynamic theory of information based on δ-entropy is proposed for excreting useful (dynamic) information from signals, images, videos, iterative processes and, in general, from the data on changes in evolutionary processes. Approaches to the synthesis of new technical solutions based on the method of knowledge increments are proposed. The peculiarities of creating artificial intelligence by the methods of informatics and cybernetics and the need for their synergy with other fields of science are determined. Keywords: computer science, cybernetics, information, artificial intelligence, computational technique, static information, dynamic information, measure of change, δ-entropy, information, knowledge gain, heuristics.

ЭКОНОМИЧЕСКИЙ ОБРАЗ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ГЛАЗАМИ ЖИТЕЛЕЙ РЕСПУБЛИКИ ТАТАРСТАН

This article considers the evolution of approaches to the concept of “artificial intelligence”. The study included the analysis of theoretical approaches to this phenomenon, starting with the founders: J. McCarthy, Stuart Russell and Peter Norvig and ending with the legislative framework in this area. Also, when analyzing the concept of artificial intelligence the functional approach was used, which showed the multitasking of the studied phenomenon and its wide use in many spheres of human activity. To identify the key aspects of the transformation of the concept of artificial intelligence in the eyes of the residents of the Republic of Tatarstan, the methods of questionnaire survey and construction of a collective image were used. The results of the study emphasized the need for reliable popularization of the concept of artificial intelligence. This study was conducted to analyze the public opinion about artificial intelligence, the attitude to it and the associations that arise in the public consciousness

Advancing Consumer Behavior: The Role of Artificial Intelligence Technologies and Knowledge Sharing

The increasing use of digital technologies has significantly reshaped marketing and consumer behavior (CB) as online communities and cutting-edge innovations such as artificial intelligence (AI) disrupt and advance consumer attitudes on specific products and services. As such, online communities that are supported by AI technologies creating new knowledge from consumer interactions through platforms like social media as consumers share experiences on specific products or services. Since AI is designed to “learn” and improve with data generated from digital technologies linked to consumer interactions, AI relies on consumer knowledge-sharing (KS) activities to replicate new knowledge for product and service improvement. However, given the knowledge gap in this area, this article applies the fsQCA technique to data generated from 291 participants to develop CB metaframework predicted on the concepts of AI, CB, and KS. Our results suggest that AI advances consumer attitudes and behaviors when knowledge is acquired while online communities promote curiosity and engage consumers to learn by sharing experiences about specific products or services. Furthermore, understanding the causality between AI, CB, and KS concepts offers critical decision-making insights to marketing experts across the industry.

AI-Based Well-Integrity Monitoring Shows Promise

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 211093, “Development of an Artificial-Intelligence-Based Well-Integrity Monitoring Solution,” by Prihandono Aditama, SPE, Tina Koziol, and Meindert Dillen, Wintershall Dea. The paper has not been peer reviewed. _ This paper presents the proof of concept (PoC) of artificial intelligence (AI)-based well-integrity monitoring for gas-lift, natural-flow, and water-injector wells. AI-model prototypes were built to detect annulus leakage as incident-relevant anomalies from time-series sensor data. The AI models for gas-lift and natural-flow wells achieved a sufficient level of performance, with a minimum of 75% of historical events detected and less than one false positive per month per well. Problem Statement In the exploration and production industry, historical well-integrity events are rare because systems are designed as robustly as possible to prevent incidents. For the authors’ study, there were only 12 historical wellbore leakage incidents spanning 13 years of well operations in the assets considered. The events were treated separately for each well type. This challenge is normally known as anomaly detection and has been studied in various fields where time-series data are used. For a historical incident, two types of anomalies were identified: - Short-term incident relevant anomaly (SIRA), an anomaly occurring within 1 day - Long-term incident relevant anomaly (LIRA), an anomaly lasting for more than 1 day Objective and Success Criteria The primary objective of the PoC was to validate the hypothesis that incident-relevant anomalies from historical well-sensor data can be predicted and monitored by using adequate AI models. A generalized AI model was built so that it would detect a well-annulus leakage in future wells. Success criteria were defined by two approaches: a human-centric approach and a statistical approach. For the human-centric approach, the future users were interviewed to obtain insights about their requirements for tool performance. The feedback was mapped against a confusion matrix. Users preferred to optimize the number of correctly predicted historical incidents (true positives) while achieving an acceptable number of false positives. In general, wrong predictions were seen as acceptable (false positives) to a certain degree, depending on the number of wells under a user’s responsibility. Based on those insights, the AI model was optimized for recall instead of precision. Recall (sometimes known as sensitivity) is defined as the number of true positives divided by the total of true positives and false negatives and measures the miss rate. The tradeoff for choosing this approach means the AI model will report some false alarms.

Artificial Intelligence – Qualification and Competence Development Requirements for Executives

When companies increasingly use artificial intelligence (AI), this changes the world of work and, in particular, the way people and technology work together and divide up their work. Because with the help of AI systems, even cognitively demanding activities can be automated. For example, AI can support executives in their leadership tasks and partially take over administrative coordination and control tasks. Workflows and processes must be redesigned or at least adapted accordingly. This scientific article provides an overview of the necessary changes and presents suitable tools for well-structured change management that is oriented toward human-centered work design and takes AI-specific design criteria into account. Furthermore, the article presents a qualification concept, which helps executives to develop, implement and establish AI applications in a socio-technical and human-oriented manner. With the help of this concept, executives and users of AI, for example, will be able to acquire the necessary competencies. In this context, the results of the practical evaluation of the concept will also be presented.

The application of eXplainable artificial intelligence in studying cognition: A scoping review.

The rapid advancement of artificial intelligence (AI) has sparked renewed discussions on its trustworthiness and the concept of eXplainable AI (XAI). Recent research in neuroscience has emphasized the relevance of XAI in studying cognition. This scoping review aims to identify and analyze various XAI methods used to study the mechanisms and features of cognitive function and dysfunction. In this study, the collected evidence is qualitatively assessed to develop an effective framework for approaching XAI in cognitive neuroscience. Based on the Joanna Briggs Institute and preferred reporting items for systematic reviews and meta-analyses extension for scoping review guidelines, we searched for peer-reviewed articles on MEDLINE, Embase, Web of Science, Cochrane Central Register of Controlled Trials, and Google Scholar. Two reviewers performed data screening, extraction, and thematic analysis in parallel. Twelve eligible experimental studies published in the past decade were included. The results showed that the majority (75%) focused on normal cognitive functions such as perception, social cognition, language, executive function, and memory, while others (25%) examined impaired cognition. The predominant XAI methods employed were intrinsic XAI (58.3%), followed by attribution-based (41.7%) and example-based (8.3%) post hoc methods. Explainability was applied at a local (66.7%) or global (33.3%) scope. The findings, predominantly correlational, were anatomical (83.3%) or nonanatomical (16.7%). In conclusion, while these XAI techniques were lauded for their predictive power, robustness, testability, and plausibility, limitations included oversimplification, confounding factors, and inconsistencies. The reviewed studies showcased the potential of XAI models while acknowledging current challenges in causality and oversimplification, particularly emphasizing the need for reproducibility.

Evolution to revolution: Critical exploration of educators’ perceptions of the impact of Artificial Intelligence (AI) on the teaching and learning process in the GCC region

The transformational nature of the 21st century in all aspects of life has brought unprecedented changes to the teaching and learning process highlighting the paradigm shift and redefining the purpose and delivery of education. Ubiquitous and innovative Artificial Intelligence (AI) tools, being one of the defining (r) evolutions of our modern era accelerated by the recent introduction of free, accessible, and highly upgraded language models, have stirred a debate on the broader implications of AI on education, teaching, learning and assessment. On one hand, the integration of AI in education, particularly, in classroom practices has been seen as a potential solution to pressing challenges and barriers in dismantling industry model of education which is considered to be outdated and misaligned with the 21st century demands. Notwithstanding, the flip side of the coin presents a different picture as to whether AI is a potential threat, rather than a solution, to acquisition of knowledge, academic honesty and integrity, critical thinking skills and teacher-student relationships. Against the backdrop of such critical debate, it is crucially significant to establish a common understanding of the concept and implications of AI through the lens of stakeholder perceptions and expectations. Thus, this study explored the views and perceptions of 11 educators across the GCC on the impact of AI on teaching and learning process focusing on whether AI should be seen as evolution or a revolution in the field of education. The participants were selected based on the set of diverse criteria, like the curriculum setting they teach/manage in, their current position and the country they are based in. The findings revealed that in general, educators are quite optimistic about and open to the use of AI in education, its potential benefits and opportunities to improve teaching and learning, however, they cited lack of understanding and training in using AI tools in classroom instruction, assessment etc. as one of the most important barriers to its use. They also cited socio-cultural reservations, systematic resistance to change, lack of structured policies and available resources as factors inhibiting its use in education in the GCC region. The study concluded that there is a need for structured policy frameworks and guidelines on the use of AI in education in the context of GCC for better integration and migitation of its risks.

Gesture Controlled Virtual Mouse with Voice Assistant

The usage of hand gestures to operate digital devices has grown in recent years, and developing accurate gesture recognition systems using computer vision technology is becoming easier. Voice assistants have also become more common, with developments in artificial intelligence (AI) and natural language processing (NLP) allowing for more accurate voice recognition. Our goal is to create a system which combines both gesture-controlled mouse functionality and voice assistant to execute things that a regular mouse and keyboard can. The GCVA (Gesture Controlled Voice Assistant) comes with a gesture-controlled mouse and a voice assistant. A gesture-controlled mouse uses OpenCV and Media-Pipe, whereas a voice assistant uses AI and NLP concepts to recognize the voice commands given by the user. The system should be used by performing hand gestures and executing the right click, left click, drag, drop, volume control and computer cursor functions, thereby omitting the need for a physical mouse. The success of this system is observed in terms of practicality, stability, and compatibility with physical mechanisms. The voice assistant works and responds to pre-defined commands present in the system. The GCVA system was tested by four different users in order to generate the accuracy scores of mouse gestures and response time graphs for voice assistant. The system yields an accuracy rate of 98%. The system's accuracy and usability showed that hand gesture detection and voice assistants have the potential to replace traditional input devices in the future.

Development trends of the concept of Artificial intelligence

Development trends of the concept of Artificial intelligence

Veštačka inteligencija i totalitarizam

After the introduction, the first part of the paper is devoted to defining the concepts of artificial intelligence and totalitarianism, where the importance of distinguishing between the current (machine learning) and the projected (superintelligence) phase in the development of artificial intelligence, i.e. between the embryonic (totalitarian movement out of power) and the established (totalitarian movement in power) stage in the development of totalitarianism is underlined. The second part of the paper examines the connection between the current level of artificial intelligence and the embryonic phase of totalitarianism, while the third part of the paper analyzes the potential relationship between the superintelligence and the established totalitarianism. It seems, considering the similarities and differences between the effects of contemporary and future artificial intelligence and the effects of earlier totalitarianism, that today (and in the future) we do not have a mere replica of totalitarian phases from the 20th century, but special totalitarian phenomena in the form of "capillary totalitarianism", i.e. "hypertotalitarianism". Last century's totalitarianism, as well as today's "capillary" variant of it, were not necessarily irreversible, but "hypertotalitarianism" will be. In conclusion, protective measures against the risk of artificial intelligence are proposed, in the form of the principle of exemption (modeled after the concept of conscientious objection).

Towards a legal framework for corporate governance amid artificial intelligence.

This paper addresses the procedures to develop a legal framework for corporate governance amid artificial intelligence technology in Jordanian legislation. The analytical and descriptive approach is adopted to analyze the texts of the Jordanian Companies Law No. 22 of 1997, its amendments, and Instructions for the Corporate Governance for Shareholding Listed Companies for the year 2017. Though the Jordanian legislation has established rules and principles of governance, whether, in the corporate law or governance instructions, the Jordanian legislation does not incorporate legal texts that simulate the artificial intelligence into these principles (Yazid, 2016). The results demonstrate that the Jordanian legislation is still in the process of organizing and legalizing the concept of artificial intelligence in the Jordanian Companies Law and the principles of commercial companies’ governance. With that, this article recommends that the Jordanian legislator should amend the law that regulates artificial intelligence issues in the governance of commercial companies to keep up with the development in the era of the digital economy. This would protect commercial companies and those with interests in them by enhancing the principles of transparency, independence, and disclosure, and preserving information security in them according to the artificial intelligence.

Awareness of medical students about the role of artificial intelligence in health care: A survey-based cross-sectional study

Background: Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially by computers. In the recent past, the utility of AI has been tested and proven in many fields like health care, education, finance, defense, etc. AI has received so much attention these days and is described as the new electricity and the fourth industrial revolution. It is anticipated that AI’s role will be crucial and imminent soon in different fields, including the health sector. The medical graduates need to update their knowledge and keep pace with the new developments in technology. Given this, our study was designed to evaluate the awareness and perception of medical students regarding the basic concepts of AI and its implications in health care. Aim and Objective: The aim and objective of the study are to assess the knowledge and perceptions of medical students about AI at a tertiary care teaching hospital. Materials and Methods: The study was a questionnaire-based, cross-sectional study. A prevalidated, self-made questionnaire was distributed to 264 medical students of all professional years through Google Forms. The student’s knowledge, opinions, and perceptions of AI were evaluated. The analysis of the collected data was done with an Excel sheet and using descriptive statistical measures such as mean and percentages. Results: Our study revealed that the majority of the students (79.16%) were under the impression that AI decreases errors in clinical practice. 86% of participants agreed that AI aids doctors in making accurate decisions in the diagnosis and treatment of different conditions. Around 72% of the students said that AI guides the patient to choose the right medical care. Onethird of the participants thought that AI hampers the relationship between patient and doctor by breaking trust between them. A few subsets of participants expressed concerns over ethical issues, confidentiality, empathy, and sympathy, which are considered to be the pillars of medical practice. Conclusions: Our study concluded that medical students at Mamatha Academy of Medical Sciences, Hyderabad, have positive perceptions and good awareness about AI and its use in health-care practice, but they still need a better understanding of AI.

Enhancing Efficiency with an AI-Augmented Clinician in Neurology.

Integrating artificial intelligence (AI) technologies into neurology promises increased patient access, engagement, and quality of care, as well as improved quality of work life for clinicians. While most studies have focused on comparing AI models to expert performance, we argue for a more practical approach: demonstrating how AI can augment clinical practice. This article presents a framework for pragmatic AI augmentation, addressing the shortage in neurology practices, exploring the potential of AI in opportunistic screening, and encouraging the concept of AI serving as a "co-pilot" in neurology. We discuss recommendations for future studies designed to emphasize human-computer collaboration, ensuring AI enhances rather than replaces clinical expertise.

Sustainable Supply Chain Finance and Supply Networks: The Role of Artificial Intelligence

Supply chain finance (SCF) is receiving increasing awareness in research as a result of uncertainties in the global financing for supply chain (SC). There are limited and fragmented studies in the implementations of financial services in SC management. This article builds on recovery from the financial crisis of 2008 and posts COVID-19 pandemic, where uncertainties crippled SCF providers and brokers services. At the same time, cutting-edge technological advancements such as Artificial Intelligence (AI) are revolutionizing the processes of business ecosystem in which SCF is entrenched. This article thus adopts a fuzzy set theoretical approach to unpack the entities relationship validity for sustainable SCF mate-framework, and the originality of AI concepts to sustainable SCF to identify the issues and inefficiencies. The results indicate that AI contributes significant economic opportunities and deliver the most effective utilization of the supply networks. In addition, the article provides a theoretical contribution to financing in SC and broadens the managerial implications in improving performance.

Modeling of Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction Approach

Industrialization and urbanization air is getting polluted due to human activities. CO, NO, C6H6, etc., are the major air pollutants. The focus of air pollutants in ambient air is controlled by the climatological parameters including wind direction, atmospheric speed of wind, temperature, and humidity. Air pollution prediction is a critical sector where machine learning (ML) technique plays a major role. Its main purpose is to tackle and understand the damaging effects of air pollutants on the environment and human health. By using a range of ML techniques such as neural networks, regression, and decision trees, we could analyze historical data on air quality alongside geographical and meteorological factors. This allows us to design model that could detect patterns and predict pollution levels. By taking proactive measures such as providing timely alerts to the public, adjusting controls on emissions, and, implementing strategies to reduce pollution, we can work towards creating healthier and cleaner environments. Embracing the potential of artificial intelligence (AI) in air pollution prediction empowers us to protect the well-being of our communities and make informed decisions. Therefore, this study develops an Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction (ISTSVNI-APP) approach. The major objective of the ISTSVNI-APP technique is to exploit AI concepts with neutrosophic sets (NS) models for the forecasting of air pollution. To do so, the ISTSVNI-APP technique makes use of min-max normalization as the initial preprocessing step. For predicting air pollution, the ISTSVNI-APP technique uses STSVNI approach. To improve the performance of the ISTSVNI-APP technique, modified crow search algorithm (MCSA) is used for the parameter tuning of the STSVNI system. The performance evaluation of the ISTSVNI-APP method is verified utilizing benchmark dataset. The experimental outcomes stated that the ISTSVNI-APP technique gains better performance in predicting air pollution

The authority to prove deepfakes technically and jurisprudentially in artificial intelligence systems

The research aims to demonstrate the authority to prove deepfakes technically and jurisprudentially in artificial intelligence systems, as many people still fear the harm of this development in artificial intelligence, mimicking voice, and image, etc. The research contains the concept of artificial intelligence, details on the method of detecting deepfakes technically, the authority of deepfakes in jurisprudence, and then the result, which is: that ‏probable ‏evidence is considered one of the methods of proof considered in the judiciary in Islam. The means of proof are not limited to a specific number. ‏Therefore, those videos or audio clips that were detected through deepfake tools and whose falsification was not proven are some of the evidence that varies in strength and weakness. That is what the judge and his specialist experts estimate. This means according to the evidence, the authority of rights is proven, or dropped.

Role of Artificial Intelligence in Different Sectors of Society

t was in 1956 that the concept of artificial intelligence came into existence. John McCarthy and Marvin Minsky hosted the Dartmouth Summer Research Project, and here they coined the word “artificial intelligence.” The workshop laid the foundation for the field of artificial intelligence. Back to the current timeline, few studies believe that artificial intelligence will contribute 15.7 trillion dollars to the world economy by the year 2030. A branch of computer science that commands machines to simulate human intelligence, artificial intelligence is bringing a paradigm shift in each sector of society. The smart machines working on the concept of deep learning, are capable of performing tasks and making decisions that only a human mind can. These software systems operate in such a manner that they adapt to the situation. Artificial intelligence has the potential to make society even better by bringing positive changes in healthcare, education, governance, policy formulation and implementation, technology, resource management, innovation, etc. Artificial intelligence holds the potential to make human lives better in thousands of ways. Today, if we look around, we are already using AI in our daily lives in some way or another. Artificial intelligence technology in smartphones has grown rapidly in recent years. Google Assistant, Siri, and Alexa are a few examples of AI personal assistants that make our daily tasks easier. Not only do these solution-driven technologies follow commands, but they also learn from experience to provide better services. Today, artificial intelligence is reshaping and redefining industries. It is bringing about a revolution in technology and impacting the global economy. Artificial intelligence can solve complex problems in a limited amount of time and with enhanced accuracy.