Idea Of Artificial Intelligence Research Articles

Искусственный интеллект как метафора, объект и субъект философского анализа

The article examines epistemological, ethical, anthropological, and other aspects of understanding self-learning programs called “neural networks” or “artificial intelligence”. The problematic nature of the latter is addressed in the research by reviewing three dozen studies of the idea of artificial intelligence, which were presented on April 4, 2024, at a joint scientific conference of the Center for Phe­nomenological Philosophy of the Russian State University for the Humanities and the Sector of Social Epistemology of the Institute of Philosophy, Russian Academy of Sciences. Based on the materials of the conference, this article examines artificial intelligence from the perspective of the history of philosophy and futurist literary images in comparison with human and other conceivable consciousness, both possessing free will as well as lacking it. Another aspect of this research provides an understanding of the problem areas of the theology of artificial intelligence and the place of artificial intelligence in modern theo­logy, as well as providing an analysis of the technical side of the implementation of existing and expected computer systems claiming to be “artificial intelli­gence”. The conference proved to be an important event for discussing philo­sophical and ethical issues related to the development of artificial intelligence and provided a platform for further research and understanding of the impact of AI on society and human individuality. This article outlines the general per­spective of the conference and, more specifically, selected theses

Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation

There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or memes. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences.The main goal of this paper is to unravel the specific nuances of such influence, determining which AI ideas are predominantly driven by Big Tech entities. By employing network and memetic analysis on AI-oriented paper abstracts and their citation network, we are able to grasp a deeper insight into this phenomenon. By utilizing two databases: OpenAlex and S2ORC, we are able to perform such analysis on a much bigger scale than previous attempts.Our findings suggest that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia. Focusing on the most contagious memes, their attribution to specific affiliation groups (Big Tech, Academia, mixed affiliation) seems equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.

Artificial Intelligence in Healthcare 5.0: Strengthening Practices for Medicos

Abstract: From the beginning, the Healthcare sector stood a pioneer for the development of Artificial Intelligence technology. Numerous research and discussion have been conducted till today based on the idea of Artificial Intelligence. This is because of the nature of the amenities and the susceptibility of ample portion of consumers. In the present study, a blended approach both qualitative and quantitative has been applied to determine the constituents of Artificial Intelligence for healthcare industry and analysis is conducted to find its effect on value formation along with market performance. By analysis of the patient perspective, it revealed the ways in which different Artificial Intelligence components contribute to healthcare organizations and provide improved patient-centered health care.

Generative AI enhances individual creativity but reduces the collective diversity of novel content.

Creativity is core to being human. Generative artificial intelligence (AI)-including powerful large language models (LLMs)-holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity.

Maximizing the Average Environmental Benefit of a Fleet of Drones under a Periodic Schedule of Tasks

Unmanned aerial vehicles (UAVs, drones) are not just a technological achievement based on modern ideas of artificial intelligence; they also provide a sustainable solution for green technologies in logistics, transport, and material handling. In particular, using battery-powered UAVs to transport products can significantly decrease energy and fuel expenses, reduce environmental pollution, and improve the efficiency of clean technologies through improved energy-saving efficiency. We consider the problem of maximizing the average environmental benefit of a fleet of drones given a periodic schedule of tasks performed by the fleet of vehicles. To solve the problem efficiently, we formulate it as an optimization problem on an infinite periodic graph and reduce it to a special type of parametric assignment problem. We exactly solve the problem under consideration in O(n3) time, where n is the number of flights performed by UAVs.

Breast cancer prediction based on the machine learning algorithm LightGBM

Nowadays, the idea of Artificial Intelligence (AI) medical detection has aroused great interest around the world. AI has the potential to strengthen medicine in both observation and operation. For instance, AI could catch crucial details that are not intuitive to humans. Robots controlled by AI could also do micro-operations that are extremely hard on human hands. In this study, the author utilizes one of the most focused traditional machine-learning methods, that is the Light Gradient Boosting Machine (LightGBM) algorithm for breast cancer prediction. The LightGBM performs both well on accuracy and speed in the studys experiment. The study applies the bootstrap aggregating (Bagging) method to cope with the over-fitting problem. As the significance of the study, the study shows that the LightGBM can be utilized in designing accurate, fast and cheap medical detection devices. Nevertheless, programmers should handle the over-fitting problem cautiously while building models based on LightGBM. This could help doctors in impoverished areas realize accurate medical detection. People could also do accurate self-diagnosing with a cheap, portable device at home.

Key Research Ideas of Artificial Intelligence in Higher Education: Bibliometric Analysis and Information Mapping

This research aims to describe and map information and research ideas that can be developed in the application of artificial intelligence in higher education. The method applied in this research is Preferred Reporting Items For Systematic Reviews And Meta-Analyses (PRISMA) which uses scientific articles or proceedings sourced from the Scopus database which are analyzed using Vos Viewer and Publish Or Perish as well as Google Trends and Dimensions. The research results reveal that artificial intelligence research covers three aspects that can be applied in higher education: development of AI tools, application of AI in learning, and digitalization of higher education.

The performance of artificial intelligence in medical field

In modern society, there is an increasing prevalence of individuals who are able to avail themselves of medical facilities for the purpose of receiving healthcare services. With the rise in population, there has been a corresponding decrease in the availability of medical resources for some disorders, leading to challenges in accurately diagnosing patients by healthcare professionals. According to specialists, artificial intelligence (AI) is being considered as a potential solution for addressing medical challenges. This paper mainly focuses on discussing the impact of artificial intelligence in the medical field. Through methods of literature review and analysis, this study explores the fundamental idea of AI and its use in the medical field. Besides, the paper also introduces the potential flaws behind AI in the medical field, and how will artificial intelligence help us further in the medical field. The study reveals that artificial intelligence is extensively employed within the medical sector. Through extensive training, AI has the potential to attain a considerable level of accuracy when it comes to diagnosing various ailments. The level of precision exhibited is akin to that observed in medical practitioners diagnoses. Nevertheless, artificial intelligence possesses certain limitations. For instance, in the context of privacy preservation, several patients exhibit a reluctance to divulge their symptoms. In the absence of adequate safeguards for information security, this situation might potentially lead to adverse consequences in the lives of these patients and their interpersonal interactions. Ultimately, AI continues to possess significant potential for advancement within the realm of medicine.

Artificial Intelligence in higher education: opportunities and challenges

The article reveals the meaning of the concept of artificial intelligence and proves the necessity of its application in the educational space. The purpose of the article is to show the importance of artificial intelligence as an educational and entertainment tool in higher education. The methodological concept is aimed at ensuring the effectiveness of students' education with the help of artificial intelligence. The research results show the types (weak and strong), directions, functions of artificial intelligence; the importance of edutainment in higher education and its main principles are revealed. Artificial intelligence is presented as a toolkit of edutainment in higher education and the importance of using artificial intelligence functions for edutainment in US higher education to improve human skills and abilities is shown. The significance of the avatar and virtual teacher in the educational process is revealed. During the research and experimental work, we singled out the leading factors of the actualization of the ideas of artificial intelligence as an educational and entertainment toolkit in the modern theory and practice of training students of higher education.

Advanced neuromonitoring powered by ICM+ and its place in the Brand New AI World, reflections at the 20th anniversary boundary

IntroductionAdoption of the ICM+® brain monitoring software by clinical research centres worldwide has been continuously growing over the past 20 years. This has necessitated ongoing updates to accommodate evolving neuromonitoring research needs, including recent explosion of artificial intelligence (AI). Research questionWe sought to provide an update on the current features of the software. In particular, we aimed to highlight the new options of integrating AI models. Material and methodsWe reviewed all currently available ICM+ analytical areas and discussed potential AI based extensions in each. We tested a proof-of-concept integration of an AI model and evaluated its performance for real-time data processing. ResultsICM+ current analytical tools serve both real-time (bed-side) and offline (file based) analysis, including the calculation engine, Signal Calculator, Custom Statistics, Batch tools, ScriptLab and charting. The ICM+ Python plugin engine allows to execute custom Python scripts and take advantage of complex AI frameworks. For the proof-of-concept, we used a neural network convolutional model with 207,000 trainable parameters that classifies morphology of intracranial pressure (ICP) pulse waveform into 5 pulse categories (normal to pathological plus artefactual). When evaluated within ICM+ plugin script on a Windows 10 laptop the classification of a 5 min ICP waveform segment took only 0.19s with a 2.3s of initial, one-off, model loading time required. ConclusionsModernised ICM+ analytical tools, reviewed in this manuscript, include integration of custom AI models allowing them to be shared and run in real-time, facilitating rapid prototyping and validating of new AI ideas at the bed-side.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ: ДРАЙВЕР ЭКОНОМИЧЕСКОГО РАЗВИТИЯ ИЛИ ГЕНЕРАТОР ПРОБЛЕМ И УГРОЗ?

The article examines the problems of the development of artificial intelligence at the pre-sent stage and emphasizes the need to develop scientific research in all areas of intelligence: human, biological and artificial. The purpose of the study is to identify the main problems and limitations of the current level of artificial intelligence development. The relevance of the problem under consid-eration lies in the fact that the work analyzes a set of problems that determine the factors of the development and use of artificial intelligence, since the idea of artificial intelligence arose among people relatively recently and this understanding is rapidly developing. The term “artificial intelli-gence” was proposed by John McCarthy in 1956 at Dortmund College at aconference on the “mecha-nization of intelligence”. Changes in society related to the development of AI have been happening and are happening constantly and, judging by the basis on which AI is developing, will never stop, since its development is completely based on the capabilities of the human mind, and we cannot determine its potential even approximately. Currently, AI is penetrating almost all areas of human activity and will dominate them. The future of AI is limitless and depends only on its creators and users. The transformations that the use of artificial intelligence brings often radically change our daily lives and relationships. The ongoing changes contain both undoubted significant positive ef-fects that greatly facilitate the solution of various problems, as well as pitfalls that manifest them-selves in various aspects: starting from imperfections of applied technical solutions and ending in ethical and social areas, which result not only in current technical, technological, and social prob-lems, but also potentially significant threats that pose the danger of human existence itself. The article shows that there is a need to clarify some concepts of AI, to determine the direction of AI development. Research in the work has shown that developments are conducted mainly in the field of “weak” artificial intelligence. They are mainly focused on commercialization and early implemen-tation of practical results. In this regard, the authors note the need to strengthen fundamental and exploratory research on the properties of intelligence and the creation of scientific foundations for the practical implementation of “strong” AI systems. In the long run, AI, as a tool for optimizing and accelerating various processes, has a future that promises its maximum possible use and develop-ment in attempts to achieve, and eventually try to surpass human intelligence. In this regard, it is necessary to provide for possible positive and negative consequences, to determine the boundaries and direction of the use of artificialintelligence so that people do not become victims of unsuccess-ful developments, and humanity can benefit from this invention.

Integrating artificial intelligence into science lessons: teachers’ experiences and views

BackgroundIn the midst of digital transformation, schools are transforming their classrooms as they prepare students for a world increasingly automated by new technologies, including artificial intelligence (AI). During curricular implementation, it has not made sense to teachers to teach AI as a stand-alone subject as it is not a traditional discipline in schools. As such, subject matter teachers may need to take on the responsibility of integrating AI content into discipline-based lessons to help students make connections and see its relevance rather than present AI as separate content. This paper reports on a study that piloted a new lesson package in science classrooms to introduce students to the idea of AI. Specifically, the AI-integrated science lesson package, designed by the research team, provided an extended activity that used the same context as an existing lesson activity. Three science teachers from different schools piloted the lesson package with small groups of students and provided feedback on the materials and implementation.FindingsThe findings revealed the teachers’ perceptions of integrating AI into science lessons in terms of the connection between AI and science, challenges when implementing the AI lesson package and recommendations on improvements. First, the teachers perceived that AI and science have similarities in developing accurate models with quality data and using simplified reasoning, while they thought that AI and science play complementary roles when solving scientific problems. Second, the teachers thought that the biggest challenge in implementing the lesson package was a lack of confidence in content mastery, while the package would be challenging to get buy-in from teachers regarding curriculum adaptation and targeting the appropriate audience. Considering these challenges, they recommended that comprehensive AI resources be provided to teachers, while this package can be employed for science enrichment programs after-school.ConclusionsThe study has implications for curriculum writers who design lesson packages that introduce AI in science classrooms and for science teachers who wish to contribute to the development of AI literacy for teachers and the extension of the range of school science and STEM to students.

Machine learning in AI Factories – five theses for developing, managing and maintaining data-driven artificial intelligence at large scale

Abstract The use of artificial intelligence (AI) is today’s dominating technological trend across all industries. With the maturing of deep learning and other data-driven techniques, AI has over the last decade become an essential component for an increasing number of products and services. In parallel to this development, technological advances have been accelerating the production of novel AI models from large-scale datasets. This global phenomenon has been driving the need for an efficient industrialized approach to develop, manage and maintain AI models at large scale. Such an approach is provided by the state-of-the-art operational concept termed AI Factory, which refers to an infrastructure for AI models and implements the idea of AI as a Service (AIaaS). Moreover, it ensures performance, transparency and reproducibility of AI models at any point in the continuous AI development process. This concept, however, does not only require new technologies and architectures, but also new job roles. Here, we discuss current trends, outline requirements and identify success factors for AI Factories. We conclude with recommendations for their successful use in practice as well as perspectives on future developments.

Artificial Intelligence and Health Care: What's to Worry?

Artificial Intelligence and Health Care: What's to Worry?

AI in Retail Industry: Reshaping Shopping Experience and Business Profitability

Abstract
 Artificial Intelligence (AI) is depicted as the actions of machines doing tasks that usually require human intelligence. Though this idea of Artificial Intelligence can be traced back to Greek mythology, it was only during modern history, the world witnessed the development of programme-driven computer machines. Now it has become a web of millions of codes and algorithms and is increasingly playing an integral role in every industry sector. E-commerce Industry had once disrupted the age-old traditional retail industry, especially in India where the retailers now largely depend on these platforms for their product’s sale. Today it's coming in the form of Artificial Intelligence which will be a great advantage for the retail industry. To be competitive today, retailers need to respond to their customers like never before, at the same time reducing the amount of waste and inefficiency in their operations. They can get there with data, but it takes intelligent analysis to make sense of the vast amount of data. This article analyses the emerging trends and practices of using Artificial Intelligence in retail industry in order to enhance customer experiences and result in value additions to retail businesses.
 Keywords: Artificial Intelligence, Retail Business, Digital Retailing, Smart Retailing

An overview of artificial intelligence use in diabetic retinopathy treatment: a narrative review

Background: Artificial intelligence (AI), usually called machine intelligence, is a scientific field that allows robots thinking like people. The most common diseases that cause visual impairment and blindness, such as age-related macular degeneration (ARMD), cataract, glaucoma, and diabetic retinopathy (DR), have recently been subject to deep learning-based on AI screening and prediction models. Aim: to discuss the core ideas of AI and its apply to DR, as well as to evaluate the currently faced issues and the future of ophthalmology. Methods: In this review, English studies from common databases such as Pubmed/MEDLINE, Google Scholar, Web of Science, Scopus, and the Cochrane Library with the keywords "Machine Learning," "Artificial Intelligence," "Deep Learning," combined with keywords, involving "diabetic retinopathy" were involved. The end date for this review is April 2022. Scientific novelty: This paper illustrates the core AI concepts and their application in diabetic retinopathy. The current ophthalmology issues and future opportunities, offering undiscovered knowledge in this field are also analysed. It will raise community knowledge of employing AI and reveal new capabilities in the analysis of ocular disorders to present the fundamental idea of AI regarding its therapeutic applications. Conclusion: Medical professionals can make quick and precise decisions using AI technologies in orded to analyse massive volumes of data, such as physiological imaging and clinical presentations. It is believed that over time, AI systems will become more precise and successful at predicting the onset and course of DR.

Research Progress on the Impact of Artificial Intelligence on China's Economy

The information age is dawning, as science and technology advance at a rapid rate, ushering us into a new epoch. The Internet's leadership of new media has not only enhanced people's quality of life, but also broadened the pathways for people to acquire, spread, exchange, and process information, thus making our lives more convenient and productive. The swift advancement of science and technology has made artificial intelligence an indispensable element of our current society. It not only affects our daily life, but also profoundly affects our creativity and social development. The steam engine revolution has been followed by artificial intelligence, a scientific revolution that will surely spur future progress. Consequently, the utilization of this potent instrument to foster China's economic expansion is of immense and far-reaching importance to be examined. Beginning with the definition of artificial intelligence, this paper delves into the fundamental ideas of artificial intelligence and its associated theoretical foundation. Subsequently, a thorough examination of the primary applications of artificial intelligence and its essential part is conducted. Thirdly, by reviewing the three industrial revolutions in history, it summarizes that each industrial revolution will bring about different degrees of industrial structure adjustment, which will lead to changes in labor demand and eventually lead to changes in employment. It is proposed that, in light of the current state of China's economy, we should make full utilization of artificial intelligence technology to foster the sustained, healthy, and rapid growth of the country.

AIYOJAN–An AI based University Management Platform

Abstract: With the onset of the technological revolution, the efficiency of the current manual systems has been improved drastically and the accuracy of the products produced are also increasing. The most affected of the fields from this technological change is Education. The combination of Education with technology has been coined as a new term of edtech. But even in these times Due to the autonomy given to then Indian Universities, the institutions have developed their own annual academic calendars. The students wanting to get admissions in other institutions need to wait for the results of the previous course. Students of those Institutions who complete their examinations and get results early have the opportunity to select the course of their choice and move to the University of choice while those whose results are declared late have no (Or) less options of courses and colleges available for them. Students also have to travel to each of these colleges to get information, apply and get admitted and so on. This results in huge cost and loss of time to students in their academic career. In order to provide level playing grounds for all the students there is a need for effective implementation of a common uniform calendar in all the colleges and Universities across the Nation. From studying various research paper, we realized that if we could introduce a central academic calendar and combine it with the idea of Artificial Intelligence for best date prediction, we could improve the efficiency of University admissions. Thus we aim to create a centralized calendar organization system which will in turn help universities become more disciplined and the students will get a more clear view and an equal opportunity for admissions to good colleges. This will save money, time, and increase the present efficiency of the system. In this project, we have studied different uses of calendars and how we can improve the efficiency of the calendars using AI and other algorithms. In this project we also aim to highlight the problems faced by students and the possible solution which could be designed to overcome the problem

How ready is artificial intelligence (AI) for clinical use?

AbstractPurpose: Use of Artificial Intelligence (AI) as a diagnostic tool or clinical decision support tool has been investigated extensively. The purpose of this study is to review the literature to date, the number and type of systems that are already available.Methods: A search of MEDLINE, Embase, Cochrane Review, and Global Health Library (2000–2022) was performed. Key words used were Ophthalmology AND AI OR Clinical Assist OR Decision Support Tools. Inclusion criteria were articles written in the English language and studies with human participants. Exclusion criteria were editorials, articles using animal models, and pure validation studies.Results: 11 557 papers when ‘Ophthalmology and AI’ filtered applied, and 906 when ‘Decision Support Tool’ is specified. 387 titles filtered for inclusion/exclusion criteria, of which 26 working tools were identified, (Cataract 4, Cornea and refraction 3, Melanoma 2, Oculoplastic 3, Paediatric Ophthalmology 1, Triage 2, Glaucoma and Neuro‐Ophthalmology 4, Retina 7). Of these, 6 are available for clinical use.Conclusions: Out of a large number of studies, we find 6 examples of Diagnostic or Decision support tools ready for implementation. Two are diagnostic, based on input of risk factors alone, and the others use image analysis for disease management and screening. Most AI work is retrospective and observational in nature, with most trials in medical retinal diseases. Further work is needed to translate AI ideas into clinical practice.

Awareness about the future of artificial intelligence in healthcare among medical students in Kancheepuram District, Tamil Nadu

Introduction: Artificial Intelligence (AI) is expected to influence and has potential to improve various fields in medical care. They have capability of rapidly diagnosing various medical conditions and improve he treatment outcomes in various specializations. The study was done to find out the awareness of AI in Healthcare among medical students in Kancheepuram district, Tamil Nadu. Methodology: This is a descriptive cross-sectional study conducted among 100 medical students studying in a private medical college selected by simple random sampling. A pretested semi-structured questionnaire was used for data collection regarding the awareness of AI in Healthcare. Data was entered in Microsoft excel and analyzed by SPSS version 22. Results: Around 85% of the study participants were aware about Artificial Intelligence and 98% were about the attitude that it could improve healthcare. It was found that 82% of the study participants welcomed the idea of AI being used in Healthcare, while 7% were least accepting. The study participants were of the belief that the medical department which could benefit the most from use of AI were Radiology (38%) and Oncology (30%).