Current Artificial Intelligence Research Articles

ARTIFICIAL INTELLIGENCE ENHANCED IDENTITY AND ACCESS MANAGEMENT PREVENTING UNAUTHORIZED ACCESS IN MODERN ENTERPRISES

As enterprises increasingly migrate to digital platforms, securing systems from unauthorized access becomes crucial to safeguarding sensitive data. Identity and Access Management (IAM) systems have traditionally been implemented to manage user access and authenticate identities. However, with growing threats of sophisticated cyber-attacks, traditional IAM methods are proving inadequate. Artificial Intelligence (AI) has emerged as a transformative force in enhancing IAM systems by providing adaptive, intelligent solutions to prevent unauthorized access. This paper explores the role of AI-enhanced IAM in modern enterprises, emphasizing its ability to predict, detect, and respond to potential security breaches. Through a detailed examination of current AI applications in IAM, this study demonstrates how AI-enabled tools can increase security resilience, reduce false positives, and automate responses to evolving threats, thus offering enterprises a proactive defense against unauthorized access.

Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy

There is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing research on K-12 AI education relies on experiential methodologies and suffers from a lack of quantitative analysis based on extensive classroom data, hindering a comprehensive depiction of AI education's current state at these educational levels. To address this gap, this article employs the advanced semantic understanding capabilities of large language models (LLMs) to create an intelligent analysis framework that identifies learning theories, pedagogical approaches, learning tools, and levels of AI literacy in AI classroom instruction. Compared with the results of manual analysis, analysis based on LLMs can achieve more than 90% consistency. Our findings, based on the analysis of 98 classroom instruction videos in central Chinese cities, reveal that current AI classroom instruction insufficiently foster AI literacy, with only 35.71% addressing higher-level skills such as evaluating and creating AI. AI ethics are even less commonly addressed, featured in just 5.1% of classroom instruction. We classified AI classroom instruction into three categories: conceptual (50%), heuristic (18.37%), and experimental (31.63%). Correlation analysis suggests a significant relationship between the adoption of pedagogical approaches and the development of advanced AI literacy. Specifically, integrating Project-based/Problem-based learning (PBL) with Collaborative learning appears effective in cultivating the capacity to evaluate and create AI.

AI Utilization in Communication Buildings and Data Centers

This paper examines the application of Artificial Intelligence (AI) in communication buildings and data centers, emphasizing its role in enhancing operational efficiency and reliability. AI technologies, including machine learning, predictive analytics, and automation, are increasingly leveraged to optimize the management of these critical infrastructures. The paper provides an overview of current AI implementations, reviews the benefits and challenges associated with these technologies, and discusses future directions. The findings indicate that while AI significantly improves system performance and resource utilization, challenges such as data security and integration complexity persist.

Is Artificial Intelligence (AI) currently able to provide evidence-based scientific responses on methods that can improve the outcomes of embryo transfers? No.

The rapid development of Artificial Intelligence (AI) has raised questions about its potential uses in different sectors of everyday life. Specifically in medicine, the question arose whether chatbots could be used as tools for clinical decision-making or patients' and physicians' education. To answer this question in the context of fertility, we conducted a test to determine whether current AI platforms can provide evidence-based responses regarding methods that can improve the outcomes of embryo transfers. We asked nine popular chatbots to write a 300-word scientific essay, outlining scientific methods that improve embryo transfer outcomes. We then gathered the responses and extracted the methods suggested by each chatbot. Out of a total of 43 recommendations, which could be grouped into 19 similar categories, only 3/19 (15.8%) were evidence-based practices, those being "ultrasound-guided embryo transfer" in 7/9 (77.8%) chatbots, "single embryo transfer" in 4/9 (44.4%) and "use of a soft catheter" in 2/9 (22.2%), whereas some controversial responses like "preimplantation genetic testing" appeared frequently (6/9 chatbots; 66.7%), along with other debatable recommendations like "endometrial receptivity assay", "assisted hatching" and "time-lapse incubator". Our results suggest that AI is not yet in a position to give evidence-based recommendations in the field of fertility, particularly concerning embryo transfer, since the vast majority of responses consisted of scientifically unsupported recommendations. As such, both patients and physicians should be wary of guiding care based on chatbot recommendations in infertility. Chatbot results might improve with time especially if trained from validated medical databases; however, this will have to be scientifically checked.

Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems.

Multi-agent, human–agent and beyond: A survey on cooperation in social dilemmas

The study of cooperation within social dilemmas has long been a fundamental topic across various disciplines, including computer science and social science. Recent advancements in Artificial Intelligence (AI) have significantly reshaped this field, offering fresh insights into understanding and enhancing cooperation. This survey examines three key areas at the intersection of AI and cooperation in social dilemmas. First, focusing on multi-agent cooperation, we review the intrinsic and external motivations that support cooperation among rational agents, and the methods employed to develop effective strategies against diverse opponents. Second, looking into human–agent cooperation, we discuss the current AI algorithms for cooperating with humans and the human biases towards AI agents. Third, we review the emergent field of leveraging AI agents to enhance cooperation among humans. We conclude by discussing future research avenues, such as using large language models, establishing unified theoretical frameworks, revisiting existing theories of human cooperation, and exploring multiple real-world applications.

The use of artificial intelligence for graduate nursing education: An educational evaluation.

With artificial intelligence (AI) rapidly advancing, advanced practice nurses must understand and use it responsibly. Here, we describe an assignment in which Doctor of Nursing Practice (DNP) students learned to use generative text AI. Using our program and course outcomes, developed from the 2021 American Association of Colleges of Nursing (AACN) Essentials competency for DNP students to learn and use AI, we reviewed the literature seeking examples using ChatGPT for the DNP informatics course. No published examples existed to guide us toward infusing a ChatGPT assignment into the course. We developed a novel assignment that included a guide for students on how to use ChatGPT. Students were given time before the assignment to learn the AI/chatbot technology. They were then given the assignment and grading rubric. The assignment was to develop a tool for their current or future practice using ChatGPT. During the course faculty debrief, we learned that few students had questions and the assignment was clear. We also learned that students who sought to develop straightforward, uncomplicated patient tools succeeded with the technology. Those who sought to create something for complex patients had more challenges. Nursing education and practice will be influenced by the increasing prevalence of AI. This manuscript outlines an AI-based assignment for graduate nursing education intended for the students to become familiar with current AI and best practices for patient care. The assignment was well received by students. We plan to use it again in the next course offering.

Artificial Intelligence for Diagnosis in Otologic Patients: Is It Ready to Be Your Doctor?

Investigate the precision of language-model artificial intelligence (AI) in diagnosing conditions by contrasting its predictions with diagnoses made by board-certified otologic/neurotologic surgeons using patient-described symptoms. Prospective cohort study. Tertiary care center. One hundred adults participated in the study. These included new patients or established patients returning with new symptoms. Individuals were excluded if they could not provide a written description of their symptoms. Summaries of the patient's symptoms were supplied to three publicly available AI platforms: Chat GPT 4.0, Google Bard, and WebMD "Symptom Checker." This study evaluates the accuracy of three distinct AI platforms in diagnosing otologic conditions by comparing AI results with the diagnosis determined by a neurotologist with the same information provided to the AI platforms and again after a complete history and physical examination. The study includes 100 patients (52 men and 48 women; average age of 59.2 yr). Fleiss' kappa between AI and the physician is -0.103 (p < 0.01). The chi-squared test between AI and the physician is χ2 = 12.95 (df = 2; p < 0.001). Fleiss' kappa between AI models is 0.409. Diagnostic accuracies are 22.45, 12.24, and 5.10% for ChatGPT 4.0, Google Bard, and WebMD, respectively. Contemporary language-model AI platforms can generate extensive differential diagnoses with limited data input. However, doctors can refine these diagnoses through focused history-taking, physical examinations, and clinical experience-skills that current AI platforms lack.

The Anticipated Impact of Artificial Intelligence on Higher Education

Since the rise of generative AI (GenAI) in late 2022, many scholars and thought leaders have wondered about its impact on higher education. This study used a survey methodology (three multiple choice questions and one open-ended question) to explore the perspectives of a nationally representative sample of 1327 US administrators and faculty, asking questions to understand how much change they anticipate as a result of advancements in artificial intelligence (AI) technology, how prepared their institution is for such change, and what aspects of higher education they expect to change. The researchers used Kranzberg’s laws of technology as a lens to interpret the findings and guide the subsequent discussion about how AI might impact higher education. The findings showed that the vast majority of participants expect that AI will change their institution over the next five years and that the majority of participants do not feel that their institution is ready for change. The comments left in response to the open-ended questions fell into one of four themes: concerns about academic integrity and rigor, issues related to AI integration (e.g., anticipated benefits, practices in teaching and learning, issues related to preparedness, and the expected scope of change), the feeling that the current AI discourse is merely hype, and feelings of uncertainty. Ultimately, AI has the potential to be both advantageous and disadvantageous to teaching and learning, with the benefits and challenges of its use varying by context.

행정에서 인공지능의 활용에 있어서 행정법적 문제과 개혁과제

In order to achieve a fair, efficient, rapid and predictable exercise of state power, it is necessary to prepare for the emergence of a “government for the people, using artificial intelligence, for the people,” which has jumped from a “government for the people, by the people, for the people” by accepting advanced science and technology and reflecting the changed social image, so it is necessary to review the active use of artificial intelligence within the framework of the rule of law administration, check administrative law, and improve legislation. Analysis of the use of artificial intelligence in administrative law does not stop at deriving the current law's interpretation or legislative tasks for the use of artificial intelligence under the Administrative Procedure Act or limited to administrative disposition, but also checks the public law interpretation and response to the use of artificial intelligence in all “administrations” subject to administrative law. Criticism or problem consciousness of current artificial intelligence mainly deals with information opacity, process inexplicability, non-decipherability, information contamination, and intentional distortion of information in the deep learning stage and the use of generative artificial intelligence. The general legislative attitude of the EU and others has attempted to establish a legislative safety net to prevent the emergence of market economy monsters using artificial intelligence, but it should be taken into account that policy judgment and normative consideration are needed to prepare for the emergence of information monsters at the same level as public and private areas, and discussions on risks are focused on deep learning-based artificial intelligence. The EU-type behavioral regulation model regulates general behavior in the use of artificial intelligence regardless of the public and private areas, but private economic entities have no democratic legitimacy in the organizational composition and pursue the maximum of private interests by nature. It is overlooked that the administrative entity, which is the public authority, has democratic legitimacy in the composition, and that the possibility of stakeholder participation in administrative processes and specific administrative procedures is guaranteed, as well as checks by the principle of separation of powers. If the act regulation law in the use of artificial intelligence by private economic entities is brought into the public domain, it leads to overregulation in the public domain, but underregulation for private economic entities occurs. Data is the resource of the future and the oil of the future in the age of artificial intelligence. The data must be managed publicly and the public's free and stable use must be guaranteed if the public property of the data is recognized and pseudonymized information is released from the personal personal rights and ownership of the data. A department in charge of creating, collecting, and managing big data at the national level will be essential. Hangeul data, with the originality of Hangeul, needs to approach the protection of Hangeul resources and the development of Hangul-specific artificial intelligence in order to protect national interests. It is necessary to actively utilize artificial intelligence to increase the objectivity and rationality of evaluation in evaluating business feasibility or predicting future demand in various private capital attraction projects and public development projects, and it is necessary to improve the legal system that detects the misappropriation and overcharges of public funds, imposes punitive fines, and the use of artificial intelligence in the process of selecting subjects and verifying them through post-mortem review in the public benefit area.

Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.

With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image-assisted diagnosis system in clinical treatment is a major trend in medical development. Therefore, in order to explore the value and diagnostic accuracy of the current AI system in clinical application, we aim to compare the detection and differentiation of benign and malignant pulmonary nodules between AI system and physicians, so as to provide a theoretical basis for clinical application. Our study encompassed a cohort of 23 336 patients who underwent chest low-dose spiral CT screening for lung cancer at the Health Management Center of West China Hospital. We conducted a comparative analysis between AI-assisted reading and manual interpretation, focusing on the detection and differentiation of benign and malignant pulmonary nodules. The AI-assisted reading exhibited a significantly higher screening positive rate and probability of diagnosing malignant pulmonary nodules compared with manual interpretation (p < 0.001). Moreover, AI scanning demonstrated a markedly superior detection rate of malignant pulmonary nodules compared with manual scanning (97.2% vs. 86.4%, p < 0.001). Additionally, the lung cancer detection rate was substantially higher in the AI reading group compared with the manual reading group (98.9% vs. 90.3%, p < 0.001). Our findings underscore the superior screening positive rate and lung cancer detection rate achieved through AI-assisted reading compared with manual interpretation. Thus, AI exhibits considerable potential as an adjunctive tool in lung cancer screening within clinical practice settings.

Overview of medical analysis capabilities in radiology of current Artificial Intelligence models

Overview of medical analysis capabilities in radiology of current Artificial Intelligence models

Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review.

Background and Objective: Gastritis represents one of the most prevalent gastrointestinal diseases and has a multifactorial etiology, many forms of manifestation, and various symptoms. Diagnosis of gastritis is made based on clinical, endoscopic, and histological criteria, and although it is a thorough process, many cases are misdiagnosed or overlooked. This systematic review aims to provide an extensive overview of current artificial intelligence (AI) applications in gastritis diagnosis and evaluate the precision of these systems. This evaluation could highlight the role of AI as a helpful and useful tool in facilitating timely and accurate diagnoses, which in turn could improve patient outcomes. Methods: We have conducted an extensive and comprehensive literature search of PubMed, Scopus, and Web of Science, including studies published until July 2024. Results: Despite variations in study design, participant numbers and characteristics, and outcome measures, our observations suggest that implementing an AI automatic diagnostic tool into clinical practice is currently feasible, with the current systems achieving high levels of accuracy, sensitivity, and specificity. Our findings indicate that AI outperformed human experts in most studies, with multiple studies exhibiting an accuracy of over 90% for AI compared to human experts. These results highlight the significant potential of AI to enhance diagnostic accuracy and efficiency in gastroenterology. Conclusions: AI-based technologies can now automatically diagnose using images provided by gastroscopy, digital pathology, and radiology imaging. Deep learning models exhibited high levels of accuracy, sensitivity, and specificity while assessing the diagnosis, staging, and risk of neoplasia for different types of gastritis, results that are superior to those of human experts in most studies.

LASIK Versus PRK Based on Increased Risk of Corneal Haze: Assessing Current Decision-Making Capabilities of Six Artificial Intelligence Models in Refractive Surgery.

To investigate the current decision-making capabilities of 6 different artificial intelligence (AI) models by assessing their refractive surgery recommendations (laser in-situ keratomileusis [LASIK] or photorefractive keratectomy [PRK]) for a theoretical patient with a history of keloid formation. Claude-2 (Anthropic, 2023), GPT-4 (OpenAI, 2023), GPT-3.5 (OpenAI, 2022), Gemini 1.0 (Google DeepMind, 2023), Microsoft Copilot (Microsoft AI, 2023), and Google-PaLM (Google AI, 2022) underwent three systematic queries to determine the most appropriate surgical plan (LASIK or PRK) for a theoretical patient with an increasing manifest refraction of -3.50, -5.00, and -7.00 diopters (D) in both eyes, an uncomplicated ocular examination, and history of keloid formation. They were then tasked with providing published scientific references to support their responses. The AI models' recommendations were compared to those of a group of 6 experienced ophthalmologists, serving as a benchmark. The group of ophthalmologists unanimously recommended LASIK (6/6 ophthalmologists), in contrast to the unanimous initial recommendation for PRK from the AI models (6/6 models). Of the 42 references provided by the AI models, 55% were fictitious and 45% were authentic. Only 1 of the 6 models altered its initial recommendation to LASIK when presented with the same patient with a history of keloid formation but with increasing severity of myopia (-3.50 to 5.00 to 7.00 D). It is evident that current AI models lack the critical-thinking abilities required to accurately analyze and assess apparent risk factors in clinical scenarios, such as the risk of corneal haze after PRK at higher levels of myopia, particularly in cases with a history of keloid formation. [J Refract Surg. 2024;40(8):e533-e538.].

AI-driven photonics: Unleashing the power of AI to disrupt the future of photonics

Recent advances in artificial intelligence (AI) and computing technologies are currently disrupting the modeling and design paradigms in photonics. In this work, we present our perspective on the utilization of current AI models for photonic device modeling and design. Initially, through the physics-informed neural networks (PINNs) framework, we embark on the task of modal analysis, offering a unique neural networks-based solver and utilizing it to predict propagating modes and their corresponding effective indices for slab waveguides. We compare our model’s predictions against theoretical benchmarks and a finite differences solver. Evidently, using 349 analysis points, the PINN approach had a relative percentage error of 0.69272% compared to the finite differences method, which had a percentage error of 1.28142% with respect to the analytical solution, indicating that the PINN approach was more accurate in conducting modal analysis. Our model’s continuity over the entire solution domain enhances its performance and flexibility while requiring no training data due to its guidance by Maxwell’s equations, setting it apart from most AI approaches. Our model design also flexibly enables simultaneous prediction of multiple modes over any specified intervals of effective indices. In addition, we present a novel reinforcement learning (RL)-based paradigm, employing an actor–critic model for inverse design. We utilize this paradigm to optimize the transmittance of a grating coupler by manipulating the device geometry. Comparing the obtained design to that obtained using the Particle Swarm Optimization (PSO) algorithm, our RL-based approach effectively produced a significant enhancement of 34% in 14 iterations only over the initial design compared to the PSO, which prematurely scored 27% enhancement in 30 iterations, proving that our model navigates the design space more efficiently, achieving a better design than PSO and resulting in a superior design. Based on these approaches, we discuss the future of AI in photonics in forward modeling and inverse design and the untapped potential in bringing these worlds together.

Education strategies in the context of AI increasing role

The development of artificial intelligence (AI) necessitates both theoretical reflection on strategic changes in educational content and forms, and the exploration of AI's potential to enhance educational quality. AI will replace many activities of a person in his/her professional career and personal life. Questions arise regarding the uniquely human abilities that current AI cannot replicate. What are the strategic implications of the introduction of artificial intelligence into our lives for university education or other levels and forms of education, including the specifics of individual disciplines? We aim to summarize research findings on the distinctions between current AI and human intelligence, focusing on identifying human intelligence's specificity and promoting its development within educational systems. We are contemplating the possibility of creating a type of AI that could replace even those human abilities that the current type of AI cannot.

Umetna inteligenca v modni in oblačilni industriji

In recent times, there has been significant interest in incorporating artificial intelligence (AI) in the fashion and apparel industry, as it has the ability to bring about significant transformations across different aspects of the sector. This comprehensive review offers a systematic examination of current AI applications in fashion, and discusses implications and future opportunities. The study investigates AI uses in the sector, with a focus on virtual try-on technology, personalized recommendations, AI-driven design and supply chain optimization. This analysis explores the benefits and constraints of these applications, and is supported by real-world case studies showcasing successful deployments. The review examines how AI influences consumer insights and market trends, and highlights the effectiveness of sentiment analysis and social media monitoring as tools for understanding consumer preferences and the shaping of brand perception. It also emphasizes AI's role in analysing market trends, enabling the identification of emerging fashion trends through AI-driven market research tools. This study provides a glimpse into future possibilities and advancements AI can introduce to fashion, including potential integration with augmented reality (AR), innovative applications in fashion shows and events, and the potential to revolutionize traditional business models. This review consolidates primary findings on the introduction of AI in the fashion and apparel sector, and emphasizes AI's potential to enhance consumer experiences, sustainability practices and market efficiency.

Evaluation of Current Artificial Intelligence Programs on the Knowledge of Glaucoma.

To measure the success of three different artificial intelligence chatbots, ChatGPT, Bard, and Bing, in correctly answering questions about glaucoma types and treatment modalities and to examine their superiority over each other. Thirty-two questions about glaucoma types and treatment modalities were asked using the ChatGPT, Bard, and Bing chatbots. The correct and incorrect answers were also provided. Accuracy rates were compared. Questions asked: ChatGPT answered 56.3%, Bard 78.1%, and Bing 59.4% correctly. There was no statistically significant difference between the three artificial intelligence chatbots in the rate of correct and incorrect answers to the questions asked (p = 0.195). Artificial intelligence chatbots can be used as a tool to access accurate information regarding glaucoma types and treatment modalities. However, the information obtained is not always accurate, and care should be taken when using this information.

Potential Predictors of Psychologically Based Stock Price Movements

Investment in stocks is increasingly dependent on artificial intelligence (AI), but the psychological and social factors that affect stock prices may not be fully covered by the measures currently used in AI training. Here, we search for additional measures that may improve AI predictions. We start by reviewing stock price movements that appear to be affected by social and psychological factors, drawing on stock market behaviour during the COVID-19 pandemic. A review of processes that are likely to produce such stock market movements follows: the disposition effect, momentum, and the response to information. These processes are then explained by regression to the mean, negativity bias, the availability mechanism, and information diffusion. Taking account of these processes and drawing on the consumer behaviour literature, we identify three factors which may not be covered by current AI training data that could affect stock prices: publicity in relation to capitalization, stock-holding penetration in relation to capitalization, and changes in the penetration of stock holding.

Future of IT Jobs in the Era of Artificial Intelligence

The rapid advancement of Artificial Intelligence (AI) technologies is set to reshape IT jobs in the coming years. This paper investigates AI's evolving role in the sector and its implications for employment trends, drawing from a review of secondary sources including various research journal papers, and reports of White House, World Economic Forum, and McKinsey, etc. It analyses the current AI applications in IT, focusing on areas where automation and augmentation will likely impact traditional roles. The study views AI as complementing rather than replacing human labour, foreseeing potential for new job creation and transformations in existing positions, especially in cybersecurity, software development, and IT infrastructure management. Policy recommendations and strategic initiatives from various stakeholders are examined to maximize AI benefits while addressing employment risks. Insights from government and industry reports stress the need for workforce preparation through reskilling and upskilling. Ethical considerations around AI deployment in IT underscore responsible innovation and inclusive growth. Ultimately, this paper aims to offer a comprehensive overview of AI's future impact on IT jobs, providing insights for policymakers, industry leaders, and educators navigating the evolving digital economy. By synthesizing diverse perspectives and empirical evidence, it informs decision-making for a sustainable and equitable future of work in IT.