Artificial Intelligence Training Research Articles

AGENT Q-LEARNING SPEED DEPENDENCY ON THE FACTORS IN TIC-TAC-TOE

Reinforcement learning of program agents has widespread usage today. Especially, Q-learning, a model-free reinforcement learning technique has shown great results in various applications, like games, self-driving car and robot control. Regarding turn-based games many scientists successfully applied it to train artificial intelligence and to make competitive opponent for human player. While algorithms are well-known there is a room for parametrical optimization to achieve maximum learning speed considering specific problem like turn-based board game. As results of this research show the speed can vary significantly. Tic-Tac-Toe is a very simple and old game that gives an opportunity to try Q-learning without excessive efforts. The algorithm is universal and can be applied to more complex games. It worth noting that core of learning algorithm is the same for any similar game – only rules and board size are changed, which is one of the important properties of Q-learning. This paper investigates the impact of learning rate and discount factor on the speed of learning of the Q-learning program agent in Tic-Tac-Toe board game. It is conducted a series of experiments using developed computer implementation of the algorithm to analyze the correlation between learning rate, discount factor, and the convergence rate of Q-learning in the specified game. So the experimental field consists of two factors, each of which has three levels, and full factorial experiment has nine combinations of these factors. The learning speed dependency on the each factor is presented. The findings reveal strong relationships between these parameters and convergence speed. For example, the speed is increased proportionally to the both factors, but in the case of discount factor the increase is about 1.4 times lower. Practical significance of the research is in the optimization of factors to achieve effective training of the software agent in order to save processing time, the payment of which is one of the main expenses of enterprises in the field of information technologies. In addition, the research contributes to a better understanding of how Q-learning performs in different game scenarios and provides guidelines for parameter selection in similar applications

Strengthening Islamic Religious Education Teacher Competencies in the Society 5.0 Era: Challenges and Interventions

The Society 5.0 era brings significant changes to various aspects of life, including education. Islamic Religious Education (PAI) teachers play a vital role in shaping students' character, ensuring they can control technology's negative influences and use it positively. This study aims to identify the challenges faced by PAI teachers in the Society 5.0 era and evaluate the strengthening of their competencies through the PAI Subject Teachers' Deliberation (MGMP) in Kutai Kartanegara, focusing on artificial intelligence (AI) integration. A qualitative case study approach was employed, with goal-setting theory guiding the intervention in a teacher training program. Data were collected through in-depth interviews with PAI teachers, participatory observation, and analysis of teaching materials produced during the training. The intervention involved AI-based applications like Google Docs' AI writing assistant for lesson planning and AI-driven tools for evaluating student performance. Thematic analysis was used to identify key patterns and themes regarding the challenges and opportunities in strengthening teacher competencies. The results showed that AI training improved the effectiveness and efficiency of teaching materials, marked by more interactive content, shorter preparation times, and better alignment with student needs. Additionally, there was a significant increase in student engagement during lessons, highlighting the success of AI technology in enhancing the learning process.

Stochastic Publics: The Emergence and Ethics of AI-Generated Publics in Technical Communication

The concept of a public—a group of strangers drawn together through their mutual attention to a text—has historically been tied to the notion of human intentionality. The recent popularization of artificial intelligence (AI) large language models (such as ChatGPT) destabilizes this connection. When large language models generate text, they may inadvertently form stochastic publics—groups pulled together through the randomization of biased data patterns drawn from AI training material. This exploratory study draws on a three-phase dialogue with OpenAI's ChatGPT 4 to identify the risks of stochastic publics and suggest human-originated interventions grounded in feminist care ethics.

Text and Data Mining Exceptions in Latin America

AbstractText and data mining (TDM) is a powerful tool in the knowledge discovery process and an essential step in the process of training Artificial Intelligence (AI) systems. Whether forms of use needed for TDM conflict with copyright rules is still a matter for debate within the specialized literature and when designing new legislation across the globe. Despite the borderless nature of research and the fact that the interplay between TDM and copyright is a matter of interest to all regions, most of the focus in the existing literature is on countries or examples from the Global North. This study contributes to filling this gap by providing additional information on recent developments across Latin America regarding the need for copyright legislation to adapt to data-intensive research practices and uses. It also provides a set of practical examples and issues specific to that region. It is hoped that these will, at least, partially, contribute to a more universal approach to the issue around the globe.

Considerations on the basis of medical reasoning for the use in AI applications.

This study discusses the integration of artificial intelligence (AI) and machine learning (ML) in medical reasoning and decision-making, with a focus on the challenges and opportunities associated with the massive consumption of data required for training AI systems, and contrasts this with the limited data typically available to medical practitioners. We advocate for a balanced approach that includes small data and emphasize the importance of maintaining the art of clinical reasoning amid technological advancements. Finally, we highlight the potential of multidisciplinary research in addressing the complexities of medical reasoning and suggest the necessity of careful abstraction and conceptual modeling in AI applications.

AI in obstetrics: Evaluating residents’ capabilities and interaction strategies with ChatGPT

In line with the digital transformation trend in medical training, students may resort to artificial intelligence (AI) for learning. This study assessed the interaction between obstetrics residents and ChatGPT during clinically oriented summative evaluations related to acute hepatic steatosis of pregnancy, and their self-reported competencies in information technology (IT) and AI.The participants in this semi-qualitative observational study were 14 obstetrics residents from two university hospitals. Students’ queries were categorized into three distinct types: third-party enquiries; search-engine-style queries; and GPT-centric prompts. Responses were compared against a standardized answer produced by ChatGPT with a Delphi-developed expert prompt. Data analysis employed descriptive statistics and correlation analysis to explore the relationship between AI/IT skills and response accuracy.The study participants showed moderate IT proficiency but low AI proficiency. Interaction with ChatGPT regarding clinical signs of acute hepatic steatosis gravidarum revealed a preference for third-party questioning, resulting in only 21% accurate responses due to misinterpretation of medical acronyms. No correlation was found between AI response accuracy and the residents’ self-assessed IT or AI skills, with most expressing dissatisfaction with their AI training. This study underlines the discrepancy between perceived and actual AI proficiency, highlighted by clinically inaccurate yet plausible AI responses – a manifestation of the ’stochastic parrot’ phenomenon.These findings advocate for the inclusion of structured AI literacy programmes in medical education, focusing on prompt engineering. These academic skills are essential to exploit AI’s potential in obstetrics and gynaecology. The ultimate aim is to optimize patient care in AI-augmented health care, and prevent misleading and unsafe knowledge acquisition.

Polymer hydrogel electronics with adhesive, self-healing, and anti-ultraviolet capabilities for machine learning-promoted electrophysiological detection

While adjustable mechanical properties, diverse biochemical features, and good ionic conductivity enabled by molecular engineering, polymer hydrogels as ionic skins (i-skins) are still limited by multiple challenges in the practical human health monitoring and health diagnosis, such as undesirable ductility and contact adhesion, poor functionality, and insufficient sensing ability. In view of this, the strategy of multiple dynamic interactions was proposed, and a mussel characteristic double-network polymer composite hydrogel (PG-BT2) with tannic acid and borax as dynamic media was reasonably designed. Due to the dynamic reversible interactions from the crosslinked network combined with special functional groups, the obtained PG-BT2 hydrogel attains remarkable stretchability with >1200 % elongation and good toughness of 268.6 kJ/m−3, and also possesses favorable versatility (e.g., self-healing time <2 s, adhesion strength of 4.62 kPa to pigskin, and UV transmittance of 15.3 % at 365 nm). Additionally, the i-skin derived from the PG-BT2 hydrogel exhibits excellent sensing properties, including a low interface contact resistance of 8.3 kΩ at 1 kHz, fast response/recovery time of 0.657 s/0.348 s, and a wide sensing range of 0–1100 % strain. By combining the hydrogel-based i-skin patch with the wireless circuit connected to a Bluetooth module, the constructed sensor system is capable of accurately monitoring micro-expressions and multi-joint movements. The portable system is also demonstrated to capture high-fidelity signals of electrocardiograms available for training artificial intelligence, achieving differentiation and recognition of blood pressure status, which will greatly benefit health management and intelligent medicine.

An Exploratory Analysis of ChatGPT Compared to Human Performance With the Anesthesiology Oral Board Examination: Initial Insights and Implications.

Chat Generative Pre-Trained Transformer (ChatGPT) has been tested and has passed various high-level examinations. However, it has not been tested on an examination such as the American Board of Anesthesiology (ABA) Standardized Oral Examination (SOE). The SOE is designed to assess higher-level competencies, such as judgment, organization, adaptability to unexpected clinical changes, and presentation of information. Four anesthesiology fellows were examined on 2 sample ABA SOEs. Their answers were compared to those produced by the same questions asked to ChatGPT. All human and ChatGPT responses were transcribed, randomized by module, and then reproduced as complete examinations, using a commercially available software-based human voice replicator. Eight ABA applied examiners listened to and scored the topic and modules from 1 of the 4 versions of each of the 2 sample examinations. The ABA did not provide any support or collaboration with any authors. The anesthesiology fellow's answers were found to have a better median score than ChatGPT, for the module topics scores (P = .03). However, there was no significant difference in the median overall global module scores between the human and ChatGPT responses (P = .17). The examiners were able to identify the ChatGPT-generated answers for 23 of 24 modules (95.83%), with only 1 ChatGPT response perceived as from a human. In contrast, the examiners thought the human (fellow) responses were artificial intelligence (AI)-generated in 10 of 24 modules (41.67%). Examiner comments explained that ChatGPT generated relevant content, but were lengthy answers, which at times did not focus on the specific scenario priorities. There were no comments from the examiners regarding Chat GPT fact "hallucinations." ChatGPT generated SOE answers with comparable module ratings to anesthesiology fellows, as graded by 8 ABA oral board examiners. However, the ChatGPT answers were deemed subjectively inferior due to the length of responses and lack of focus. Future curation and training of an AI database, like ChatGPT, could produce answers more in line with ideal ABA SOE answers. This could lead to higher performance and an anesthesiology-specific trained AI useful for training and examination preparation.

Acceptance of artificial intelligence technologies in business management, finance, and e-commerce: factors, challenges, and strategies

This research investigates the comprehensive acceptance of artificial intelligence (AI) in business management, finance, and e-commerce, focusing on the factors driving its adoption, the obstacles encountered, and strategies for enhancing integration. AI technologies have transformed these sectors, delivering exceptional efficiencies, predictive analytics, and personalized customer experiences. However, their acceptance is influenced by various factors, including technological readiness, organizational culture, and perceived benefits. In business management, AI improves decision-making processes, optimizes operations, and fosters innovation. Financial institutions utilize AI for risk management, fraud detection, and personalized banking services, while the e-commerce sector gains from AI through enhanced customer service, dynamic pricing, and inventory management. Despite these benefits, challenges such as data privacy concerns, high implementation costs, and resistance to change impede widespread adoption. Additionally, ethical considerations and the need for regulatory compliance add layers of complexity. This paper identifies key strategies to address these challenges, such as promoting a culture of innovation, investing in AI education and training, and developing robust data governance frameworks. Strategic partnerships and collaborations with AI experts and tech firms are also essential for navigating the AI landscape. By comprehensively addressing these factors and challenges, businesses can unlock AI's full potential, driving sustainable growth and competitive advantage. This study contributes to understanding AI acceptance in critical sectors, providing a roadmap for successful AI implementation and emphasizing the importance of strategic planning and stakeholder engagement.

Brainchop: Providing an Edge Ecosystem for Deployment of Neuroimaging Artificial Intelligence Models.

Deep learning has proven highly effective in various medical imaging scenarios, yet the lack of an efficient distribution platform hinders developers from sharing models with end-users. Here, we describe brainchop, a fully functional web application that allows users to apply deep learning models developed with Python to local neuroimaging data from within their browser. While training artificial intelligence models is computationally expensive, applying existing models to neuroimaging data can be very fast; brainchop harnesses the end user's graphics card such that brain extraction, tissue segmentation, and regional parcellation require only seconds and avoids privacy issues that impact cloud-based solutions. The integrated visualization allows users to validate the inferences, and includes tools to annotate and edit the resulting segmentations. Our pure JavaScript implementation includes optimized helper functions for conforming volumes and filtering connected components with minimal dependencies. Brainchop provides a simple mechanism for distributing models for additional image processing tasks, including registration and identification of abnormal tissue, including tumors, lesions and hyperintensities. We discuss considerations for other AI model developers to leverage this open-source resource.

On-device training of artificial intelligence models on microcontrollers

&lt;p&gt;&lt;span&gt;Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.&lt;/span&gt;&lt;/p&gt;

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Artificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

Artificial Intelligence- and Physician-Interpreted Stool Image Characteristics Correlate With C-Reactive Protein Among Inpatients With Acute Severe Ulcerative Colitis: A Pilot Study.

Stool characteristics are used as a measure of ulcerative colitis (UC) disease activity, but they have not been validated against objective inflammation. We aimed to determine whether stool characteristics measured by trained artificial intelligence (AI) and physicians correlate with inflammation in UC. Patients hospitalized with acute severe UC (ASUC) were asked to capture images of all bowel movements using a smartphone application (Dieta®). Validated AI was used to measure five stool characteristics including the Bristol stool scale. Additionally, four physicians scored each image for blood amount, mucus amount, and whether stool was in a toilet or commode. AI measurements and mean physician scores were rank-normalized and correlated with rank-normalized CRP values using mixed linear regression models. Mann-Whitney tests were used to compare median CRP values of images with and without mucus and with and without blood. We analyzed 151 stool images collected from 5 patients admitted with ASUC (mean age 42 years, 40% male). Overall, Bristol stool scale and fragmentation positively correlated with CRP, while stool consistency negatively correlated with CRP. The median CRP of images with mucus was higher than that of images without mucus. Smartphone application AI measurements of Bristol stool scale, stool consistency, and stool fragmentation significantly correlate with CRP values in hospitalized patients with ASUC. Additionally, median CRPs are higher when mucus is seen. Further training of smartphone-based AI algorithms to validate the association of stool characteristics with objective inflammation may yield a novel, noninvasive tool for UC disease monitoring.

Exploring social and computer science students’ perceptions of AI integration in (foreign) language instruction

Artificial intelligence (AI) has gained acceptance in the field of education. Nevertheless, existing research on AI in education, particularly in foreign language (FL) learning and teaching, is notably limited in scope and depth. In the present study, we addressed this research gap by investigating social and computer science students' perceptions of the integration and use of AI-based technologies in education, focusing specifically on foreign language teaching. Using an online questionnaire, we analysed factors such as students' field of study, gender differences, and the type of AI used. The questionnaire included statements categorised into thematic clusters, with responses measured on a five-point Likert scale. Statistical analysis, including chi-square tests and Cohen's d, revealed that individuals studying computer science, males, and supporters of generative AI are more likely to use AI tools for educational purposes. They perceive fewer barriers to the integration of AI into FL education. Social science students and women are less likely to use AI tools in FL education and express scepticism about their potential to improve academic outcomes. They tend to be more critical or cautious regarding the role of AI in FL education. They view AI as a valuable tool that enhances the learning experience but, at the same time, recognise the irreplaceable role of human teachers. The study highlights the need for targeted educational initiatives to address gender and disciplinary gaps in AI adoption, promote informed discussions on AI in education, and develop balanced AI integration strategies to improve FL learning. These findings suggest educators and policymakers should implement comprehensive AI training programs and ethical guidelines for responsible AI use in (FL) education.

Proposing an AI Passport as a Mitigating Action of Risk Associated to Artificial Intelligence in Healthcare.

The integration of Artificial Intelligence (AI) in healthcare signifies a substantial shift, offering benefits to patients and healthcare systems while also introducing new risks. The emphasis on patient safety and performance standards is pivotal, especially with the European Union's strides towards regulating AI through the AI Act. This act focuses on classifying AI systems based on risk levels, mandating stringent requirements for high-risk AI, enhancing transparency, and ensuring ethics in AI applications. The concept of an "AI passport" is introduced as a living document detailing the AI system's purpose, ethical declarations, training, evaluation, and potential biases. This passport aims to enhance transparency and safety in medical AI applications, serving as a comprehensive record for patients, clinicians, and stakeholders. The AI passport, structured in JSON format, encapsulates key information about the AI system as a mechanism for continuous performance evaluation and transparency. This initiative may represent a significant step towards mitigating the risks associated with AI in healthcare, emphasizing the importance of accountability, transparency, and patient safety in the development and application of AI technologies.

Artificial intelligence facilitates the potential of simulator training: An innovative laparoscopic surgical skill validation system using artificial intelligence technology.

The development of innovative solutions, such as simulator training and artificial intelligence (AI)-powered tutoring systems, has significantly changed surgical trainees' environments to receive the intraoperative instruction necessary for skill acquisition. In this study, we developed a new objective assessment system using AI for forceps manipulation in a surgical training simulator. Laparoscopic exercises were recorded using an iPad®, which provided top and side views. Top-view movies were used for AI learning of forceps trajectory. Side-view movies were used as supplementary information to assess the situation. We used an AI-based posture estimation method, DeepLabCut (DLC), to recognize and positionally measure the forceps in the operating field. Tracking accuracy was quantitatively evaluated by calculating the pixel differences between the annotation points and the points predicted by the AI model. Tracking stability at specified key points was verified to assess the AI model. We selected a random sample to evaluate tracking accuracy quantitatively. This sample comprised 5% of the frames not used for AI training from the complete set of video frames. We compared the AI detection positions and correct positions and found an average pixel discrepancy of 9.2. The qualitative evaluation of the tracking stability was good at the forceps hinge; however, forceps tip tracking was unstable during rotation. The AI-based forceps tracking system can visualize and evaluate laparoscopic surgical skills. Improvements in the proposed system and AI self-learning are expected to enable it to distinguish the techniques of expert and novice surgeons accurately. This system is a useful tool for surgeon training and assessment.

Responsible AI in Organizational Training: Applications, Implications, and Recommendations for Future Development

Through a literature review, this study investigates the responsibility, application, and impact of Artificial Intelligence (AI) in organizational training based on the theoretical frameworks of Psychological, Economic, and Systems Theories in Human Resource Development (HRD). It emphasizes the importance of responsible AI training systems that adhere to non-discrimination, privacy, interpretability, professional responsibility, and accountability to ensure AI’s beneficial and equitable contribution to training. The application scenarios of AI in areas such as knowledge management, training needs analysis, training delivery, and feedback to provide personalized and efficient training solutions are analyzed. Moreover, it highlights the differing impacts of AI-supported training on organizations, trainers, and trainees and the significance of stakeholder engagement. Finally, it proposes recommendations for future research to broaden our understanding of AI’s application in training and assess its effects on policies and practices, guiding organizations to adopt AI technologies per HRD principles and ethical standards.

Detektion von Bindefehlern beim DED-Arc

Abstract Based on artificial intelligence (AI) developed for monitoring arc welding, this article presents a deep neural network for monitoring lack of fusion defects in wire arc additive manufacturing of aluminium. The aim is to detect defects in built-up volumes on the basis of weld source data. These can be successfully processed by the algorithm presented and a trained AI. The achieved accuracy of the network is &gt; 90 percent.

Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods

BackgroundPostoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train artificial intelligence (AI) because of its complex nature, AI may be trained to intraoperatively detect parathyroid glands using various augmentation methods. The purpose of this study was to train an effective AI model to detect parathyroid glands during thyroidectomy.MethodsVideo clips of the parathyroid gland were collected during thyroid lobectomy procedures. Confirmed parathyroid images were used to train three types of datasets according to augmentation status: baseline, geometric transformation, and generative adversarial network-based image inpainting. The primary outcome was the average precision of the performance of AI in detecting parathyroid glands.Results152 Fine-needle aspiration-confirmed parathyroid gland images were acquired from 150 patients who underwent unilateral lobectomy. The average precision of the AI model in detecting parathyroid glands based on baseline data was 77%. This performance was enhanced by applying both geometric transformation and image inpainting augmentation methods, with the geometric transformation data augmentation dataset showing a higher average precision (79%) than the image inpainting model (78.6%). When this model was subjected to external validation using a completely different thyroidectomy approach, the image inpainting method was more effective (46%) than both the geometric transformation (37%) and baseline (33%) methods.ConclusionThis AI model was found to be an effective and generalizable tool in the intraoperative identification of parathyroid glands during thyroidectomy, especially when aided by appropriate augmentation methods. Additional studies comparing model performance and surgeon identification, however, are needed to assess the true clinical relevance of this AI model.Graphical abstract

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning

Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal hernia (HH), a common endoscopic finding connected to gastro-esophageal reflux disease. A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) is designed. We identified 21,970 gastroscopic images as training data and used our AL to train a model for predicting the Hill classification and detecting HH. Performance of the AL and traditionally trained models were evaluated on an external expert-annotated image collection. The AL model achieved accuracy of 76%. A traditionally trained model with 125% more training data achieved 77% accuracy. Furthermore, the AL model achieved higher precision than the traditional one for rare classes, with 0.54 versus 0.39 (p < 0.05) for grade 3 and 0.72 versus 0.61 (p < 0.05) for grade 4. In detecting HH, the AL model achieved 94% accuracy, 0.72 precision and 0.74 recall. Our AL pipeline is more efficient than traditional methods in training AI for endoscopy.