Artificial Intelligence Products Research Articles

How reliable is the artificial intelligence product large language model ChatGPT in orthodontics?

To evaluate the reliability of information produced by the artificial intelligence-based program ChatGPT in terms of accuracy and relevance, as assessed by orthodontists, dental students, and individuals seeking orthodontic treatment. Frequently asked and curious questions in four basic areas related to orthodontics were prepared and asked in ChatGPT (Version 4.0), and answers were evaluated by three different groups (senior dental students, individuals seeking orthodontic treatment, orthodontists). Questions asked in these basic areas of orthodontics were about: clear aligners (CA), lingual orthodontics (LO), esthetic braces (EB), and temporomandibular disorders (TMD). The answers were evaluated by the Global Quality Scale (GQS) and Quality Criteria for Consumer Health Information (DISCERN) scale. The total mean DISCERN score for answers on CA for students was 51.7 ± 9.38, for patients was 57.2 ± 10.73 and, for orthodontists was 47.4 ± 4.78 (P = .001). Comparison of GQS scores for LO among groups: students (3.53 ± 0.78), patients (4.40 ± 0.72), and orthodontists (3.63 ± 0.72) (P < .001). Intergroup comparison of ChatGPT evaluations about TMD was examined in terms of the DISCERN scale, with the highest value given in the patients group (57.83 ± 11.47) and lowest value in the orthodontist group (45.90 ± 11.84). When information quality evaluation about EB was examined, it GQS scores were >3 in all three groups (students: 3.50 ± 0.78; patients: 4.17 ± 0.87; orthodontists: 3.50 ± 0.82). ChatGPT has significant potential in terms of usability for patient information and education in the field of orthodontics if it is developed and necessary updates are made.

Research on Copyright Subjectivity in Generative Artificial Intelligence Production

Research on Copyright Subjectivity in Generative Artificial Intelligence Production

From the Perspective of Originality: Analysis of the Legal Nature of Artificial Intelligence Products

From the Perspective of Originality: Analysis of the Legal Nature of Artificial Intelligence Products

Aerospace Software and AI Sustainability: A Software Developer Capabilities Approach

Artificial intelligence (AI) mediated approaches towards generating or refining digital software are influencing the aerospace industry and its emerging, developing, and innovative operational systems. Such firm and/or industry inclusions can enhance entrepreneurial effectiveness and ongoing sustainability. A nationwide Qualtrics survey delivers semi-structured interview summations of registered software-house developers (‘experts) showing nodal thematic data can NVivo project map that stagewise models towards a suite of sustainability outcomes. Respondent software developers consider software house digital and AI new capacities, plus their knowledge creation frame across two capabilities areas that relationally link into an entrepreneurial capabilities suite and a strategic AI product trending capabilities suite. This system likely coalesces, as a digital AI-supported collective, towards enhancements into digital and AI globally sustainable outcomes. The aerospace industry. and their engaged software houses can use this operational approach when strategically planning future ongoing software and AI development inclusions across their globally located corporate aerospace entities, by considering how best to adjust ongoing digital and AI competencies and entrepreneurial capabilities towards delivering enhanced sustainable outcomes. This study suggests a stagewise and measurement model approach can likely be derived to introduce chosen software and AI levers, but these should focus on delivering ongoing digital and sustainable aerospace industry outcomes.

Unveiling bias in artificial intelligence: Exploring causes and strategies for mitigation

With the rapid advancement of Artificial Intelligence (AI), the emergence of various AI models such as Stable Diffusion, ChatGPT, and MidJourney has brought numerous benefits and opportunities. Through users' extensive utilization, they have discovered biases towards gender, race, and other factors in these AI systems. This paper focuses on bias in AI and aims to investigate its causes and propose strategies for mitigation. Through a comprehensive literature review, the paper has explored the phenomenon of bias in AI-generated content. Furthermore, we examine the reasons behind bias and solutions from social and intelligence science perspectives. From a social science perspective, we examine the effects of gender bias in AI and highlight the importance of incorporating diversity and gender theory in machine learning. From an intelligence science standpoint, we explore factors like biased datasets, algorithmic fairness, and the role of machine learning randomness in group fairness. Additionally, we discuss the research methodology employed, including the literature search strategy and quantity assessment. The results and discussions confirm the existence of bias in current AI products, particularly in the underrepresentation of women in the AI development field. Finally, we present future perspectives on reducing bias in AI products, including the importance of fair datasets, improved training processes, and increased participation of female engineers and intelligence scientists in the AI field. By addressing bias in AI, the paper can strive for more equitable and responsible AI systems that benefit diverse users and promote social progress.

Unraveling Indonesian Public R&amp;D Institutions’ Perspectives on Chatgpt: An Analytical Approach To Decoding Open-Ended Surveys Through Topic Modeling

&lt;p&gt;The rapid development of artificial intelligence (AI) technologies like ChatGPT has made global management easier. Since 2020, AI has been widely utilized for all purposes, notably due to its ease in access for scientific production. This study aims to analyze open-ended questions and responses from web-based survey in Indonesian public research and development (R&amp;amp;D) institutions using topic modeling. A total of 205 data points were obtained through web-based surveys conducted among researchers in Indonesian public R&amp;amp;D institutions. To learn their thoughts on the use of ChatGPT (Chat Generative Pre-Trained Transformer), two AI language topic modeling, Latent Dirichlet Allocation (LDA) and Principal Component Analysis (PCA), were employed to detect survey topic structures and show the results. Three theme groups represent the institution's research cohort. Based on generational disparities in birth year and functional position level, this study selected the seven most popular topics from three themes of researchers' opinions on ChatGPT. Researchers of certain age generations and functional position levels focused on new AI technologies, efficiency, and production gains, while others valued methodological innovation, ethics, and scientific integrity. When formulating a strategy for incorporating AI into the public R&amp;amp;D institution's future research agendas, it is imperative to include diverse perspectives.&lt;/p&gt;

Spanish media coverage of journalistic artificial intelligence: relevance, topics and framing

Artificial intelligence (AI) has become a much-discussed topic due to its implementation in many areas, including journalism. This article examines the coverage of journalistic AI in Spanish written media, in particular the relevance allocated to it, the topics that are addressed, and the framing from which it is approached. Quantitative content analysis and statistical analysis were used. Although journalistic AI only appears as the main topic in a third of cases, it is increasingly present, it is distributed across more sections, and it mostly appears in self-written articles. Despite informative texts prevailing, journalists author the majority of interpretative and practically all opinion pieces, thus increasing knowledge and promoting public debate. They deal largely with the most widespread application of AI, which is news automation, ranking well above the issues of job loss and ethics. Although journalistic AI is generally framed from a perspective of its benefits rather than from its risks, some recent changes are observed: growing concern about its dangers and its ethical implications; the detection of numerous pieces with a Personal Frame, where journalists reflect on their profession and the use of ChatGPT; and the growing relevance of the Episodic Frame as specific AI products develop. This study could have examined all types of media, although that approach would have gone beyond the exploratory nature of this pioneering research. With this work we extend scientific production on journalistic AI in Spain, where this perspective has not been analysed.

Research on Copyright Protection and Attribution of Artificial Intelligence Products - Focusing on Reviewing Cases of Major Foreign AI Products -

Research on Copyright Protection and Attribution of Artificial Intelligence Products - Focusing on Reviewing Cases of Major Foreign AI Products -

확장된 통합기술수용이론을 활용한 ChatGPT 서비스에 관한 중국 소비자 행동 연구

In this study, we focused on empilically analyzing the factors that influence consumer intention to use and purchase behavior at the introduction and diffusion stages of the innovative technology, ChatGPT Plus, using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT). Furthermore, we examined the moderating roles of education and monthly income in specific relationships. To summarize the research results, firstly, among the technology acceptance factors affecting Chinese consumers' use of the innovative ChatGPT Plus paid service, the performance expectancy, effort expectancy, facilitating conditions proposed in the basic UTAUT, and hedonic motivation, price value, and content quality proposed in the extended UTAUT were found to significantly influence the intention to purchase the ChatGPT Plus paid service. On the contrary, it was found that social influence and usage habits do not significantly influence the intention to purchase ChatGPT Plus paid service. Secondly, among the technology acceptance factors that influence the use of ChatGPT Plus paid service, performance expectancy, hedonic motivation, and content quality showed a significant impact on purchase intention through the moderating effect of education. Thirdly, in this study, the purchase intention of ChatGPT Plus paid service was confirmed to have a positive effect on purchase behavior. Through these research results, artificial intelligence-related companies can respond more effectively to consumers' intention to use and purchase behavior, and on this basis, they can improve the quality of artificial intelligence products and GPT Plus services.

Use of AI artificial intelligence chatGPT for arabic language learning media

The existence of AI (artificial intelligence) has helped and solved many problems faced by humans, including in the world of learning Arabic. Currently, AI is mostly produced and developed by Western countries, but Islamic countries have become consumers and markets for AI products. If AI is not utilized in various areas of life, including the world of Arabic language learning, then the development of human resources, especially Arabic language experts, will certainly be hampered in the future. Therefore, in the world of Arabic language learning, useArtificial Intelligence It is very necessary for the teaching and learning process to be effective and efficient and to achieve the learning objectives. This is what underlies the discussion of this research. This research uses qualitative methods and the type is library research. In the context of ChatGPT which is part of information technology, it is necessary to briefly discuss IT which provides advantages such as speed, consistency, accuracy and reliability so that the learning process runs well and results are obtained. Fundamentals of AI also provides insight into the basic principles of AI development itself, starting from a problem and working to develop a solution. In general, the presence of ChatGPT as an AI product has recently become very important in the world of learning because it helps teachers and makes their work easier, especially in implementing learning strategies in schools. Even if we can utilize the sophistication of AI, of course it should be operated by humans who have a strong understanding of the basics of learning science in detail and in depth. Technology that is part of a scientific application is independent of the values of its users. This means that the benefits of technology depend on the producers and users of each technology. Islam itself strongly recommends its followers to utilize technology in managing the natural resources that Allah has given to their people. Utilization of artificial technology in the form of chatGPT is an inseparable part of the world of learning to facilitate the teaching and learning process and motivate students to study hard and achieve quality results in the future

Artificial Intelligence for Creating a Digitized Lifestyle and Multifaceted Applications: A Case Study in the Realm of Smart Home Technology

Research Background: As a new generation of general-purpose technology, artificial intelligence (AI) has emerged as the core driver of the fourth industrial revolution. Its extensive application in various fields has not only reshaped the forms of traditional industries, but also spawned the rise of emerging industries. Current Situation: The application of AI in the field of intelligent home decoration has garnered significant attention both domestically and internationally. Notable progress has been made in technological research and development, product innovation, and market applications. Reasons: With the rapid development of productive forces, the contradiction between people's ever-growing demand for a better life and the lagging social productive forces has been resolved. Correspondingly, the increasing demand for improved living quality and comfort has propelled the integration of AI into homes, accelerating the construction of digital households. Purpose: This article aims to analyze the impact of artificial intelligence on smart homes, assess the feasibility of achieving digital home design, and explore how AI can facilitate human life. It hopes to contribute to the future development direction of artificial intelligence. Method: Starting from people's basic material needs, this article enumerates typical application cases in smart homes, covering various aspects of daily life such as clothing, food, shelter, and transportation. Furthermore, it delves into the research on how to create intelligent systems that enable interconnection and interoperability among various AI products.

Artificial Intelligence Systems and Non-Contractual Civil Liability: A Risk-Based Approach

Under the legislation, when artificial intelligence (AI) systems cause harm to third parties, the restoration of violated rights is carried out according to the rules of strict or culpable liability. Strict liability is applied if the AI system is recognized as a source of increased danger or has a defect. For all other cases, culpable civil liability is used. The authors have developed a new approach to non-contractual civil liability for cases of harm caused by AI systems based on the criterion of the risk level of AI systems. According to this approach, for AI systems that create unacceptable or high risk in relation to human rights and freedoms, it is proposed to apply strict liability to their developer, and for AI systems belonging to the low-risk classification group, the rules of culpable liability to restore violated rights and compensate for the harm caused should be applied. With regard to the basic models, the use of culpable liability is envisaged, except situations where AI products with unacceptable or high risk are created on their basis. The proposed approach can become an alternative to using the concept of a source of increased danger in relation to AI systems and will allow transferring strict responsibility from owners of high-risk AI systems to their developers, who have a greater impact on the safety and reliability of AI systems.

Determination of Artificial Intelligence Anxiety Status of Nursing Students: Cross-Sectional-Descriptive Study

Aim: The study aimed to determine the anxiety of nursing students about the emergence and use of artificial intelligence products. Material and Method: The data of this descriptive and cross-sectional study were collected between 02.01.2023 and 15.04.2023. The sample of the research consisted of 243 students. The data collection tool included an introductory information form and the Artificial Intelligence Anxiety Scale. T-test, and one-way ANOVA test were used to analyze the data. Results: 64.6% of the students had heard of artificial intelligence-supported devices used in healthcare, 54.7% thought that artificial intelligence applications were useful in ensuring patient safety, and 54.7% thought that the system would reduce the risk of making medical errors. The mean total score of the scale was 46.25 ± 9.66. There was a statistically significant relationship between thinking that artificial intelligence should be a course in education and thinking that artificial intelligence would be indispensable in surgical applications and the artificial intelligence anxiety scale (p

AI in medical education: the moderating role of the chilling effect and STARA awareness

BackgroundThe rapid growth of artificial intelligence (AI) technologies has been driven by the latest advances in computing power. Although, there exists a dearth of research on the application of AI in medical education.Methodsthis study is based on the TAM-ISSM-UTAUT model and introduces STARA awareness and chilling effect as moderating variables. A total of 657 valid questionnaires were collected from students of a medical university in Dalian, China, and data were statistically described using SPSS version 26, Amos 3.0 software was used to validate the research model, as well as moderated effects analysis using Process (3.3.1) software, and Origin (2021) software.ResultsThe findings reveal that both information quality and perceived usefulness are pivotal factors that positively influence the willingness to use AI products. It also uncovers the moderating influence of the chilling effect and STARA awareness.ConclusionsThis suggests that enhancing information quality can be a key strategy to encourage the widespread use of AI products. Furthermore, this investigation offers valuable insights into the intersection of medical education and AI use from the standpoint of medical students. This research may prove to be pertinent in shaping the promotion of Medical Education Intelligence in the future.

Differences in the Definition of an Author under Copyright Laws of the United Kingdom, the United States, and Germany in the Context of Artificial Intelligence

In modern copyright law, which began with Queen Anne s Law, the author plays a very important role as the subject of the work. As such, identifying the author s identity is a matter of priority. ??As science and technology advances, granting author status to anyone has been discussed under copyright law since Burrow-Giles Lithographic Co. v. Sarony in 1884, and the development of artificial intelligence technology is again an issue of legal issues concerning the confirmation of author status of artificial intelligence products. Though it was thought that artificial intelligence products should be protected by law, the problem is that only creations expressing human thoughts or emotions can be protected under the current copyright law. In addition, only human beings can be recognized as authors in precedents and theories. ??This paper introduces the author problem of the distant, recent issue of artificial intelligence products and review the author s concept through content on the author s concept of Anglo-American and continental law countries.

The Synergy of Ambidextrous Leadership, Agility, and Entrepreneurial Orientation to Achieve Sustainable AI Product Innovation

This study aims to explore potential mechanisms of ambidextrous leadership (AL) in product innovativeness from the perspective of organizational agility (OA) and entrepreneurial orientation (EO) in firms operating in the artificial intelligence (AI) industry. A quantitative research method was used with 405 questionnaires, and the respondents were randomly selected from reputable databases. Structural equation modeling was employed to evaluate the model fit and conduct hypothesis testing. The findings suggest that ambidextrous leadership demonstrates a significant positive influence on product innovativeness and OA; also, through the mediating role of OA, it is possible to analyze both the direct and indirect relationships among the factors. Additionally, the moderating effect of EO on the intercorrelations among these factors was explored. This study enhances existing knowledge on leadership dynamics in the context of new product development, highlights the importance of adaptability in leadership, and sheds light on the interplay between OA, EO, and new product innovation. This study highlights the role of product innovativeness in sustainable AI product development. Enhanced product innovativeness not only sustains AI product development but also promotes environmental sustainability. This is achieved through the minimization of energy use, reduction in material requirements, and prevention of pollution. Firms are using these insights to develop sustainable and eco-friendly products, as well as create new market opportunities while reducing environmental impact. This research underscores the interconnectedness of factors in this study and sustainability, providing a new perspective on sustainable AI product development.

A growing number of AIs cleared for clinical use is finally available: The AI-assisted Pathologist

As more pathology laboratories are transitioning to a digital workflow, the availability of commercial Artificial Intelligence assistance systems is also increasing. Today nearly 40 such products approved for diagnostic use are available. This article provides an overview of the most widely addressed use cases, including Immunohistochemistry scoring for breast cancer and non-small-cell lung cancer, Gleason grading for prostate cancer, or metastasis detection in lymph nodes. While automation alone already promises an increase in efficiency that may help to bridge the growing gap between supply (pathology work force) and demand (histological testing), this article introduces another category of Artificial Intelligence products that go beyond just mimicking today’s established score. Various Artificial Intelligence are being introduced that detect genetic alterations or stratify risk, directly from the standard hematoxylin &amp; eosin staining. Finally, a brief outlook explains how basic AI models are currently finding their way into computational pathology and promise to further accelerate product developments by decreasing the time-to-model.

On the Copyright Analysis of Artificial Intelligence Products

With the gradual maturity of artificial intelligence technology, artificial intelligence products continue to emerge in the market, which poses new challenges to the current copyright law system. In particular, whether artificial intelligence products are copyrightable is a hot and difficult issue in the current theoretical and practical circles. Based on this, there are two mainstream viewpoints in the current theoretical circle, that is, the viewpoint that supports copyright protection for artificial intelligence products from the perspective of "reader-centered", and the viewpoint that opposes copyright protection for artificial intelligence products from the perspective of author-centered". However, the question of whether artificial intelligence products can be protected by copyright involves verifying the reasonableness and forward-looking nature of the "open" regulation of copyright objects in China on one hand. On the other hand, it is also relates to determining the future legislative direction of high-level artificial intelligence. Therefore, by comparing the similarities and differences in originality between the copyright law system and the copyright law system, the conclusion is drawn that artificial intelligence products should be protected by copyright. The evaluation criteria of subject-object separation established by "reader-centered doctrine" should be adopted.

Dual Sensing Signal Decoupling Based on Thermoelectric Polymer Aerogels for Precise Temperature and Pressure Recognition

AbstractThe capability to emulate skin‐like temperature and pressure sensing is fundamental for next‐generation artificial intelligence products. However, detecting temperature and pressure simultaneously with a single sensor without signal interference is challenging. Herein, a novel PCC aerogel sensor composed of PEDOT:PSS, CNTs, and CNF via directional freezing technology is developed. The PCC sensor can decouple temperature and pressure stimuli into individual voltage and resistance signals. It exhibits high‐precision temperature sensing capabilities, boasting an exceptionally high Seebeck coefficient of 30.4 µV K‐1 and the ability to detect temperature variations as low as 0.1 K. PCC sensors show excellent sensitivity and fast response times for detecting static and dynamic pressures, as well as high stability after 1000 cycles. Its maximum pressure sensitivity can reach 159.1% kPa−1, and the lowest detection limit is 10 Pa. Additionally, its excellent thermoelectric properties also enable to generating thermopower from human skin for self‐powered pressure sensing. A 3×3 PCC sensor array has been proposed to simulate the unique features of human skin in temperature and pressure recognition. This work provides a scalable manufacturing strategy for multi‐functional tactile sensors.

Co‐designing enduring learning analytics prediction and support tools in undergraduate biology courses

AbstractEven highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co‐redesign methods in partnership with disciplinary experts to create high‐structure STEM courses that better support students and produce informative digital event data. To those data, we applied theory‐ and context‐relevant labels to reflect active and self‐regulated learning processes involving LMS‐hosted course materials, formative assessments, and help‐seeking tools. We illustrate the predictive benefits of this process across two cycles of model creation and reapplication. In cycle 1, we used theory‐relevant features from 3 weeks of data to inform a prediction model that accurately identified struggling students and sustained its accuracy when reapplied in future semesters. In cycle 2, we refit a model with temporally contextualized features that achieved superior accuracy using data from just two class meetings. This modelling approach can produce durable learning analytics solutions that afford scaled and sustained prediction and intervention opportunities that involve explainable artificial intelligence products. Those same products that inform prediction can also guide intervention approaches and inform future instructional design and delivery.Practitioner notesWhat is already known about this topic Learning analytics includes an evolving collection of methods for tracing and understanding student learning through their engagements with learning technologies. Prediction models based on demographic data can perpetuate systemic biases. Prediction models based on behavioural event data can produce accurate predictions of academic success, and validation efforts can enrich those data to reflect students' self‐regulated learning processes within learning tasks. What this paper adds Learning analytics can be successfully applied to predict performance in an authentic postsecondary STEM context, and the use of context and theory as guides for feature engineering can ensure sustained predictive accuracy upon reapplication. The consistent types of learning resources and cyclical nature of their provisioning from lesson to lesson are hallmarks of high‐structure active learning designs that are known to benefit learners. These designs also provide opportunities for observing and modelling contextually grounded, theory‐aligned and temporally positioned learning events that informed prediction models that accurately classified students upon initial and later reapplications in subsequent semesters. Co‐design relationships where researchers and instructors work together toward pedagogical implementation and course instrumentation are essential to developing unique insights for feature engineering and producing explainable artificial intelligence approaches to predictive modelling. Implications for practice and/or policy High‐structure course designs can scaffold student engagement with course materials to make learning more effective and products of feature engineering more explainable. Learning analytics initiatives can avoid perpetuation of systemic biases when methods prioritize theory‐informed behavioural data that reflect learning processes, sensitivity to instructional context and development of explainable predictors of success rather than relying on students' demographic characteristics as predictors. Prioritizing behaviours as predictors improves explainability in ways that can inform the redesign of courses and design of learning supports, which further informs the refinement of learning theories and their applications.