Artificial Intelligence Design Research Articles

Analysis of Artificial Intelligence Design Incorporated into Design of Street Space in Small Cities

INTRODUCTION: At present, China's urbanization continues to develop, but the level of urban planning and design has not kept pace with the progress of development, especially the creation of street space in small cities, which is still not able to adapt to the current needs of people's cultural life. With the development of the economy and science and technology, artificial intelligence and other information technologies are constantly integrated into daily life. Therefore, utilizing artificial intelligence for street space design research is significant.
 OBJECTIVES: To improve the development level of artificial intelligence, information technology, and the application level of artificial intelligence in China; to solve the problem of the relatively low level of street space design in the current city, especially in small towns, to meet the needs of people's cultural life; to promote the improvement of the level of artificial intelligence in China for the convenience of civil life, and to further improve the level of street space design in China.
 METHODS: Firstly discusses the theories, such as the principles of street space design in small cities, to illustrate the necessity of integrating AI into street space design in small towns; secondly, the model established by AI is used to optimize the street space in small cities; finally, the feasibility of integrating AI design into street space design in small towns is illustrated through cases and examples.
 RESULTS: Street space design in small cities in China needs more design aesthetics, and artificial intelligence needs to be integrated into street space design. Artificial intelligence has a noticeable optimization effect on street space design, and using artificial intelligence can improve the design level of limited space. Also illustrates that artificial intelligence can be better integrated into the street space design of small cities through cases.
 CONCLUSION: In the context of the digital era, the street space design of small cities needs to apply artificial intelligence. In modernization-oriented urban construction, the street space design level of small towns should be further strengthened to improve the modernization level of cities.

K-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence

This paper describes a non-destructive detection method for identifying cable defects using K-mer frequency encoding. The detection methodology combines magnetic leakage detection equipment with artificial intelligence for precise identification. The cable defect identification process includes cable signal acquisition, K-mer frequency encoding, and artificial intelligence-based identification. A magnetic leakage detection device detects signals via sensors and records their corresponding positions to obtain cable signals. The K-mer frequency encoding method consists of several steps, including cable signal normalization, the establishment of K-mer frequency encoding, repeated sampling of cable signals, and conversion for comparison to derive the K-mer frequency. The K-mer frequency coding method has advantages in data processing and repeated sampling. In the identification step of the artificial intelligence identification model, an autoencoder model is used as the algorithm, and the K-mer frequency coding method is used to introduce artificial parameters. Proper adjustments of these parameters are required for optimal cable defect identification performance in various applications and usage scenarios. Experiment results show that the proposed K-mer frequency encoding method is effective, with a cable identification accuracy rate of 91% achieved through repeated sampling.

Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses

ObjectiveThe unique structure of human teeth limits dental repair to custom-made solutions. The production process requires a lot of time and manpower. At present, artificial intelligence (AI) has begun to be used in the medical field and improve efficiency. This study attempted to design a variety of dental restorations using AI and evaluate their clinical applicability. MethodsUsing inlay and crown restoration types commonly used in dental standard models, we compared differences in artificial wax-up carving (wax-up), artificial digital designs (digital) and AI designs (AI). The AI system was designed using computer calculations, and the other two methods were designed by humans. Restorations were made by 3D printing resin material. Image evaluations were compared with cone beam computed tomography (CBCT) by calculating the root mean squared error. ResultsSurface truth results showed that AI (68.4 µm) and digital-designed crowns (51.0 µm) had better reproducibility. Using AI for the crown reduced the time spent by 400% (compared to digital) and 900% (compared to wax-up). Optical microscopic and CBCT images showed that AI and digital designs had close margin gaps (p < 0.05). The margin gap of the crown showed that the wax-up group was 4.1 and 4.3 times greater than those of the AI and digital crowns, respectively. Therefore, the utilization of artificial intelligence can assist in the production of dental restorations, thereby enhancing both production efficiency and accuracy. SignificanceIt is expected that the development of AI can contribute to the reproducibility, efficiency, and goodness of fit of dental restorations.

The evolution of Chinese traditional ethnic clothing design style based on interactive dichroism algorithm

Abstract In this paper, a traditional ethnic clothing design style model is proposed, aiming to promote the good development of clothing in artificial intelligence design. The model uses an interactive dichotomous meter algorithm for top-down information gain, differentiates sample attributes by selecting entropy values, and defines an average categorical fuzzy subset based on discrete attribute leaf node division for deep, circular arithmetic decision-making. After satisfying the clothing design evolution condition, the minimum prediction entropy value genus block is extended, and the feedback matrix random input variables are used to induce the regularized likelihood frequency classification value distribution. The results of the analysis of traditional ethnic clothing design show that there are {0, 0.5, 1} mathematical expressions on clothing design hues, the demand of clothing design value subject shows a positive state, the change of consumption wave that festival and style will bring, the knowledge value in the mean difference of clothing design orientation is as high as 0.9173 with a significant value of 0.00&lt;0.05. Therefore, the design style of traditional ethnic clothing is not only the inheritance and promotion of national culture but also the enhancement and development of aesthetic consciousness.

ChatGPT Isn't Magic

ChatGPT Isn't Magic

Algorithmic bias in anthropomorphic artificial intelligence: Critical perspectives through the practice of women media artists and designers

Current research in artificial intelligence (AI) sheds light on algorithmic bias embedded in AI systems. The underrepresentation of women in the AI design sector of the tech industry, as well as in training datasets, results in technological products that encode gender bias, reinforce stereotypes and reproduce normative notions of gender and femininity. Biased behaviour is notably reflected in anthropomorphic AI systems, such as personal intelligent assistants (PIAs) and chatbots, that are usually feminized through various design parameters, such as names, voices and traits. Gendering of AI entities, however, is often reduced to the encoding of stereotypical behavioural patterns that perpetuate normative assumptions about the role of women in society. The impact of this behaviour on social life increases, as human-to-(anthropomorphic)machine interactions are mirrored in human-to-human social interactions. This article presents current critical research on AI bias, focusing on anthropomorphic systems. Moreover, it discusses the significance of women’s engagement in AI design and programming, by presenting selected case studies of contemporary female media artists and designers. Finally, it suggests that women, through their creative practice, provide feminist and critical approaches to AI design which are essential for imagining alternative, inclusive, ethic and de-biased futures for anthropomorphic AIs.

Developments in Image Processing Using Deep Learning and Reinforcement Learning.

The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

Ethics by design for artificial intelligence

AbstractIn this paper, we present an approach for the systematic and comprehensive inclusion of ethical considerations in the design and development process of artificial intelligence systems, called Ethics by Design for AI (EbD-AI). The approach is the result of a three-year long research effort, and has recently be adopted by the European Commission as part of its ethics review procedure for AI projects. We describe and explain the approach and its different components and its application to the development of AI software and systems. We also compare it to other approaches in AI ethics, and we consider limitations of the approach as well as potential criticisms.

Algorithmic extremism? The securitization of artificial intelligence (AI) and its impact on radicalism, polarization and political violence

Artificial intelligence is often framed as a tool to be used to counter violent extremism. This paper seeks to make a contribution to scholarship on the use of AI by considering the other side of the debate: how AI and algorithms themselves can and are being used to radicalize, polarize, and spread racism and political instability. The central argument of the paper is that AI and algorithms are not just tools deployed by national security agencies to prevent malicious activity online, but contributors to polarization, radicalism and political violence. Further to this, securitization processes have been instrumental in how AI has been designed, used and to the harmful otucomes that it has generated. The paper begins with an analysis of the connections between AI, polarization, radicalism and political violence. Drawing on the ‘Copenhagen School’ of International Relations theory, it then moves on to an empirical assessment of how AI has been securitized throughout its history, and in media and popular culture depictions, and by exploring a number of modern examples of AI having polarizing, radicalizing effects that have contributed to political violence. The third section of the article examines AI technology itself, arguing that problems exist in the design of AI, the data that it relies on, how it is used, and in its outcomes and impacts. The final section draws conclusions and policy implications, arguing that a reconceptualisation of AI-enabled security is necessary that is more attuned to the human, social and psychological impacts of the technology.

A Quantum-like Model of Interdependence for Embodied Human-Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty.

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human-machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels-some low, and others high. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few results that can be generalized to human-machine interactions; by the replication crisis of today (e.g., the invalid scale for self-esteem); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us theorization about the bistability of mind and body, but it poses a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We review the science of teams and human-machine team research in the laboratory versus in the open field; justifications for rejecting traditional social science while supporting our approach; a fuller understanding of the complexity of teams and tasks; the mathematics involved; a review of results from our quantum-like model in the open field (e.g., tradeoffs between team structure and performance); and the path forward to advance the science of interdependence and autonomy.

Trading Degrees of Freedom: User Control in Pens, GPT, and Automated Vehicles

Technology development often has profound consequences for human agency. Considering the evolution of seemingly mundane tools, such as pens, can help us anticipate the consequences of emerging technology. We consider how the evolution of pens might guide automation and artificial intelligence (AI) design. Automation and AI change activities by trading degrees of freedom—expanding, eliminating, or resolving—to transform intent into action. The evolution of pens shows three approaches to trading degrees of freedom. A technology-based approach eliminates degrees of freedom to improve efficiency A human-centered approach trades degrees of freedom to enhance human agency. A system-centered approach trades degrees of freedom to address domain entropy and promote resilience. We link trading degrees of freedom to concrete design considerations with examples from pens, large language models, and vehicle automation. We hope that analyzing a technology far removed from AI and vehicle automation will provoke new discussions of humanautomation interaction.

Impact of Social Emotional Intelligence on Students' Interpersonal Relationships and Academic Development

The paper explores the intersection of Artificial Intelligence (AI) and mobile homestay design, elucidating AI’s multifaceted role in optimizing space, fostering sustainability, and enhancing user experience within this specific context. Through a comprehensive literature review, theoretical framework development, and detailed case studies, the paper unveils the significant potential and challenges of implementing AI in mobile homestay design. The case studies spotlight AI’s ability to dynamically optimize small spaces, promote sustainable practices, and tailor user experiences, providing invaluable insights for designers and researchers alike. However, alongside its potential, the ethical considerations, including privacy, security, and bias, are scrutinized, emphasizing the necessity for responsible and ethical AI deployment. Furthermore, the review of various AI tools and techniques provides practical insights for practitioners in the field. The paper concludes by highlighting areas for future research, particularly in developing ethical frameworks and exploring diverse AI applications in various mobile homestay contexts, to further understand and leverage the potent synergy between AI and design in crafting innovative, sustainable, and user-centric spaces.

Automatic transparency evaluation for open knowledge extraction systems

BackgroundThis paper proposes Cyrus, a new transparency evaluation framework, for Open Knowledge Extraction (OKE) systems. Cyrus is based on the state-of-the-art transparency models and linked data quality assessment dimensions. It brings together a comprehensive view of transparency dimensions for OKE systems. The Cyrus framework is used to evaluate the transparency of three linked datasets, which are built from the same corpus by three state-of-the-art OKE systems. The evaluation is automatically performed using a combination of three state-of-the-art FAIRness (Findability, Accessibility, Interoperability, Reusability) assessment tools and a linked data quality evaluation framework, called Luzzu. This evaluation includes six Cyrus data transparency dimensions for which existing assessment tools could be identified. OKE systems extract structured knowledge from unstructured or semi-structured text in the form of linked data. These systems are fundamental components of advanced knowledge services. However, due to the lack of a transparency framework for OKE, most OKE systems are not transparent. This means that their processes and outcomes are not understandable and interpretable. A comprehensive framework sheds light on different aspects of transparency, allows comparison between the transparency of different systems by supporting the development of transparency scores, gives insight into the transparency weaknesses of the system, and ways to improve them. Automatic transparency evaluation helps with scalability and facilitates transparency assessment. The transparency problem has been identified as critical by the European Union Trustworthy Artificial Intelligence (AI) guidelines. In this paper, Cyrus provides the first comprehensive view of transparency dimensions for OKE systems by merging the perspectives of the FAccT (Fairness, Accountability, and Transparency), FAIR, and linked data quality research communities.ResultsIn Cyrus, data transparency includes ten dimensions which are grouped in two categories. In this paper, six of these dimensions, i.e., provenance, interpretability, understandability, licensing, availability, interlinking have been evaluated automatically for three state-of-the-art OKE systems, using the state-of-the-art metrics and tools. Covid-on-the-Web is identified to have the highest mean transparency.ConclusionsThis is the first research to study the transparency of OKE systems that provides a comprehensive set of transparency dimensions spanning ethics, trustworthy AI, and data quality approaches to transparency. It also demonstrates how to perform automated transparency evaluation that combines existing FAIRness and linked data quality assessment tools for the first time. We show that state-of-the-art OKE systems vary in the transparency of the linked data generated and that these differences can be automatically quantified leading to potential applications in trustworthy AI, compliance, data protection, data governance, and future OKE system design and testing.

The media inequality, uncanny mountain, and the singularity is far from near: Iwaa and Sophia robot versus a real human being

Design of Artificial Intelligence and robotics habitually assumes that adding more humanlike features improves the user experience, mainly kept in check by suspicion of uncanny effects. Three strands of theorizing are brought together for the first time and empirically put to the test: Media Equation (and in its wake, Computers Are Social Actors), Uncanny Valley theory, and as an extreme of human-likeness assumptions, the Singularity. We measured the user experience of real-life visitors of a number of seminars who were checked in either by Smart Dynamics’ Iwaa, Hanson's Sophia robot, Sophia's on-screen avatar, or a human assistant. Results showed that human-likeness was not in appearance or behavior but in attributed qualities of being alive. Media Equation, Singularity, and Uncanny hypotheses were not confirmed. We discuss the imprecision in theorizing about human-likeness and rather opt for machines that ‘function adequately.’

Basic values in artificial intelligence: comparative factor analysis in Estonia, Germany, and Sweden

AbstractIncreasing attention is paid to ethical issues and values when designing and deploying artificial intelligence (AI). However, we do not know how those values are embedded in artificial artefacts or how relevant they are to the population exposed to and interacting with AI applications. Based on literature engaging with ethical principles and moral values in AI, we designed an original survey instrument, including 15 value components, to estimate the importance of these values to people in the general population. The article is based on representative surveys conducted in Estonia, Germany, and Sweden (n = 4501), which have varying experiences with implementing AI. The factor analysis showed four underlying dimensions of values embedded in the design and use of AI: (1) protection of personal interests to ensure social benefit, (2) general monitoring to ensure universal solidarity, (3) ensuring social diversity and social sustainability, and (4) efficiency. We found that value types can be ordered along the two dimensions of resources and change. The comparison between countries revealed that some dimensions, like social diversity and sustainability evaluations, are more universally valued among individuals, countries, and domains. Based on our analysis, we suggest a need and a framework for developing basic values in AI.

Artificial Intelligence in the Diagnosis of Maxillofacial Disorders

Dear Editor, Recently, studies and research have focused on the use of artificial intelligence (AI) in medical science [1,2]. It is probable that the healthcare industry, especially radiology, is a step or two ahead of the curve when using convolutional neural networks in clinical practice. The number of investigations into the use of radiography in daily life continues to grow, as does the number of accessible methods that have already impacted the issue of patient care, both of which are on the rise. In addition, there is a whole area devoted to Medical Imaging using AI. Additionally, a dedicated domain has emerged, focusing on the synergy between AI and Medical Imaging, particularly in the context of diagnosing Maxillofacial Disorders. The diagnosis is made based on the patient’s medical history, linked testing, and other susceptible variables, known to be risk factors for human memory retention. AI from human professionals performs much better than human specialists when using primary health data [3]. A study indicated that by using AI in conjunction with clinical diagnostics, the accuracy and efficiency of diagnosis might be significantly improved. Recently, machine learning techniques have been used to diagnose several illnesses, including tumors, cancer, and metastases, among others. These algorithms demonstrated excellent reliability in distinguishing periapical cysts from keratocystic odontogenic tumors when manually created parameters [4] were used in their development. However when these approaches, such as convolutional neural network (CNN), were employed to examine the cytological pictures gathered, they revealed an inadequate performance error in identifying malignant lesions of the mouth. Although these results are hopeful, existing AI algorithms for diagnosing oral and maxillofacial lesions predominantly rely only on a single type of data, cytopathological reports. Using models that include the patient’s medical history is critical for a more accurate analysis [5]. Deep learning (DL) and CNN have made significant contributions to AI in caries and endodontics because of their capacity to automate waste categorization and classification. To classify radiographs or photographs, several criteria, including comparable qualities, are used to separate them into many discontinuous sections [6]. This process results in predictable data being generated from unpredictable data. Using understanding network (U-Net), the DL categorizes the cone beam computed tomography (CBCT) vertices into “lesions,” “tooth structures,” “bones,” “restorative materials,” and “backgrounds,” with the findings comparable to the diagnosis of total lesions. Apical is a company that supplies doctors [7]. Distal caries lesions may also be detected by DL using imaging data [8]. The clinical signs and symptoms that the patient exhibits are crucial in diagnosing temporomandibular disorders (TMD). It is a method for converting spoken language into an ordered computer language known as speech processing. It was found that constructing a software model based on the sorts of words used in the patient’s speech and the size of their mouth was more successful than using the actual mode [9]. A full degree of agreement between AI and the physician is shown in AI’s identification of condyle morphology. Reviewing these articles was instructive since it provided us with an opportunity to observe the diverse range of approaches that have been created and assessed across a diverse range of images and experiences. However, it is important to note that no one has determined how these approaches will be integrated into a clinical workflow or, more importantly, whether and how they will impact radiologists’ diagnostic accuracy and efficiency, and consequently, patient outcomes. Therefore, it is difficult to predict which ones will be implemented in a clinical environment. As underscored by the study findings, continued research endeavors are imperative to harness the full potential of AI in transforming the landscape of diagnosing Maxillofacial Disorders. Best regards,

Retracted: Design and Research of Artificial Intelligence in Multimedia Intelligent Question-Answering System and Self-Test System

Retracted: Design and Research of Artificial Intelligence in Multimedia Intelligent Question-Answering System and Self-Test System

From Artificial Design to Artificial Intelligent Design: Process Model of Digital Reinvention

From Artificial Design to Artificial Intelligent Design: Process Model of Digital Reinvention

Conceptual Design of Artificial Intelligence Powered Automated Space Debris Remover (ASDR)

The focus of this research article is the application of AI technology for space debris removal. With the increasing number of non-functional artificial objects orbiting Earth, commonly known as space debris or junk, the need to address this issue has become crucial. Currently, there are over 200,000 such objects posing a significant threat to operational satellites and space missions. To mitigate this risk, we propose the utilization of AI-trained robotic systems to safely de-orbit space debris, thereby reducing the probability of collisions and potential damage. This research paper introduces the concept of employing AI and robotics technology to achieve this objective, with a specific emphasis on targeting Point Nemo as a designated disposal location. By harnessing the capabilities of AI, we can effectively identify and track space debris, enabling the robotic system to navigate and remove debris from critical orbital paths safely. Furthermore, the re-entry trajectory directed towards Point Nemo ensures that approximately 95% of the debris will burn up upon atmospheric re-entry, thus minimizing any potential risks to human life and property.

Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup

In this white paper, the ACR Pediatric AI Workgroup of the Commission on Informatics educates the radiology community about the health equity issue of the lack of pediatric artificial intelligence (AI), improves the understanding of relevant pediatric AI issues, and offers solutions to address the inadequacies in pediatric AI development. In short, the design, training, validation, and safe implementation of AI in children require careful and specific approaches that can be distinct from those used for adults. On the eve of widespread use of AI in imaging practice, the group invites the radiology community to align and join Image IntelliGently (www.imageintelligently.org) to ensure that the use of AI is safe, reliable, and effective for children.