Understanding and Creating Art with AI: Review and Outlook

It is with great pleasure that we present this special issue of ShodhKosh: Journal of Visual and Performing Arts titled “AI-Driven Creativity and Intelligent Practices in Visual Arts.” As artificial intelligence and intelligent design systems increasingly shape contemporary artistic practice, visual arts are evolving into data-driven, interactive, and computational processes. The contributions in this issue examine how AI influences artistic creation, pedagogy, curation, and creative management. Reflecting interdisciplinary perspectives from visual arts, computational intelligence, emotion analytics, and education technology, this special issue documents and critically evaluates emerging paradigms, offering scholars and practitioners an academic platform to explore innovation in creative practice. Issue Editor: Dr. Krishna Sankar KusumaProfessor, AJK Mass Communication Research Centre, Jamia Millia Islamia, New Dehli, IndiaEmail: kusumakk@gmail.com Dr. Naresh KshetriAssistant Professor (Cybersecurity), Department of Math, CS & IT, Lindenwood University, USAEmail: kshetrinaresh@gmail.com Dr. Gabriel KabandaAdjunct Professor of Machine Learning, Woxsen University, Hyderabad, IndiaEmail: gabrielkabanda@gmail.comStephen Olatunde OlabiyisiProfessor, Department of Computer Science Ladoke Akintola University of Technology Ogbomoso, NigeriaEmail: tundeolabiyisi@gmail.com Dr. Dipti ChauhanProfessor & Head in the Department of Artificial Intelligence & Data Science, Prestige Institute of Engineering Management & Research, Indore, M.P., IndiaEmail: diptichauhan09@gmail.com

Using GANs to Generate Lyric Videos

Acknowledgments Introduction: Reviewing Visual Arts Research - Graeme Sullivan Changing Demands of Visual Arts Theory and Practice - Graeme Sullivan Limitations of Current Visual Research Methodologies - Graeme Sullivan Art Practice as Research - Graeme Sullivan Strategies for Using Art Practice as Research - Graeme Sullivan Part 1: Contexts for Visual Arts Research CONTEXTS FOR VISUAL ARTS RESEARCH PART ONE CONTEXTS FOR VISUAL ARTS RESEARCH PART ONE CONTEXTS FOR VISUAL ARTS RESEARCH PART ONE CONTEXTS FOR VISUAL ARTS RESEARCH Contexts For Visual Arts Research - Graeme Sullivan 1. Pigment to Pixel - Graeme Sullivan The Enlightenment as a Research Project - Graeme Sullivan Promise of Progress - Graeme Sullivan Fractured Realities - Graeme Sullivan Conclusion - Graeme Sullivan 2. Paradigms Lost - Graeme Sullivan Method as Truth - Graeme Sullivan Doubting Doctrines - Graeme Sullivan Conclusion - Graeme Sullivan Part 2: Theorizing Visual Arts Practice - Graeme Sullivan 3. Explanation, Understanding, and Beyond - Graeme Sullivan Theorizing in Practice - Graeme Sullivan Theorizing Visual Arts as Practice-Based Research - Graeme Sullivan Theorizing Art Practice as Transformative Research - Graeme Sullivan Visual Arts Research Practices - Graeme Sullivan Conclusion - Graeme Sullivan 4. Visual Knowing - Graeme Sullivan Visual Cognition - Graeme Sullivan Thinking Practices in the Visual Arts - Graeme Sullivan Visual Arts Knowing: A Framework - Graeme Sullivan Visual Arts as Transcognitive Practice - Graeme Sullivan A Case Study: Critical Influence - Graeme Sullivan Conclusion - Graeme Sullivan 5. Artist as Theorist - Graeme Sullivan Sites of Practice - Graeme Sullivan Reemergence of the Artist-Theorist - Graeme Sullivan Critical Perspectives and Practices - Graeme Sullivan Conclusion - Graeme Sullivan Part 3: Visual Arts Research Practices - Graeme Sullivan 6. Practice as Theory - Graeme Sullivan A Framework for Visual Arts Research Projects - Graeme Sullivan Visual Practice: Experiences - Graeme Sullivan Empirical Inquiry: Exercises - Graeme Sullivan Interpretive Discourse: Encounters - Graeme Sullivan Critical Process: Enactments - Graeme Sullivan Conclusion - Graeme Sullivan Epilogue: Conclusions and Beginnings - Graeme Sullivan Chipping Away - Graeme Sullivan Uncertain Conclusions - Graeme Sullivan References - Graeme Sullivan Index About the Author

Globally, the use of artificial intelligence (AI) is expanding exponentially. The issue of managing intellectual property in AI is raised by this surge. Discussions and moderating have taken place, but no resolution has been reached. The issue of whether the work created by an AI should get a special status still exists. When it comes to the control of IPR in artificial intelligence, there are a few oddities. The ownership of patents and copyrights is in doubt, and there are serious worries about the consequences of violation. With the development of technology, there is no certainty on the law, despite existing international accords and conventions. In the absence of artificial intelligence (AI), intellectual property (IP) rules have, up to this point, treated IP as a product of human cognition. The current boom in AI, which has seen the development of IP from the "intelligence" of software, has upended this base. But the non availability of law on works created by artificial intelligence the law is left far behind to deal with the anomalies created by the intersection of “Artificial intelligence and intellectual property rights”. This research paper examines the role of intellectual property theories in addressing AI anomalies, focusing on copyright, patent, and trademark laws. It analyzes utilitarianism, labour theory and personality theory, examining authorship and ownership in AI-generated works and potential conflicts between human creators and machine-generated content. In this research paper researcher(s) will- Analyse various theories of Intellectual properties to tackle the anomolities created by the intersection of IPR and AI. Criticize and suggest changes in the theories to make them helpful for emerging frameworks on AI and IPR.

The intersection of artificial intelligence (AI) and industrial ecology (IE) is gaining significant attention due to AI's potential to enhance the sustainability of production and consumption systems. Understanding the current state of research in this field can highlight covered topics, identify trends, and reveal understudied topics warranting future research. However, few studies have systematically reviewed this intersection. In this study, we analyze 1068 publications within the IE–AI domain using trend factor analysis, word2vec modeling, and top2vec modeling. These methods uncover patterns of topic interconnections and evolutionary trends. Our results identify 71 trending terms within the selected publications, 69 of which, such as “deep learning,” have emerged in the past 8 years. The word2vec analysis shows that the application of various AI techniques is increasingly integrated into life cycle assessment and the circular economy. The top2vec analysis suggests that employing AI to predict and optimize indicators related to products, waste, processes, and their environmental impacts is an emerging trend. Lastly, we propose that fine‐tuning large language models to better understand and process data specific to IE, along with deploying real‐time data collection technologies such as sensors, computer vision, and robotics, could effectively address the challenges of data‐driven decision‐making in this domain.

PurposeThis paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all.Design/methodology/approachThis paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL.FindingsImpact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices.Practical implicationsTo enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research.Originality/valueThis paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.

SummaryThe recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI—as a tool versus agent—with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders.

Introduction:Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process.Methods:An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning.Results:When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well.Discussion:Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved.

In this article, we embark on a journey exploring the impact of Artificial Intelligence (AI) on various art forms such as painting, sculpture, photography, and illustration. The beginning of this document serves as a brief overview, covering the historical evolution of AI in the art world and discussing theories at the intersection of AI and traditional artistic practices. It also provides a thorough examination of case studies, showcasing artists, who have integrated AI techniques into their usual mediums. The investigation closely looks at the use of AI and discerns differences in approaches among these artists. Moreover, we have conducted a detailed inquiry into the ethical and sociocultural implications of the AI integration in traditional art, touching on concerns about authorship, ownership, and cultural appropriation. The discussion extends to the impact on the art market and the industry. This exploration encompasses the various aspects that AI introduces to conventional art forms. Regarding illustration, the piece delves into the use of AI algorithms, guiding the creation of digital artworks and supporting artists in the creative process. Notably, these algorithms play a crucial role in illustration, prompting discussions about originality, creative innovation, and AI as a tool to enhance artistic creativity. The conclusion summarizes key findings, resonating not only in the art world but also having broader implications for the visual arts and media industry. The intersection of AI and traditional art forms presents both opportunities and challenges, poised to reshape the landscape of creative expression and artistic endeavor.

The intersection of nonlinear analysis and artificial intelligence (AI) presents a promising frontier in the rapidly changing field of technology, with the potential to yield ground-breaking solutions in a multitude of fields. In order to investigate how different disciplines may work together to transform technological progress, this study explores the synthesis of these fields. Deeper comprehension of complicated patterns and behaviours is made possible by the intersection of AI and nonlinear analysis, which can both model complex systems and events. This combination has the potential to go beyond conventional linear thinking and open doors to solve complex problems. Through the utilisation of AI's learning skills to augment nonlinear models, a novel approach to problem-solving is revealed. The purpose of the paper is to examine the applications of this synergy in various disciplines. The combination of nonlinear analysis and AI holds the potential to transform a number of industries, from banking and predictive analytics to biological systems research and autonomous vehicle development. This investigation also includes the societal and ethical ramifications of using such cutting-edge technologies. It aims to provide a thorough understanding of the factors necessary for the ethical integration of these advances into our lives by delving into concerns of privacy, prejudice, and responsible use. This study tries to give academics, engineers, and innovators a full road map by looking at recent advances and possible future possibilities. It seeks to stimulate novel research directions, interdisciplinary teamwork, and the advancement of ground-breaking technical innovations that have the potential to fundamentally alter our understanding of and interactions with the environment around us.

The intersection of artificial intelligence (AI) and acoustics strikes a perfect chord between innovation and responsibility. AI has the potential to transform the field of acoustics by streamlining tasks like searching through drawings or automating mechanical noise calculations, making complex information more accessible and actionable. However, with great potential comes great responsibility. While AI offers substantial benefits, errors in its outputs have, rightfully so, raised public skepticism. As acoustic professionals, our expertise remains paramount: AI has the ability to augment our work as a tool and should not replace the human touch and creative design process. The real challenge lies in implementing AI ethically, securely, and accurately. Ensuring AI systems are transparent, unbiased, and accountable is critical for fostering trust both internally and with our clients. In addition, safeguarding data privacy and avoiding over-reliance on AI in key acoustic design decisions are equally important. Through research and experiments, this paper explores how AI can enhance acoustic consulting, focusing on responsible use to maximize benefits while minimizing risks. The future of AI in acoustics shows vast potential, which can be harnessed successfully if we approach it with caution, creativity, and strong ethical practices.

This interdisciplinary volume introduces new theories and ideas on creativity from the perspectives of science and art. Featuring contributions from leading researchers, theorists and artists working in artificial intelligence, generative art, creative computing, music composition, and cybernetics, the book examines the relationship between computation and creativity from both analytic and practical perspectives. Each contributor describes innovative new ways creativity can be understood through, and inspired by, computers. The book tackles critical philosophical questions and discusses the major issues raised by computational creativity, including: whether a computer can exhibit creativity independently of its creator; what kinds of creativity are possible in light of our knowledge from computational simulation, artificial intelligence, evolutionary theory and information theory; and whether we can begin to automate the evaluation of aesthetics and creativity in silico. These important, often controversial questions are contextualised by current thinking in computational creative arts practice. Leading artistic practitioners discuss their approaches to working creatively with computational systems in a diverse array of media, including music, sound art, visual art, and interactivity. The volume also includes a comprehensive review of computational aesthetic evaluation and judgement research, alongside discussion and insights from pioneering artists working with computation as a creative medium over the last fifty years. A distinguishing feature of this volume is that it explains and grounds new theoretical ideas on creativity through practical applications and creative practice. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future.

Since February 24, 2022, the discourse of visual arts in Ukraine has been characterised by a significant focus on revising and rethinking the topic of the ongoing war in the country since 2014. The author analyses the forms of art representation of the war in Ukraine based on visual projects and art practices in the dimension of existential resistance to cruel reality, which contribute to the narrative of Ukraine’s resistance. The article discusses the correlations between the art discourse and the figurative topic of war, which has become an integral part of the art landscape. The article suggests analysing the events in Ukraine during 2022–2023 in the paradigm of cruel optimism, which is exemplified by the art projects by Alexander Krolikowski, Fedir Alexandrovich, Kateryna Yakovlenko, Anton Logov, and others. A crucial aspect of the research involves delineating the inherent characteristics of the new imagery and language in visual arts. This allows the author to posit the realisation of the concept of “zero” language, which lacks conventional forms of expressiveness but embodies an objectification of depicted signs and motifs associated with archetypal structures and linked to thanatological discourse. It has been demonstrated that the use of thanatological discourse in visual art practices was the impetus for finding new vitality in the pathological reality of military necrophilia. The art discourse takes on an objective ground, with a clear demarcation line between the living and the dead, one’s own and alien, cruel and optimistic, etc.

The 2019 Federal Cybersecurity Research and Development Strategic Plan highlighted the mutual needs and benefits of artificial intelligence (AI) and cybersecurity. AI techniques are expected to enhance cybersecurity by assisting human system managers with automated monitoring, analysis, and responses to cybersecurity attacks. Conversely, it is essential to guard AI technologies from unintended uses and hostile exploitation by leveraging cybersecurity practices. Research results at the intersection of AI and cybersecurity can help us to be better equipped with tools and techniques to tackle the growing cybersecurity challenges, while also presenting an opportunity to devise fundamentally new ways to motivate and educate students about cybersecurity in the age of AI. Likewise, a June 2019 technical workshop on 'Artificial Intelligence and Cybersecurity: Opportunities and Challenges' noted how the interplay between AI, machine learning, and cybersecurity will continue to introduce new opportunities and challenges in the security of AI as well as AI for cybersecurity. Basic research at the intersection of AI, cybersecurity, and education has the potential to expand existing AI opportunities and resources in cybersecurity education and workforce development. Education efforts are needed to foster workforce knowledge and skills about applying AI expertise to cybersecurity as well as building robust and trustworthy AI. This BOF session will bring together researchers who are interested in these collaborative explorations.

Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023. We performed co-occurrence keywords and co-authorship network analyses using VOSviewer software to explore the relationships between keywords and country collaboration. We found a remarkable surge in articles applying AI in ophthalmology after 2017, marking a turning point in the integration of AI within the medical field. The primary application of AI shifted towards the diagnosis of ocular disease, which was particularly evident through keywords such as glaucoma, diabetic retinopathy and age-related macular degeneration. Analysis of the collaboration networks of countries revealed a global expansion of ophthalmology-related AI research. This study provides valuable insights into the evolving landscape of AI integration in ophthalmology, indicating its growing potential for enhancing disease detection, diagnosis, treatment planning and monitoring of disease progression. In order to translate AI technologies into clinical practice effectively, it is imperative to comprehend the evolving research trends and advancements at the intersection of AI and ophthalmology.