Creation Of Artificial Intelligence Research Articles

Modeling creativity in artificial intelligence: possibilities and limits

The article is dedicated to exploring the possibilities and limitations of developing creative artificial intelligence, particularly the ability of machines to determine the level of creativity in the objects they produce. To assess creativity, a three-step model of novelty is proposed, including ontological, subjective, and semantic levels. Three features of creative ideas or artifacts are identified: novelty, unexpectedness, and value. The article describes the model of a competitive generative network called "CAN: Creative Adversarial Networks," which creates new artistic styles and evaluates their novelty. The possibilities and limitations of modeling humor in creative artificial intelligence are discussed. The article analyzes examples of successful work by neural networks that generate jokes and write scripts, showing that the limitation of such systems is the machine's lack of a sense of context, space, and time. Additionally, a crucial condition for successfully writing jokes is the ability to laugh at them; humans can consciously choose the topic and format of humor, while machines lack their own goal-setting. It is shown that the technologies developed to date can be generalized as "weak creative artificial intelligence" since they can create new objects but are not capable of goal-setting and reflection. However, the possibilities of artificial intelligence are constantly expanding, changing our understanding of the limits of modeling natural intelligence.

Creative artificial intelligence and narrative transportation.

Creative artificial intelligence and narrative transportation.

Artificial intelligence: free will, self-consciousness and ethics.

The article discusses key aspects of artificial intelligence creation, including issues of free will, self-awareness and ethics. The focus is on the neurobiological basis of consciousness, in particular the structure and functions of the new cerebral cortex, as well as the mechanisms of recognition, memory and prediction, which are important for modelling cognitive processes in artificial systems. The paper discusses the role of neural networks in reproducing cognitive functions, such as perception and decision making, and presents modern approaches to training neural networks. A separate part of the paper is devoted to the issue of modelling self-awareness and subjective experience in artificial intelligence and how realistic it is to create self-aware machines. Ethical issues of artificial intelligence creation are at the centre of the discussion, including the topics of the rights of self-aware machines, their responsibilities and their role in society. The article considers the possible social consequences of the emergence of artificial personalities, the need to develop new legal frameworks and legal protections for such beings. It also discusses the problem of free will in the context of both biological and artificial systems, citing experiments and philosophical theories that question free will as a phenomenon. It concludes that the creation of artificial intelligence has great potential, but requires careful ethical and legal analysis to ensure the harmonious integration of artificial persons into social and legal structures.

A Mathematical Model to Enhance Creativity in Generative AI Systems

People often think it will be hard for artificial intelligence (AI) systems to copy creativity because it is a complicated and important part of human intelligence. Generative AI, which tries to make new and useful things, has shown promise in being able to copy creative processes. However, generative AI systems that are already in use often have trouble making results that are truly original. This is mostly because there isn't a clear scientific framework to help people be creative. We present a new mathematical model to improve creativity in creative AI systems in this work. Our model is based on the ideas of lateral and convergent thinking, which are important parts of how people think creatively. Divergent thinking means coming up with many ideas or solutions, while convergent thinking means picking the best ones and making them even better. To put our model into action, we come up with the idea of a "creativity score," which measures how new and useful the results are. The creativity score is determined by looking at a number of things, such as the variety of outputs, how different they are from current solutions, and how useful they are for fixing the problem at hand. Finding the right balance between divergent and convergent thinking is one of the hardest parts of making generative AI systems more creative. Divergent thinking is important for coming up with many ideas, but convergent thinking is needed to pick out the best ones and make them even better. This problem is solved by our model, which uses both divergent and convergent thought. To make sure our model works, we test it on different creative AI tasks, such as making images and writing text. Our results show that our model can greatly improve the creativity of generative AI systems, resulting in more varied, unique, and useful outputs.

Artificial Intelligence (AI) in Customer Service: Revolutionising Support and Engagement

Customer service, marketing, human resource management, finance, accounting, product and service development, healthcare, commerce, and manufacturing are just a few of the areas where artificial intelligence (AI) has completely changed the game. AI enhances decision-making and work processes by means of machine learning algorithms, automation, and predictive analytics. AI makes it possible to personalise, target adverts accurately, and forecast sales in marketing. Through recruiting, performance reviews, and training of employees, AI in human resource management raises productivity and engagement. Demand forecasting, inventory control, and condition based monitoring are all made easier in factory management by artificial intelligence. Maintaining compliance requirements, AI also helps with risk reduction, financial reporting, and fraud prevention. AI enhances consumer happiness and competitiveness in product creation via use of data analytics, modelling, and suggestions. AI also aids in strategic planning and decision-making in healthcare and life sciences. In retail and e-business, AI improves stocking management, customer profiling, and shopping experiences. This review examines AI in Customer Service: Revolutionising Support and Engagement. We utilised relevant published data (2004–2014) from diverse, reliable databases. Findings suggest that other trends like creative AI, XAI, and quantum computing, as well as collaboration between human beings and AI, continue to advance. As a result, ethical concerns remain a critical element to address when it comes to the application of AI, identifying both threats and opportunities. Finally, we note that AI continues to be a formidable and revolutionary force in organisations, enhancing value creation while promoting ethical principles. Keywords: Artificial Intelligence, Customer Service, Chatbots, Virtual Assistants, Predictive Analytics, Manufacturing, Creative AI

Integration effect of artificial intelligence and traditional animation creation technology

Abstract Despite the advancements in modern computer hardware and software, the creation of digital animation still demands a substantial investment of both manpower and time. This article aimed to explore how artificial intelligence (AI) technology can be combined with traditional animation creation techniques to achieve better integration effects. By combining intelligent character animation generation with hand drawing, a generative adversarial network was used to achieve high-quality animation generation. The generator generated realistic animations, and the discriminator measured the authenticity of the animations by comparing the differences between the generator-generated animations and the real animations, which was used for automated character animation generation. This can greatly reduce the cost and time of digital animation creation, improve the quality of digital animation, and provide more innovation for the application of traditional animation technology. The average number of audience attracted by characters through blended creation was 122.2% more than that of characters through traditional animation creation and 32.6% more than that of characters through AI creation. This not only helps animation producers complete animation production more quickly, but also enhances the creativity and artistic value of animation.

Examining artificial intelligence literacy among pre-service teachers for future classrooms

In the context of global integration and increasing reliance on Artificial Intelligence (AI) in education, evaluating the AI literacy of pre-service teachers is crucial. As future architects of educational systems, pre-service teachers must not only possess pedagogical expertise but also a strong foundation in AI literacy. This quantitative study examines AI literacy among 529 pre-service teachers in a Nigerian university, utilizing structural equation modeling (SEM) for comprehensive analysis. The research explores various dimensions of AI literacy, revealing that a profound understanding of AI significantly predicts positive outcomes in AI use, detection, ethics, creation, and problem-solving. However, no correlation exists between AI knowledge and emotion regulation or the assumption that active AI use enhances AI detection capabilities. The study identifies a trade-off between AI application and creation, emphasizing the ethical considerations intertwined with emotional and persuasive facets of AI use. It also supports the link between AI creation and problem-solving, emphasizing the foundational role of AI knowledge in shaping diverse aspects of AI literacy among pre-service teachers. The findings offer valuable insights for educators, administrators, policymakers, and researchers aiming to enhance AI literacy in pre-service teacher education programs.

Искусственный интеллект для персонализированного подхода к перкутанной нефролитотрипсии.

Introduction. In recent years, there has been more and more research on the creation of artificial intelligence (AI) to help diagnose and treat patients in various fields of medicine. The use of algorithms allows us to reduce the time for processing large volumes of information and give a «second opinion» to a specialist in complex and non-standard clinical cases. In order to increase the effectiveness of percutaneous nephrolithotripsy, we have created an AI algorithm to personalize approach in the surgical treatment of nephrolithiasis. Purpose. To develop a universal tool for predicting the most optimal tactics of percutaneous nephrolithotripsy depending on the clinical case. Materials and methods. The total number of patients treated at N. Lopatkin Scientific Research Institute of Urology and Interventional Radiology and those who took part in the study amounted to 1000 people. The number of men was 419 (41.9%), women 581 (58.1%). The age of patients included in the studies ranged from 18 to 88 years (52.3±13.47). The division into Training and Holdout partitions varied depending on the target variable to obtain the most accurate result. The maximum difference in the samples was 600n and 400n (60% and 40%), the minimum 800n and 200n (80% and 20%). Data processing was carried out using IBM SPSS Statistics and Modeler programs using the neural network modeling method. Results. The accuracy of prediction for the choice of nephroscope size was 82.2%, requirement for intraoperative stent placement 93.9%, requirement for ureteroscopy or contact ureterolithotripsy 98.5%. The accuracy of the forecast for the number of puncture accesses was 92.6%, for access through the upper group 95%, for access through the middle group 91.2%, for access through the lower group 91.2%. The algorithm allows us to predict the presence of residual stones with an accuracy of 84.1%, the duration of surgical intervention is 87.3%.The number of test observations was 30. The algorithm demonstrated high accuracy of prediction (from 73.8% to 94.5%) of surgical tactics in complex clinical cases. Conclusion. The results of using the algorithm for personalized prognosis in the surgical treatment of nephrolithiasis showed high accuracy and efficiency. Further development of technology to create algorithms for each nosology and surgical interventions will reduce the time of data analysis by the attending physician, provide a «second opinion» to young specialists and standardize the approach to treatment in rare and complex clinical cases.

Leveraging Creative Intelligence for Sustainable Customer Relationship Management (CRM) Solutions: Conceptual paper

The study aims to identify critical dimensions of employee creative intelligence and AI CRM systems and explore their impact on service quality and customer satisfaction in customer relationship management (CRM). The advancement of technology and the potential of artificial intelligence (AI) increase customer expectations regarding service delivery. The conceptual framework illustrates that creative intelligence and AI-generated systems significantly impact service quality and customer satisfaction. The findings will help organisations implement CRM service delivery projects by providing employees with the appropriate skills and competencies. The appropriateness of the system is vital towards CRM solutions.

The innovation paradox: concept space expansion with diminishing originality and the promise of creative artificial intelligence

The innovation paradox: concept space expansion with diminishing originality and the promise of creative artificial intelligence

Synergies of Artificial Intelligence and Mathematics: A Study

Since mathematics is the basis of the models, algorithms, and procedures that let machines process and interpret enormous volumes of data, mathematics plays a crucial role in the creation of artificial intelligence. Consequently, a thorough grasp of the mathematical ideas underlying AI is crucial for professionals wishing to comprehend, develop, and utilize the technology. AI and mathematics are related and enhance one another. The advancement of algorithm development and the solution of challenging issues have been made possible by the incorporation of machine learning into mathematics. In this paper, we study various articles to provide a summary of the relationship between AI and Mathematics.

ПРОФЕСІЙНА КОМПЕТЕНТНІСТЬ ПЕРСОНАЛУ. ПСИХОЛОГІЧНІ ПОКАЗНИКИ. ЇХ ОЦІНКА ТА ВИКОРИСТАННЯ ПІД ЧАС ПРИЙНЯТТЯ РІШЕННЯ ЩОДО СЕРТИФІКАЦІЇ ПРОДУКЦІЇ

Despite the advancing technical progress which resulted into the replacement of human labor by the work of machines and mechanisms, the creation of artificial intelligence that can manage production processes without human intervention, in modern conditions, the skills, knowledge, and experience of a person have no small share in determining the forms, directions, methods and means of improvement, be it the production of products, the provision of services, the implementation of processes. Since teamwork is mostly used in various types of production processes, therefore, of course, the question of relations between team members, their ability to work together for the final result, and their ability to use personal abilities to achieve this result arises. There is no denying the fact that the psychological state of each employee along with his professional abilities create conditions for performing tasks with the least losses and greater efficiency. Optimum arrangement of personnel, taking into account special knowledge, practical experience in performing work, personal qualities regarding the ability to communicate, focus on a certain task, being attentive and observant are the key success factors in any business.

Artificial intelligence capabilities in the context of the author's conception of creativity

The study provides some philosophical reflections on the creativity of artificial in-telligence. The study supposes that artificial intelligence can be considered creative only if it creates something new with help of imagination (or its equivalent) and ap-pealing to the so-called “background” (background and general knowledge, biases, competencies, experience, habits, intuition, prejudices, political preferences, skills, stereotypes, values, and others), and its creative activity must be either necessary or arbitrary. Necessary creative activity is related to the solution of specific tasks, for example, within the framework of technical invention or scientific discovery. Arbitrary creative activity is associated with spontaneous, aimless and inexpedient human activity. This type of creative activity takes place when a creative doer has free time, leisure, enthusiasm (hobby), plays a game or is bored. Based on the two types of creative activity, two types of creative artificial intelligence can be distinguished: a weak creative artificial intelligence that makes necessary creative activities related to the tool nature of artificial intelligence, specifically to effectively solving specific problems and tasks, and a strong creative artificial intelligence that makes arbitrary creative activities, that is, creates for the sake of creating alone. The strong creative artificial intelligence can be possible only if the artificial intelligence is given autono-my, the freedom to manage that autonomy, and learns to manage its freedom.

The rise of CreAltives: Using AI to enable and speed up the creative process

The ancient Greeks invented the concept of the muse goddess to be a vessel that would enter a human’s life and spark long-desired creativity. The Greek gods did not want to give humans credit for coming up with creativity; however, humans may have invented something as good as a muse — artificial intelligence (AI). Today, AI, machine learning (ML), deep learning (DL) and natural language processing (NLP) could be taking up that ‘muse role’ as well as becoming the workhorse for many a visual artist’s grunge work, and possibly even their creative work as well. In this paper, we will look at how modern creative AI technologies can be viewed through the lens of five groups. We will also explore how recent advances in creative AI offer users the ability to create images, compose music, animation and even video in ways never before possible — and then wrap up with final takeaways on the future of artistic creativity in the era of AI.

General Systems Theory and Creative Artificial Intelligence

The article analyzes the possibilities and limitations of artificial intelligence. The article considers the subjectivity of artificial intelligence, determines its necessity for solving intellectual problems depending on the possibility of representing the real world as a deterministic system. Methodological limitations of artificial intelligence, which is based on the use of big data technologies, are stated. These limitations cause the impossibility of forming a holistic representation of the objects of cognition and the world as a whole. As a tool for deterministic description of the universe it is proposed to use empirical-metaphysical general theory of systems, which is an extension of existing general theories of systems due to ontological justification of the phenomenon of isomorphism and definition of a limited set of laws, rules, patterns and primitives of forms and relations of objects in the universe. The distinction of natural (human) and artificial intelligence is considered, including the realization of multisystem integration of intelligence in physical, biological, social and spiritual systems. A philosophically grounded approach to ensuring the evolutionary properties of artificial intelligence is formulated, based on the inclusion of non-equilibrium mechanisms through which stability is realized.

ET-DM: Text to image via diffusion model with efficient Transformer

Text-to-image synthesis is widely used in many applications, such as virtual reality, game development, image editing, etc. It is a challenging task that requires the conversion of natural language descriptions into corresponding images. Text-to-image synthesis techniques based on generative adversarial networks (GANs) have succeeded greatly. However, there are still some challenges in generating high-quality, diverse, and semantically consistent images. To solve these problems. This paper proposes a novel text-to-image synthesis technique (ET-DM) based on a diffusion model and an efficient Transformer. ET-DM technology combines the diffusion model and the high-efficiency Transformer model. On the one hand, the diffusion model is used to simulate the evolution process of pixel values in the image, and the image is generated through repeated iterations. At the same time, it also uses an efficient Transformer model to process text input and generate corresponding images. In image generation, ET-DM technology can control the image at the pixel level to ensure the image’s visual consistency and semantic consistency. In addition, it can generate diverse images by controlling random noise. We conduct experiments on multiple datasets and show that ET-DM outperforms existing methods regarding image quality and diversity while being more computationally efficient. ET-DM represents a promising approach to image generation from textual descriptions, which can find applications in fields such as computer vision, natural language processing, and creative artificial intelligence.

Mind ascribed to AI and the appreciation of AI-generated art

Creative artificial intelligence (AI) has received a lot of attention in recent years. Artworks that are introduced to be generated by AI (rather than a human artist) are, however, often evaluated negatively. Integrating extant research, we suggest that AI is ascribed less mind (i.e. agency and experience) which is responsible for this effect. In two experiments ( N = 176 and N = 381) we observed negative indirect effects of artist information (AI vs human artist) on the appreciation of visual artworks. The AI is consistently ascribed less agency and less experience than a human artist. Higher levels of experience and agency ascribed to an artist are, in turn, associated with higher appreciation of a piece of art. In both experiments the total effect of artist information on appreciation was not significant. Artist information did not predict whether the artwork deviated positively from viewers’ expectations developed before the actual artwork was encountered.

인컨텍스트 러닝을 통한 ChatGPT의 시적 표현 해석 가능성 연구

In 2023, as artificial intelligence(AI) moves beyond the realm of imitation and into the realm of creation, Korean literature is also entering the era of “creative AI,” with Slitscope’s SIA releasing a book of poetry, “시를 쓰는 이유(Why I Write Poetry).” However, the time and cost required to reach such a level is quite burdensome for the general public to attempt.
 Therefore, this study focuses on in-context learning of conversational A.I. that has already been trained through deep learning to explore the possibility of learning about A.I.’s allegory. In particular, by using ChatGPT as a research object, we can gain research advantages in terms of accessibility and functionality over previous studies on the model.
 In order to study the learning potential of ChatGPT in an in-context learning environment, this study consists of a structure of (1) pre-learning verification, (2) output request based on pre-learning, (3) related information input, and (4) verification re-execution of (2) after learning, and examines the possibility of conversational AI learning about poetic alliteration.
 The poetry texts that are input as learning information in the above process are allegorical poetry texts extracted from Yoon Gon-gang’s 『動物詩集』(Hansung Book Co., 1938) because it was judged to have value as the first Korean poetry collection with a single animal theme, which was approached and interpreted from the perspective of satirical and allegorical poetry at the time and even now.
 This study determines the learning potential of literary rhetoric for ChatGPT through the education and output process based on Yoon Gon-kang’s poems, and considers its utilization in various aspects such as education, creation, and education in the future. This will be an exploration of possibilities to solidify the position of literature in the era of artificial intelligence that will become a reality in the future.

The dynamics of AI capability and its influence on public value creation of AI within public administration

Artificial Intelligence (AI) technologies in public administration are gaining increasing attention due to the potential benefits they can provide in improving governmental operations. However, translating technological opportunities into concrete public value for public administrations is still limited. One of the factors hindering this progress is the lack of AI capability within public organisations. The research found that various components of AI capability are essential for successfully developing and using AI technologies, including tangible, intangible, and human-related factors. There is a distinction between the AI capability to develop and the AI capability to implement AI technologies, with more administrations capable of the former but finding difficulties in the latter. A lack of in-house technical expertise to maintain and update the AI systems, legal challenges in deploying developed AI systems, and the capability to introduce changes in the organisation to ensure the system remains operational and used by relevant end-users are among the most critical limiting factors for long-term use of AI by public administrations. The research underlines the strong complementarity between historical eGovernment developments and the capability to deploy AI technologies. The study suggests that funding alone may not be enough to acquire AI capability, and public administrations need to focus on both the capability to develop and implement AI technologies. The research emphasizes that human skillsets, both technical and non-technical, are essential for the successful implementation of AI in public administration.

Philosophical Aspects of the Problem of "Artificial Man" in Fiction

The problem of the creation of artificial man and the creation of artificial intelligence are issues that have now become not just potential, but also actual scientific tasks. The original genetic kinship of philosophy and literature as forms of human culture and meaning formation made it possible to comprehend the most important problems in works rich in ideological content and beautiful in form. The subject of the research is the philosophical aspects of the problem of the creation of artificial man in the classic works of fantasy literature of the XIX-XX centuries. This goal is achieved by consistent consideration and comparison of philosophical and anthropological ideas that can be isolated from the works of Johann Wolfgang Goethe, Mary Shelley, Herbert George Wells and M.A. Bulgakov. Hermeneutical method as interpretation and reconstruction of meanings, comparative historical analysis, philosophical comparative studies are used as the methodological basis of this historical and philosophical research. The novelty of the research lies in the historical and philosophical reconstruction of the problem of creating an artificial person, posed for the first time on the pages of the world art classics. The very idea of creating an artificial man was a continuation of the God-fighting aspirations of the Renaissance and the embodiment of the ardent desire to become not only on a par with the Creator, but also to surpass Him. When creating the image of the homunculus, Goethe also had in mind the contrivance, artificiality and fruitlessness of many enlightenment ideas, because the enlighteners questioned the very existence of God, putting a scientist-creator in His place. Goethe's idea of the futility and danger of experimenting with human nature was later developed by Mary Shelley, H. G. Wells and Mikhail Bulgakov. Wagner's homunculus and the creation of Dr. Frankenstein are the closest to the idea of man that prevailed in the philosophy of Modern times and educational pedagogy. Powerful notes of philosophical foresight of many plot moves of the coming history of mankind sound in the novel by Wells (the creation of Dr. Moreau) and the story of Bulgakov (Sharikov).