Application of generative AI in managing projects for 3D game environment development

Artificial intelligence applied to medicine: There is an “elephant in the room”

Over the last decade, artificial intelligence (AI) technologies have become one of the most sought-after areas of scientific and technological development. This process has also impacted historical science, where the first research in this area began in the 1980s (the so-called first wave) – both in our country and abroad. Then came the "AI winter," and at the beginning of the 2010s, the "second wave" of AI emerged. The subject of this article is the new opportunities for applying AI in history and the new problems arising in this process today, when the main focus of AI has shifted to artificial neural networks, machine learning (including deep learning), generative neural networks, large language models, etc. Based on the experience of historians applying AI, the article proposes the following seven directions for such research: recognition of handwritten and old printed texts, their transcription; attribution and dating of texts using AI; typological classification and clustering of data from statistical sources (particularly using fuzzy logic); source criticism tasks, data completion and enrichment, and reconstruction using AI; intelligent search for relevant information, utilizing generative neural networks for this purpose; using generative networks for text processing and analysis; and the use of AI in archives, museums, and other institutions that store cultural heritage. An analysis of the discussion of similar issues organized by the leading American historical journal AHR has been conducted. These are conceptual questions regarding the interaction between humans and machines ("historian in the world of artificial neural networks"), the possibilities for historians to use machine learning technologies (particularly deep learning), various AI tools in historical research, as well as the evolution of AI in the 21st century. Practical aspects were also touched upon, such as the experience of recognizing newspaper texts from past centuries using AI. In conclusion, the article addresses the problems related to the use of generative neural networks by historians.

This study investigates the processes and means of requirements engineering (RE) of software and complex information systems (SWS). The subject of this study is the instrumental means of SWS RE based on the methods of artificial (computational) intelligence (AI). The goal is to improve the accuracy and efficiency of CCD requirements development processes using AI tools by providing better communications between business teams and technical teams and automating complex requirements collection and documentation processes. The tasks are as follows: to analyze the principles and means of integrating AI tools into requirements engineering processes, the development and verification of which is a critical stage in the development of SWS; to determine the problems of using traditional SWS RE methods and perform their comparative analysis with AI-based methods; and to develop a chatbot architecture for requirements validation and identify characteristics of RE processes that are improved through its use. The results. The results of this study demonstrate that AI tools can significantly improve the accuracy, timeliness, and efficiency of requirements development processes and project team communication. An architecture and software solution for an intelligent chatbot for requirements validation is proposed, and the benefits and limitations of its application are discussed. Directions for further development and research regarding the end-to-end implementation of AI tools in IP processes have been developed.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of the open-submission paper “Artificial Intelligence for Seismic-Image Reconstruction,” by Yogendra Narayan Pandey, SPE, and Govind Chada, Prabuddha, and Tejas Karmarkar, Oracle Cloud Infrastructure. The paper was not presented at an SPE conference and has not been peer-reviewed. Seismic imaging provides vital tools•for the exploration of potential hydrocarbon reserves and subsequent production-planning activities. The acquisition of high-resolution, regularly sampled seismic data may be hindered by physical or financial constraints, which lead to undersampled, sparse seismic data. However, if seismic data are available at a higher resolution and sampled evenly throughout the region of interest, the generated 3D models of petrophysical properties could be improved. Such improvements would show potential benefits through the successive steps of reservoir modeling and production•planning. Traditional Approaches Traditional methods used to overcome the previously mentioned data-quality issues can be divided broadly into three categories. Wave-equation-based methods. These methods use physics-based•wave-propagation equations, using•velocity models•to•reconstruct missing seismic traces. Domain-transform methods. These are data-driven methods that involve transformation of data•between different domains, such as time and frequency. Prediction-error filters. These methods use a filter that learns from the known seismic data and constructs missing seismic data. Given recent advances in the field of artificial intelligence (AI), it is worth examining whether AI methods also can be useful in the task of seismic-data•reconstruction. Generative Adversarial Networks (GANs) GANs are a recent addition to the field of solution techniques. A variant of GANs, conditional generative adversarial networks, was used to test the efficacy of GANs for seismic-data reconstruction. Fig. 1 shows a schematic of a GAN model for seismic-data reconstruction. To effectively reconstruct a seismic image, a large number of 2D seismic images are fed into the GAN during the training process. A GAN model consists of two main components: Generator. This is a deep convolutional neural network (CNN) that uses random noise as an input and generates an image by expanding the input through a series of deconvolutions. As shown in Fig.•1, the generator network is fed 2D seismic images, with a portion of these masked to replicate missing traces. The generator network tries to reconstruct these•masked portions of the 2D seismic images. Discriminator. This is also a deep CNN that is shown the images reconstructed by the generator network and the original seismic images from which some parts were masked. The discriminator’s job is to distinguish between the images reconstructed by the generator from the original seismic images. As training progresses, the generator network tries to create images that the discriminator will not be able to differentiate from the original seismic images. At the same time, the discriminator’s ability to distinguish the generated images from the original images continuously improves. As a result, the generator begins producing reconstructed seismic images, which look similar to the original ones.

The purpose of the article is to examine the impact of artificial intelligence on the development of multimedia technologies, identify the main areas of its application, and analyse the challenges and prospects arising in their integration. The research methodology is based on analysing scientific and educational sources, the practical application of artificial intelligence in multimedia technologies, a comparative analysis of the effectiveness of neural networks, and the systematisation of the data obtained regarding their impact on digital, cultural and creative industries. The scientific novelty lies in a comprehensive analysis of the impact of artificial intelligence on the development of multimedia technologies, identifying the main areas of its application in cultural and creative industries, and the systematisation of challenges and prospects associated with integrating neural networks into digital creativity. Conclusions. Artificial intelligence is changing approaches to creating, processing and consuming content, contributing to developing new forms of interaction with the digital environment. One of the key factors for the effective use of artificial intelligence in multimedia is the synergy of technologies that enables the creation of interactive and personalised media products. Generative neural networks, image, audio and video processing algorithms not only improve the quality of content, but also provide new opportunities for creativity and innovation. At the same time, the active introduction of artificial intelligence into multimedia technologies is accompanied by a number of challenges, including ethical issues regarding copyright, algorithmic bias, manipulation of information through deepfakes, and the potential impact on public opinion. These issues require careful regulation and a responsible approach to developing and applying artificial intelligence technologies. Prospects for further research in this area include the creation of more sophisticated algorithms capable of taking into account the context and emotional state of the user, which will contribute to the development of even more intuitive and personalised multimedia products. Special attention should be paid to raising ethical standards in using artificial intelligence, particularly copyright protection, combating fake content and minimising algorithm bias. Artificial intelligence's development in multimedia expands technological prospects and opens up space for interdisciplinary research that integrates technical innovations with the humanitarian and ethical aspects of their application.

In view of the increasing scarcity of resources and global efforts to reduce CO2 emissions, production management approaches are focusing on enabling a circular economy. The remanufacturing of used products to the quality standards of a new product is one key enabler. Remanufacturing offers economic and ecologic advantages by reducing the amount of resources used in production. Thus, associated manufacturing costs are reduced and the dependence on imports of critical raw materials decreases. To do so, remanufacturing requirements must be considered in the early development phases of products and production processes. In practice, companies focus on the economic perspective in the development phase, methodically supported by highly iterative product and production process development (HIP3D) approaches. However manufacturing companies neglect the inclusion of the ecological perspective in the development phases, partly due to missing methodical support. This paper presents a framework for the industrialization of remanufacturing in the HIP3D. Since the feasibility of remanufacturing is defined at the early stages of product and production process development, this paper aims at integrating remanufacturing requirements in the development phase. First, the requirements arising from remanufacturing are identified through a systematic literature review. Subsequently, it is examined to what extent HIP3D already covers these requirements. For non-fulfilled remanufacturing requirements, adaptions and extensions to the HIP3D approach are derived and described in design guidelines. This results in a framework for the industrialization of remanufacturing in the HIP3D, enabling manufacturing companies to exploit their economic and ecological potential.KeywordsRemanufacturingHighly iterative product and process developmentIndustrialization

Dear Editors, I read your editorials with interest [1,2]. Artificial intelligence has become increasingly popular in recent years. Moreover, it is seen that valuable comments made on the article focus on issues such as potential harms in the academic use of artificial intelligence, publication ethics, and the ontological position of artificial intelligence [3-6]. As with other technological developments in our life, artificial intelligence seems to be increasingly present in our lives [7]. The use of artificial intelligence in article writing is a current topic of discussion. Writing an article from scratch with artificial intelligence doesn't seem quite right. The fact that artificial intelligence doesn't have an entirely independent thought structure, and that it obtains its information only based on the web, could lead to plagiarism. Also, it will not be fully understood whether it provides correct references and whether it violates the privacy of personal data. Furthermore, we do not find it correct to accept it as a co-author. Authorship carries a responsibility that an AI tool, no matter how advanced, cannot bear. Conversely, artificial intelligence will be incapable of assuming responsibility. So, should we stay away from artificial intelligence in this case? We believe the answer to this question is no. We can use artificial intelligence while writing an article, and it can facilitate our work. Finding a title after writing the article is an important issue. It must be impressive, and some journals have a character or word limit for the title. Artificial intelligence can help us in finding a title. Artificial intelligence can also assist in creating an abstract. Each journal has different rules about the abstract section. When rejected from one journal, it's necessary to create a new abstract for another journal. Artificial intelligence can speed up this process. When the article is ready, it needs to be checked for grammar and spelling rules. Artificial intelligence will be very useful in this situation. Preparing a good cover letter is important to attract the editor's attention. Artificial intelligence will assist in writing the cover letter. Although we think that artificial intelligence cannot be a co-author and cannot be used to write an article from scratch, we believe that we can benefit from artificial intelligence while writing our article. Using artificial intelligence will save us time. It will also help us write higher quality articles. In conclusion, artificial intelligence is now a part of our lives. Instead of avoiding it, we believe that we need to take advantage of artificial intelligence in a way that will facilitate our work. Sincerely yours,

The article is devoted to the history of the development of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), their current and probable future achievements, and the problems (which have already arisen, but will become even more acute in the future) associated with the development of these technologies and their active introduction in society. The close connection between the development of AI and cognitive science, the penetration of ICT and AI into various fields, in particular the field of health care, is shown. A significant part of the article is devoted to the analysis of the concept of ‘artificial intelligence’, including the definition of generative AI. We analyze recent achievements in the field of Artificial Intelligence, describe the basic models, in particular the Large Linguistic Models (LLM), and forecast the development of AI and the dangers that await us in the coming decades. We identify the forces behind the aspiration to create artificial intelligence, which is increasingly approaching the capabilities of the so-called general/universal AI, and also suggest desirable measures to limit and channel the development of artificial intelligence. The authors emphasize that the threats and dangers of the development of ICT and AI are particularly aggravated by the monopolization of their development by the state, intelligence services, large corporations and those often referred to as globalists. The article forecasts the development of computers, ICT and AI in the coming decades, and also shows the changes in society that will be associated with them. The study consists of two articles. The first, presented below, provides a brief historical overview and characterizes the current situation in the field of ICT and AI, it also analyzes the concepts of artificial intelligence, including generative AI, changes in the understanding of AI related to the emergence of the so-called large language models and related new types of AI programs (ChatGPT). The article discusses the serious problems and dangers associated with the rapid and uncontrolled development of artificial intelligence. The second article, to be published in the next issue of the journal, describes and comments on current assessments of breakthroughs in the field of AI, analyzes various forecasts, and the authors give their own assessments and forecasts of future developments. Particular attention is given to the problems and dangers associated with the rapid and uncontrolled development of AI, the fact that achievements in the field of AI are becoming a powerful means of controlling the population, imposing ideology and choice, influencing the results of elections, and a weapon for undermining security and geopolitical struggle.

The past few years have seen unprecedented growth in technology development in the field of advanced artificial intelligence. One of the latest major advances was the release of a platform called ChatGPT in November 2022.ChatGPT is changing expectations and practices in education; the teaching and academic community is concerned about the rapid pace of implementation of the chatbot in the educational process and the possible negative consequences of this implementation.The relevance of the research is due to the development of artificial intelligence technologies, the rapid growth of the popularity of ChatGPT and the introduction of new technologies in the field of higher education.The purpose of the research is to study the practical aspects of using generative neural networks in higher education.The objectives of the research are to consider real cases of using artificial intelligence technologies in higher education and analyze our own experience of using ChatGPT in educational practice.The methodological basis of the research consists of the following methods: analysis of Internet resources and literary sources, method of studying cases for research and generalization of teaching experience, synthesis.The research results include the possibilities of using artificial intelligence technologies (using the example of the generative language GPT model) in the educational process of higher educational institutions, the advantages and disadvantages of integrating generative neural networks into the educational process of a university.Key conclusions: Generative neural networks are a controversial technology, raising concerns about ethics and academic integrity in higher education, but are also discussed as a powerful tool for teaching and research. In higher education ChatGPT can be used to create learning materials, engage learners in subject-matter discussions, provide personalized feedback, develop assessment rubrics, and support interactive and collaborative learning, thereby enhancing learning effectiveness. In the process of introducing generative neural networks into the educational process, it is necessary to take into account the limitations of their use: lack of creativity, bias and inaccuracy, dependence on technology. Teachers must adapt their teaching methods to make the most of these powerful tools.

The problem of face recognition is the important task in the security field, closed-circuit television (CCTV), artificial intelligence, and etc. One of the most effective approaches for pattern recognition is the use of artificial neural networks. In this presentation, an algorithm using generative adversarial networks is developed for face recognition. The proposed method consists in the interaction of two neural networks. The first neural network (generative network) generates face patterns, and the second network (discriminative network) rejects false face patterns. Neural network of feed forward type (single-layer or multilayer perceptron) is used as generative network. The convolutional neural network is used as discriminative network for the purpose of pattern selection. A big database of normalized to brightness changes and standardized in scale artificial images is created for the training of neural networks. New facial images are synthesized from existing ones. Results obtained with the proposed algorithm using generative adversarial networks are presented and compared with common algorithms in terms of recognition and classification efficiency and speed of processing.

Introduction to the problem. The development of artificial intelligence technologies has significantly affected the pedagogical discourse due to the growing availability and popularity of generative neural networks among students. Technological progress has actualized the problems of a moral and ethical nature. In the process of forming the professional subjectivity of the future teacher as an educator, it is necessary to prepare students for pedagogical interaction with gifted students who are passionate about artificial intelligence technologies. The purpose of the article is to identify and characterize the main directions of changes in the content of training future teachers for educational activities in the context of the development of artificial intelligence technologies. The study was conducted on the basis of data on the mentoring activities of teachers in the course of preparing schoolchildren for participation in the All-Russian Olympiad in Artificial Intelligence in 2021–2022. The study is based on the methodological basis of the systems approach. 100 schools from 46 regions of the Russian Federation took part in the study. The researchers used a questionnaire method from school administrators, computer science teachers, schoolchildren and their parents to collect data. Research results. It is substantiated that the current level of development of artificial intelligence technologies has an impact on the content of general professional pedagogical competencies and on the targets for the formation of the professional position of a teacher as an educator. To perform professional labor functions, a future teacher of any subject needs to have a basic understanding of the complex of technological solutions that are united by the concept of “artificial intelligence”, about the key tasks in which artificial intelligence technologies are used today, about achievements in the field of artificial intelligence. To carry out educational activities, the future teacher needs a formed personal and professional position, including a value attitude to the ethical boundaries of the use of artificial intelligence. Conclusion. The main directions of changes in the content of preparing future teachers for educational activities in the context of the development of artificial intelligence technologies are associated, firstly, with the expansion of ideas about the areas of application of artificial intelligence, and secondly, with the mastery of technological developments for their application in professional pedagogical activity, in- thirdly, with a specification of the role of the teacher as a mentor in the personal development of gifted students.

With the rapid development of artificial intelligence technology, the application of AI in the judicial field is gradually expanding, especially in civil trials, where artificial intelligence auxiliary positioning and generative artificial intelligence technology show great potential. Through intelligent case analysis, legal text retrieval, case matching, and document generation, artificial intelligence can effectively improve judicial efficiency, ensure the quality of judgments, reduce the workload of judges, and promote judicial fairness. However, the application of artificial intelligence in the judicial field still faces challenges such as technical adaptation, data privacy, and ethical issues. This paper aims to explore how auxiliary positioning and generative artificial intelligence can empower civil trials, and reveal its potential and development prospects by analyzing the current application status, technical principles, practical effects, and challenges of AI in civil trials. Studies have shown that the introduction of artificial intelligence can improve judicial efficiency and consistency of judgments, but it also poses new challenges to the current judicial system and legal framework. In the future, with the continuous advancement of technology and the improvement of policy frameworks, artificial intelligence will play an increasingly important role in civil trials and contribute to the intelligent transformation of the judicial field.

Chapter 6 - A review of techniques to detect the GAN-generated fake images

This paper outlines some preliminary research toward the viability of utilizing unsupervised generative deep learning networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to create image assets for game development. This work explores existing GAN research and the viability of using generative networks to generate both textures and human face assets within a game engine. This research and its preliminary results examines the viability and advantages/disadvantages of utilizing generative networks directly within game engines. It also examines the potential trade-offs of a generative approach versus more traditional approaches for image assets in video game development.

The increasing technological progress and global competition are leading to a changing market environment. To remain competitive, companies must meet customer requirements as quickly and individually as possible. Accordingly, products must be developed faster, more cost-effectively, and in a customer-centric way. However, this cannot be achieved by plan-driven development approaches since sequential development of product and production process leads to long lead times and increases the effort to integrate late changes into the development. Agile approaches provide initial solutions for customer-oriented product development but neglect the integration of production process development. An approach that considers both is the highly iterative and integrated product and production process development (HIP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> D). It presents a possibility to enable shorter development cycles, greater customer involvement, and a reduction of time-to-market. However, this approach requires a change in organizational structure and lacks a management framework. Accordingly, characteristics of the HIP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> D are derived, central positions in the company are identified, and an organizational structure is presented. Additionally, an overview of the tools and methods in the framework and relevant KPIs are presented.