Exploring generative artificial intelligence: a comprehensive guide

The rapid advancement of artificial intelligence (AI) has introduced a new paradigm in informatics known as Generative AI. One of the key driving forces behind this innovation is the application of deep learning algorithms, which can emulate human cognitive patterns to automatically generate text, images, audio, and video. This study aims to analyze how deep learning algorithms particularly Generative Adversarial Networks (GANs) and Transformer-based Models (such as GPT and Diffusion Models) are utilized in developing generative AI systems for automated content creation. The research employs a literature review of recent studies, comparative analysis of generative models, and performance evaluation based on quality, creativity, and computational efficiency. The findings reveal that Transformer-based models exhibit greater adaptability in understanding semantic context and producing more realistic content compared to traditional GAN models. However, challenges such as overfitting, data bias, and high computational resource demands remain major obstacles to large-scale implementation. This study concludes that optimizing deep learning algorithms supported by ethical considerations and careful data management will be crucial to the successful development of generative AI that is both effective and responsible within the modern informatics ecosystem.

Generative AI has revolutionized computer vision by enabling machines to synthesize and enhance visual data. Advances in deep learning, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, have led to high-quality image generation with applications in medical imaging, data augmentation, and autonomous systems. Despite its potential, challenges like ethical concerns, dataset biases, and computational costs remain critical for future research and implementation. This paper explores the evolution of generative AI, its methodologies, applications, and the ethical and computational challenges it presents.

The combination of generative AI and data engineering has the potential to create a new breed of data that can be effectively applied at scale through effective use of high-quality augmentation as well as AI-driven pipelines. This post examines cutting-edge artificial intelligence (AI) capabilities, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and diffusion models integrated within AWS cloud tools, data warehousing with Snowflake, and Apache Airflow to act as the orchestration layer. It also integrates GitHub CI/CD through Push to deploy the model and utilize MLOps workflow. Solution Overview: The Proposed Framework Enriches Real-Time Pipelines that Automate Data Quality and Fairness with Outputs for Both Enterprise AI and Cloud Pipeline Paths for Users in Healthcare, Financial Services, Retail, Manufacturing, Construction Materials, and Gaming.

The general objective of the research is to determine the advances related to Generative Artificial Intelligence. Methodology, in this research, 47 documents have been selected, carried out in the period 2014 - 2023; including: scientific articles, review articles and information from websites of recognized organizations. Results, Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. Conclusions, the general objective of the research is to determine the advances related to Generative Artificial Intelligence. Artificial intelligence has evolved from predictive to generative. Key Techniques: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Autoregressive Models. Countries are establishing standards for the ethical use of AI, while respecting human rights. Currently, AI has many applications in human activity, but the ethical use of AI is necessary. Various countries are establishing regulations in this regard. Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. The specific objectives of the research are to identify the applications and the software of Generative Artificial Intelligence. Applications: Generating realistic images, creating natural language text, composing music. Generative artificial intelligence (AI) tools, such as Bard, ChatGPT, and GitHub CoPilot.

Generative Artificial Intelligence (AI) has shown significant promise in healthcare, particularly in medical imaging, by improving data augmentation, diagnostic accuracy, and model generalization. However, existing models such as Generative Adversarial Networks (GANs) and Diffusion Models face challenges in balancing image realism, structural fidelity, and computational efficiency. GANs often suffer from mode collapse and limited diversity, while diffusion models, though high in fidelity, are computationally intensive and slow to sample. To address these limitations, we propose a Hybrid GAN–Diffusion approach that synergistically combines the strengths of both architectures. Our pipeline first pretrains a StyleGAN2-ADA model to capture coarse-to-fine radiographic structure efficiently, then refines the generated samples using a Denoising Diffusion Probabilistic Model (DDPM) to enhance anatomical plausibility and diversity. This sequential design fuses the sharpness and speed of GANs with the coverage and robustness of diffusion models, yielding synthetic images of superior quality and clinical utility. We evaluate the framework on the Kaggle Chest X-Ray Pneumonia dataset for training and perform external validation on NIH ChestXray14 to assess cross-dataset generalization. Experimental results demonstrate that the hybrid model achieves an ROC-AUC of 0.984 in-distribution and 0.892 on the external dataset, outperforming classical augmentation, GAN-only, and diffusion-only baselines. The synthetic data quality is further validated by a simulated Fréchet Inception Distance (FID) of 16.2, indicating high realism and diversity. The proposed method establishes a new state-of-the-art in medical image augmentation, demonstrating that hybrid generative pipelines can effectively overcome data scarcity while improving model robustness and generalization.

Recent advancements in generative artificial intelligence (AI) have introduced novel opportunities for the application of AI within the financial sector. This chapter examines the various ways in which generative AI is contributing to the rapidly evolving landscape of finance. While traditional machine learning techniques have long been applied to financial tasks such as customer service, text summarization, risk prediction, algorithmic trading, and document processing, generative AI offers enhanced capabilities aimed at improving operational efficiency, elevating customer experience, and enabling responsible growth. Applications of generative AI in finance now extend to areas including decision-making, risk assessment, portfolio optimization, personalized financial advising, fraud detection, and predictive analytics. The field encompasses a diverse set of models, including generative adversarial networks (GANs), variational autoencoders (VAEs), temporal point processes (TPPs), transformers, and large language models (LLMs). This chapter explores how these models are applied in financial contexts, highlighting both theoretical foundations and practical implementations. A dedicated case study is presented, summarizing recent literature on the use of LLMs in financial tasks, including approaches such as zero-shot and few-shot learning with pretrained models, domain-specific fine-tuning, and training models from the ground up. Finally, the chapter discusses key opportunities and challenges associated with generative AI in FinTech, including the balance between innovation and responsibility, and the need for enhanced interpretability, robustness, and ethical compliance in financial applications, hence aiding towards goals of Industry 5.0.

Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1–158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

<sec><title>Objective</title> As urban design faces increasing demands for contextual responsiveness, iterative optimization, and data-informed reasoning, integrating artificial intelligence into the design process has gained renewed relevance. Among emerging technologies, generative artificial intelligence (GAI) shows strong potential for automating content creation and simulating spatial configurations. This research provides a comprehensive review of recent developments in the application of GAI to urban design. The research identifies representative technical pathways, their respective intervention stages, and the functional mechanisms by which generative models are reshaping the design workflow. This research presents a structured, theory-informed synthesis of how different generative models contribute to tasks such as intention modeling, spatial reasoning, and performance-driven design. Building on design thinking and a descriptive lens informed by the technology acceptance model (TAM), the research examines how model type, data modality, and task characteristics affect GAI’s functional role, usability, and acceptance. Particular attention is given to mapping deployment forms, from isolated tools to coordinated multi-model workflows, and to characterizing cross-cutting challenges of controllability, transparency, and contextual adaptability in urban design settings. </sec><sec><title>Methods</title> Following the PRISMA protocol, the research conducts a multi-stage literature review combining automated search and expert screening. A total of 125 peer-reviewed articles and high-impact preprints are selected from Web of Science, CNKI, arXiv, and selected industry sources, covering the period from 2014 to July 2025. Search terms such as “generative AI”, “AIGC”, “GAN”, “diffusion model”, “variational autoencoder”, “autoregressive model”, “large language model”, and urban-related keywords are used in various combinations. Based on the collected literature, four types of generative models are summarized as image-driven, language-driven, structure-driven, and feedback-optimized models, according to their application characteristics in urban design tasks. These types are aligned with four stages of the design process: preliminary analysis, scheme generation, evaluation and decision-making, and outcome expression. On this basis, a two-dimensional framework to examine how different GAI pathways intervene across tasks is formed. To refine the mapping, each design stage is further broken down into three representative sub-tasks. Preliminary analysis includes public demand analysis, urban data enhancement, case/task framing, and spatial element recognition. Scheme generation covers design intention modeling, spatial layout generation, and 3D form construction. The evaluation and decision-making stage includes multi-objective optimization, scheme evaluation, and scenario prediction. The final expression stage involves textual documentation, 2D representation, and visual rendering. A quantitative analysis is also conducted to show the distribution of model types over design stages, identify common combinations, and trace the evolution of research focus over time. TAM informs a descriptive synthesis of perceived usefulness (PU) and perceived ease of use (PEU) across model types to illuminate adoption patterns. </sec><sec><title>Results</title> The findings reveal that GAI models are increasingly integrated into urban design workflows but exhibit uneven adoption across task types and modalities. Image-driven models dominate in both early-stage analysis and final visual representation due to their high interpretability, usability, and compatibility with existing design practices. Language-driven models are commonly used in public demand analysis, participatory planning, and scenario scripting, enabled by the rise of large language models (LLMs) such as ChatGPT and DeepSeek. Structure-driven models, though less prevalent, show promise in generating street networks, land-parcel layouts, and spatial typologies using graph-based logic. Feedback-optimized models, which rely on reinforcement learning, evolutionary algorithms, and performance simulation are the least adopted, but demonstrate strong potential in multi-objective optimization and iterative decision-making. Recent research indicates an increasing use of multi-model workflows, such as text-to-image pipelines integrated with urban simulation or feedback loops. While GAI applications increasingly support design iteration, their adoption is heavily influenced by the controllability, explainability, and contextual adaptability of models. PU and PEU vary significantly by model type, with image-driven models rated highest and structure-driven and feedback-optimized models facing usability challenges due to complexity and low transparency. </sec><sec><title>Conclusion</title> Although GAI has demonstrated broad applicability across the urban design process, current implementations are largely procedural and auxiliary in nature. Most models recombine existing inputs rather than construct original logic, and few possess autonomous reasoning or normative awareness. This limits their role to content augmentation rather than conceptual guidance in design development. Moreover, issues such as opaque decision logic, lack of domain-specific knowledge embedding, and poor adaptability to local planning norms hinder practical adoption. Addressing these challenges requires multi-level efforts: 1) Construct structured, regionally grounded urban design datasets; 2) improve model interpretability, controllability, and responsiveness to professional input; and 3) develop modular, multi-model systems that support seamless interaction across design stages. Human – AI collaboration mechanisms — especially those based on iterative prompts and semantic feedback-must be enhanced to enable AI not just as a tool, but as an active design partner. This review offers a comprehensive reference for scholars and practitioners seeking to understand how GAI is reshaping the logic, structure, and agency of urban design. </sec>

In recent years, the manufacturing industry has undergone transformative changes spurred by the rapid evolution of artificial intelligence (AI), particularly Generative AI (GenAI). As a subset of AI capable of creating new data from learned patterns, GenAI is poised to reshape manufacturing processes by enhancing productivity, product customization, quality assurance, and operational efficiency. This review synthesizes key findings from scholarly articles published in the first half of 2024, with a primary focus on three GenAI models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-Based Architectures. The paper critically analyzes the roles and capabilities of GenAI technologies in improving predictive maintenance, supply chain optimization, and sustainable production. It also sheds light on the prevailing challenges and potential future advancements of GenAI integration in industrial environments.

Among the broad area of artificial intelligence (AI), generative AI algorithms have emerged as a revolutionary technology able to produce highly realistic ‘synthetic’ data, akin to standard simulation but with fewer contraints. The main focus of generative AI has been on phenotypes, but here we argue it can serve as well for generating synthetic environments and genotypes. This data-driven technology may be able to overcome some of the limitations that standard simulations have, such as strong assumptions on the underlying genotype to phenotype map. We discuss key features of popular generative models including autoregressive models, generative adversarial networks, variational autoencoders, diffusion and flow-based models. Several of these methods utilize a latent space, often of lower dimensionality than the raw data, that can help making the models interpretable and can be a link between simulation and generative algorithms. Augmenting data as realistically as possible with genAI can improve inference and predictive performance of genomic prediction models, but symbolic simulation will continue to play a fundamental role in predictive breeding. A hybrid tool that implements both approaches can be extremely powerful to evaluate predictive breeding strategies in silico. One promising direction is to simulate novel genotypes using conventional methods, then apply generative models to produce realistic phenotypes conditional on genotype and environment.

The semiconductor industry plays a vital role in driving technological advancements, and the incorporation of AI (Artificial Intelligence) can greatly enhance its efficiency and productivity. Through optimizing material usage and reducing defects, AI can significantly reduce costs and enhance production efficiency and product quality. However, despite the increasing interest in AI applications in the semiconductor industry, comprehensive reviews are lacking to systematically analyze existing research and identify the challenges and opportunities in this field. This review aims to bridge this gap by providing a thorough overview of AI-driven techniques in optimizing semiconductor manufacturing and offering valuable insights for future research directions. The integration of Artificial Intelligence (AI) into chip design marks a transformative phase for the semiconductor industry. Traditional design methodologies, often labor-intensive and time-consuming, are increasingly constrained by human expertise and iterative processes. Generative AI, utilizing advanced machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offers innovative approaches to automate and optimize various stages of chip design. This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation. Through case studies, we demonstrate significant improvements in design efficiency, performance optimization, and reduced time-to-market. Additionally, we address challenges such as data availability, model interpretability, and the integration of AI-generated designs into existing verification workflows. The findings highlight the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.

This article presents a comprehensive survey on the integration of Generative Artificial Intelligence (AI) technologies in radar applications, with a focus on enhancing radar data processing and system capabilities. Generative AI techniques, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are explored for their potential to address persistent challenges in radar technology such as noise management, data augmentation, and target classification. The study investigates how GANs can generate synthetic radar datasets, aiding in model training when actual data is scarce, and how VAEs contribute to signal processing by denoising and reconstructing accurate radar signals. The analysis includes case studies on clutter suppression, radar data augmentation, beam blockage correction, and data fusion, highlighting the transformative impact of Generative AI on radar systems. This paper aims to provide insights into the current advancements and future directions of Generative AI applications in radar, suggesting that these technologies hold significant promise for improving the accuracy and efficiency of radar systems in diverse and dynamic environments.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

Malware detection plays a significant role in the realm of cybersecurity, yet traditional methods frequently struggle to identify novel and polymorphic threats. It investigates the growing role of Generative Artificial Intelligence (GenAI), including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, in enhancing malware detection. We systematically reviewed and analyzed over 25 peer-reviewed studies and technical reports published between 2017 and 2024, including recent surveys and case studies on GenAI-based cybersecurity solutions. Four benchmark datasets-Malimg, UNSW-NB15, CIC-IoT23, and EMBER-were evaluated across the literature for their suitability in GenAI-based malware detection. The techniques are compared based on performance metrics such as classification accuracy, anomaly detection precision, F1-score, adversarial robustness, and computational efficiency. The paper also identifies key limitations, including mode collapse, low interpretability, and vulnerability to evasion and poisoning attacks. Finally, we identify various possibilities for future research, including real-time AI defenses, hybrid GenAI-discriminative approaches, and explainable AI integration. This study serves as a foundational reference for advancing GenAI-powered malware detection frameworks in cybersecurity. To provide a more reliable and precise malware detection pipeline, the suggested hybrid framework combines Transformer-based sequence modeling, VAE-driven latent feature encoding, and GAN-based data augmentation. Furthermore, the proposed hybrid model combining GAN-based augmentation, VAE-driven latent representation learning, and Transformerbased sequence modeling demonstrates improved robustness and detection accuracy, offering a unified approach to address malware diversity, imbalance, and evolving threat behaviors. The proposed hybrid model delivers the highest accuracy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(96.4 \%)$</tex>.

Purpose This conceptual paper examines what underlies decision-making in generative change processes to explore how generative artificial intelligence (GAI) might shape the future of organisational change. Design/methodology/approach This investigation draws on Snowden and Stanbridge's (2004) social complexity concept, Archer's (1995) morphogenetic/morphostatic explanatory methodology and Mackenzie and Bititci's (2023) social systems-based model for organisational change to explain how structure, culture and agency influence generative change processes in complex adaptive social systems. Findings Both human-based decision-making and machine-based decision-making have roles to play in generative change. This paper proposes that human reflexivity mediates ideas, whereas the material aspects of artificial intelligence (AI) and GAI mediate tasks. The former shapes the change interventions that take place and the latter contributes to their more effective execution. Practical implications In generative change processes, AI and GAI technologies should be focused on tasks that support human-based decision-making. Originality/value This paper explores decision-making in organisational change from a social complexity perspective and identifies the complementary roles of human reflexivity and AI and GAI materiality in delivering emergent outcomes.