Generative AI for predictive breeding: hopes and caveats

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.

<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>

Generative artificial intelligence (GAI), a specialized branch of artificial intelligence, has developed as a dynamic discipline that drives innovation and creativity across several domains. It is concerned with creating models that can autonomously produce novel text, images, videos, music, 3D, code, and more. GAI is distinguished by its capacity to acquire knowledge from extensive datasets, identify recurring patterns, capture distributions, and produce novel content demonstrating similar features. This review presents a comprehensive historical overview of the development and progression of GAI techniques over the years. Various essential methodologies employed in developing GAI are also discussed, including Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), transformers, and diffusion models. Moreover, a detailed overview of the technologies used in GAI is provided for generating images, videos, music, code, and text, such as ChatGPT, DALL-E, Midjourney, Claude, Bard, GitHub Copilot, and others. The research subsequently introduces the different datasets used to train the GAI models and the evaluation metrics for evaluating their performances. Ultimately, the research investigates the diverse applications of GAI across various domains, challenges, and ethical implications.

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.

Single-cell RNA-seq (scRNAseq) analyses performed at the cellular level aim to understand the cellular landscape of tissue sections, offer insights into rare cell-types, and identify marker genes for annotating distinct cell types. ScRNAseq analyses are widely applied to cancer research to understand tumor heterogeneity, disease progression, and resistance to therapy. Single-cell data processing is a challenging task due to its high-dimensionality, sparsity, and having imbalanced class(cell-type) distributions. An accurate cell-type identification is highly dependent on preprocessing and quality control steps. To address these issues, generative models have been widely used in recent years. Techniques frequently used include Variational Autoencoders (VAE), Generative Adversarial Networks (GANs), Gaussian-based methods, and, more recently, Flow-based (FB) generative models. We developed a Masked Affine Autoregressive transform-embedded FB (MAF-FB) model. Then, to improve MAF-FB further, we incorporated a mixture of experts (MOE) of attention mechanisms on top of it, resulting in our proposed MOE-FB model. We conducted a comparative analysis of fundamental generative models, aiming to serve as a preliminary guidance for developing novel automated scRNAseq data analysis systems. We performed a large-scale analysis by combiningfour datasets derived from pancreatic tissue sections and for further generalizability assessments, we employed Peripheral Blood Mononuclear Cells (PBMC68K and PBMC3K) and Human Cell Atlas Bone Marrow (HCA-BM10K) datasets. We utilized VAE, GAN, Gaussian Copula, and Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA), and compared them against our two novel FB models, MAF-FB and MOE-FB for ScRnaseq synthesis. To evaluate the performances of generative models, we used various discrepancy metrics and performed automated cell-type classification tasks. We also identified differentially expressed genes for each cell type, and inferred cell-cell interactions based on ligand-receptor bindings across distinct cell-type pairs. Among the generative models, FB models, especially MOE-FB, consistently outperformed others across all experimental setups in both discrepancy metrics with comparison to the baseline test set and cell-type classification tasks (with an F1-score of 0.90 precision of 0.89 and recall of 0.92 for the integrated pancreatic datasets). MOE-FB produced biologically more relevant synthetic data, and ligand–receptor–based cell–cell interactions inferred from the synthetic cells closely resemble the original data, achieving an RMSE of 0.65 against the corresponding pancreatic test set. These findings highlight the potential and promising use of FB models, especially MOE-FB, in scRNAseq analyses.

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.

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.

Today, with the involvement of artificial intelligence (AI), where sensitive information is being collected massively to analyse data for various purposes, privacy-preserving data analysis has gained much importance in the data-driven world. Traditionally, using normal data analysis techniques, privacy-preserving methods often faced several security challenges to ensure the privacy of individuals and organizations where the shared data resources have been used for collaboration or decision-making. Hence, generative artificial intelligence has offered numerous solutions that can ensure privacy to such security concerns by enabling privacy-preserving analysis while getting fruitful knowledge from data. Therefore, in this chapter, the authors aim to provide an overview of privacy-preserving data analysis techniques using AI-based approaches such as differential privacy, federated learning (FL), generative adversarial network (GAN), and other variational auto-encoders (VAEs). While providing individuals protection in privacy, they have discussed how generative AI can be used to preserve statistical properties of plain data using synthetic data analysis. Moreover, some real-world applications have also been part of the discussion to illustrate the effectiveness of such techniques in various domains. Finally, they have also provided some future challenges, limitations, and ethical considerations for implementing these techniques effectively and have provided future directions in the rapidly emerging domain.

Sudan's post-conflict transition presents a critical opportunity to reimagine state institutions through inclusive, resilient, and ethically grounded reform.This paper explores the potential for generative artificial intelligence (AI) specifically Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models to support this complex process of institutional reconstruction.Moving beyond technocratic solutions, the study examines the theoretical foundations of these models and maps their operational mechanisms onto key domains of reform, including constitutional design, budget simulation, civic engagement, and transitional justice.It argues that generative AI, when locally adapted and ethically governed, can serve as a catalyst for reimagining governance, amplifying civic agency, and embedding pluralistic narratives into the fabric of institutional renewal.The paper concludes with ethical reflections and comparative insights from Rwanda, Colombia, and Tunisia, outlining a framework for contextually sensitive implementation.

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 Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory. A review of recent literature was conducted to evaluate the current applications, identify potential benefits, and outline limitations of integrating generative AI in orthopedic education. Key findings indicate that generative AI holds substantial promise in enhancing orthopedic training through its various applications such as providing real-time explanations, adaptive learning materials tailored to individual student’s specific needs, and immersive virtual simulations. However, despite its potential, the integration of generative AI into orthopedic education faces significant issues such as accuracy, bias, inconsistent outputs, ethical and regulatory concerns and the critical need for human oversight. Although generative AI models such as ChatGPT and others have shown impressive capabilities, their current performance on orthopedic exams remains suboptimal, highlighting the need for further development to match the complexity of clinical reasoning and knowledge application. Future research should focus on addressing these challenges through ongoing research, optimizing generative AI models for medical content, exploring best practices for ethical AI usage, curriculum integration and evaluating the long-term impact of these technologies on learning outcomes. By expanding AI’s knowledge base, refining its ability to interpret clinical images, and ensuring reliable, unbiased outputs, generative AI holds the potential to revolutionize orthopedic education. This work aims to provides a framework for incorporating generative AI into orthopedic curricula to create a more effective, engaging, and adaptive learning environment for future orthopedic practitioners.

Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of generative modeling is to implement a system that produces novel compounds that are expected to have a desired set of chemical features, effectively sidestepping issues found in the forward design process. In this contribution, we overview and critically analyze popular generative algorithms like generative adversarial networks, variational autoencoders, flow, and diffusion models. We highlight key differences between each of the models, provide insights into recent success stories, and discuss outstanding challenges for realizing generative modeling discovered solutions in chemical applications.

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 recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate singletype of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy.