Generative AI and causal-spatial modeling for understanding nighttime pedestrian risk in urban systems

In the architectural exterior design domain, design intent is usually expressed by textual design intent [e.g., client needs, architectural language (AL)] and non-verbal design intent (e.g., sketch). However, existing generative AI-based methods for automated architectural exterior conceptual design can only use the general image description as the prompt. Thus, despite its potential, existing generative image AI cannot produce appropriate design alternatives that meet various design requirements. Enabling automated architectural exterior conceptual design requires solving two problems: teaching the AI model to understand textual design intent and allowing generative AI to combine textual design intent with non-verbal design intent. The study aims to propose an automated architectural exterior conceptual design approach by incorporating domain-specific prompting strategies and sketch-to-image synthesis into fine-tuned generative image AI models. In the proposed approach, textual design intent annotations (including client needs and AL) are added to architectural images and general image description annotations. Web crawler and ChatGPT automatically extract design intent-related annotations from online sources for famous architectural works that are used as training images. The constructed dataset is then used to fine-tune a generative AI model [i.e., Stable Diffusion (SD)] via the Lora algorithm, teaching the AI model to understand textual design intent. Also, ControlNet is used to control the generation process of the SD model to enable the generative AI to reflect the design intent expressed by the sketches. The proposed approach is validated by comparing generated images from our approach with those from two existing models. The results show that the proposed method can successfully generate architectural exterior conceptual design images that fulfil the requirements based on the architectural design intent. The proposed approach is expected to streamline and facilitate time-consuming and demanding iterative processes during a conceptual design phase.

In recent years, generative AI has attracted significant public attention, and its use has been rapidly expanding across a wide range of domains. From creative tasks such as text summarization, idea generation, and source code generation, to the streamlining of medical support tasks like diagnostic report generation and summarization, AI is now deeply involved in many areas. Today’s breadth of AI applications is clearly distinct from what was seen before generative AI gained widespread recognition. Representative generative AI services include DALL·E 3 (OpenAI, California, USA) and Stable Diffusion (Stability AI, London, England, UK) for image generation, ChatGPT (OpenAI, California, USA), and Gemini (Google, California, USA) for text generation. The rise of generative AI has been influenced by advances in deep learning models and the scaling up of data, models, and computational resources based on the Scaling Laws. Moreover, the emergence of foundation models, which are trained on large-scale datasets and possess general-purpose knowledge applicable to various downstream tasks, is creating a new paradigm in AI development. These shifts brought about by generative AI and foundation models also profoundly impact medical image processing, fundamentally changing the framework for AI development in healthcare. This paper provides an overview of diffusion models used in image generation AI and large language models (LLMs) used in text generation AI, and introduces their applications in medical support. This paper also discusses foundation models, which are gaining attention alongside generative AI, including their construction methods and applications in the medical field. Finally, the paper explores how to develop foundation models and high-performance AI for medical support by fully utilizing national data and computational resources.

This research paper explores the transformative potential of generative AI in the context of document processing within large financial organizations, with a particular focus on fraud detection. As financial institutions increasingly rely on vast amounts of documentation for operations ranging from customer onboarding to compliance, the inefficiencies and limitations of traditional manual processing methods become glaringly apparent. These legacy systems are not only time-consuming and prone to human error but also struggle with scalability, a critical requirement in today’s fast-paced financial environment. Moreover, manual systems and traditional Optical Character Recognition (OCR) engines often lack the necessary accuracy and contextual understanding to reliably process complex financial documents and detect fraudulent activities. While OCR technology has automated certain aspects of document processing, its inherent limitations in accuracy, particularly in dealing with degraded documents or complex layouts, and its inability to interpret context, significantly impede its effectiveness in high-stakes financial applications. Furthermore, OCR’s limited capability in detecting subtle indicators of fraud leaves financial organizations vulnerable to increasingly sophisticated fraudulent schemes. Generative AI emerges as a revolutionary solution to these challenges by enhancing the accuracy, scalability, and security of document processing systems. Unlike traditional OCR, generative AI models are designed to understand and interpret the context of documents, thereby significantly improving the accuracy of text recognition, even in complex scenarios. These AI models, trained on vast datasets, are capable of processing large volumes of documents in parallel, making them ideally suited for the high-speed, high-volume environments characteristic of financial institutions. Additionally, generative AI incorporates advanced algorithms that enhance fraud detection capabilities by analyzing patterns, detecting anomalies, and cross-referencing data across multiple documents. This approach not only improves the detection of fraudulent activities but also reduces the likelihood of false positives, thereby enhancing the overall reliability of the system. The paper further delves into the practical applications of generative AI in various critical areas within financial organizations. Key applications include Know Your Customer (KYC) compliance, where AI streamlines the processing and verification of customer documents, thereby ensuring both compliance with regulatory requirements and the authenticity of the information provided. In loan processing, generative AI accelerates the analysis of loan applications, providing real-time risk assessments that enable faster decision-making. Additionally, the technology is applied in invoice and payment processing, where it automates and verifies transactions, reducing errors and ensuring the timely execution of financial operations. In the realm of contract analysis, generative AI facilitates the extraction and interpretation of key terms and clauses, enabling more effective contract negotiation and management. Beyond its practical applications, the paper also addresses the continuous learning capabilities of generative AI models, which allow them to evolve and adapt to new data and document types over time. This feature is particularly crucial in the financial sector, where the types of documents and the nature of fraudulent activities are continually changing. The continuous learning aspect of generative AI ensures that the systems remain up-to-date and effective, even as new challenges and document types emerge. The research also highlights the comparative analysis between traditional OCR-based systems and AI-powered systems, demonstrating the superior performance, efficiency, and scalability of the latter. Moreover, the paper discusses the challenges associated with the implementation of generative AI in financial document processing. These include technical challenges such as the integration of AI systems with existing IT infrastructure, as well as regulatory and compliance issues that arise when deploying AI technologies in the highly regulated financial sector. Despite these challenges, the paper argues that the long-term benefits of adopting generative AI, including improved accuracy, enhanced fraud detection, and greater operational efficiency, far outweigh the initial hurdles. The research also considers the future of generative AI in financial document processing, suggesting that as the technology continues to advance, its applications and benefits will expand even further. Future research opportunities are identified, particularly in the areas of improving the efficiency and scalability of AI models, enhancing their ability to handle increasingly complex document types, and developing more sophisticated fraud detection algorithms. The paper concludes with a discussion on the potential long-term impact of generative AI on the financial industry, arguing that it will play a crucial role in shaping the future of financial operations by providing more accurate, scalable, and secure document processing solutions. This paper makes a significant contribution to the existing body of knowledge on the application of AI in financial services, particularly in the area of document processing and fraud detection. By providing a detailed analysis of the challenges faced by financial organizations and demonstrating how generative AI can address these challenges, the research offers valuable insights for both academic researchers and practitioners in the field. The findings presented in this paper have important implications for the future of document processing in financial organizations, suggesting that the adoption of generative AI will be essential for maintaining operational efficiency, accuracy, and security in an increasingly complex and fast-paced financial environment. In summary, this research not only highlights the transformative potential of generative AI in financial document processing but also provides a roadmap for its successful implementation in large financial organizations, with a particular emphasis on enhancing fraud detection capabilities.

In a few years, most investment firms will deploy Generative AI (GenAI) and large language models (LLMs) for reduced-cost stock trading decisions. If GenAI-run investment decisions from most firms are heavily coordinated, they could all give a “sell” signal simultaneously, triggering market crashes. Likewise, simultaneous “buy” signals from GenAI-run investment decisions could cause market bubbles with algorithmically inflated prices. In this way, coordinated actions from LLMs introduce systemic risk into the global financial system. Existing risk analysis for GenAI focuses on endogenous risk from model performance. In comparison, exogenous risk from external factors like macroeconomic changes, natural disasters, or sudden regulatory changes, is understudied. This research fills the gap by creating a framework for measuring exogenous (systemic) risk from LLMs acting in the stock trading system. This research develops a concrete, quantitative framework to understand the systemic risk brought by using GenAI in stock investment by measuring the covariance between LLM stock price predictions across three industries (technology, automobiles, and communications) produced by eight large language models developed across the United States, Europe, and China. This paper also identifies potential data-driven technical, cultural, and regulatory mechanisms for governing AI to prevent negative financial and societal consequences.

The world of health insurance and Medicare has traditionally been perceived as complex and difficult to navigate. Fortunately, the application of Generative AI to virtual agents has begun to transform the industry. Large language and image, AI models, also known as generative AI or foundation models, have opened up new prospects for organizations and people involved in content creation. Once trained, a generative model can be "fine-tuned" for a certain content domain with far less data.

This paper examines the impact of generative AI on international diplomacy through the lens of EU–China diplomatic relationship-building. The first section introduces the broader context of AI’s geopolitical impact by distinguishing two different models—the European and Chinese models of regulating and implementing generative AI. The second discuss or explain how the two AI models contrast one another. The third section of the paper focuses on discussing Generative AI and its possible implications for EU–China relations, the extent to which their efforts are likely to strain or facilitate diplomatic relationship-building. The fourth section takes the analysis forward by examining how generative AI could be an unexpected enabling force for EU–China relationship-building. This paper purports that the distinctive EU and Chinese models of generative AI too often belie the opportunities that could potentially enable the EU and China to build their relationship, since generative AI raises shared concerns for the EU and China, and utilising generative AI could make their communications become more efficient, the EU and China may come to reach some kind of shared framework for generative AI development and governance; this could lead to productive talks in other domains such as the trade deficit issue plaguing the EU–China relations or more sensitive cross-strait issues; moreover, in the realm of public diplomacy generative AI could facilitate the EU and China’s public diplomatic efforts towards each other.

Claims that GPT-4 can outperform more than 90% of human test-takers in the US Uniform Bar Examination have sparked heated debates about the impact of Generative AI (GenAI) on legal education, academic integrity, and the future of legal practice. Yet GenAI’s capabilities in broader legal examination contexts – including in jurisdictions outside the US – are unclear. This study addresses this gap by evaluating GenAI’s performance against students who took the ‘Criminal Law’ final exam at an Australian law school in Spring 2023. Various AI models and prompt engineering techniques were used to generate 10 distinct answers to the exam question. Five criminal law tutors, unaware of AI involvement, graded a mix of AI-generated and student responses. Then, the tutors were briefed on the AI-generated papers they marked and engaged in reflective semi-structured interviews. The study found that GenAI performed below the student average in questions that required detailed legal and critical analysis. However, all GenAI papers performed better than students in open-ended questions and essay writing tasks. These results provide a benchmark for the capabilities and limitations of GenAI in higher education and provide insights into the potential implications of its application to legal assessments and education, curriculum development, and the future workforce.

Generative AI (GenAI) models are designed to produce realistic and natural data, such as images, audio, or written text. Due to their high computational and memory demands, these models traditionally run on powerful remote compute servers. However, there is growing interest in deploying GenAI models at the edge, on resource-constrained embedded devices. Since 2018, the TinyML community has proved that running fixed topology AI models on edge devices offers several benefits, including independence from internet connectivity, low-latency processing, and enhanced privacy. Nevertheless, deploying resource-consuming GenAI models on embedded devices is challenging since the latter have limited computational, memory, and energy resources. This review paper aims to evaluate the progresses made to date in the field of Edge GenAI, an emerging area of research within the broader domain of EdgeAI which focuses on bringing GenAI on edge devices. Papers released between 2022 and 2024 that address the design and deployment of GenAI models on embedded devices are identified and described. Additionally, their approaches and results are compared. This manuscript contributes to understand the ongoing transition from TinyML to Edge GenAI and provides valuable insights to the AI research community on this emerging, impactful, and quite under-explored field.

Claims that GPT-4 can outperform more than 90% of human test-takers in the US Uniform Bar Examination have sparked heated debates about the impact of Generative AI (GenAI) on legal education, academic integrity, and the future of legal practice. Yet GenAI’s capabilities in broader legal examination contexts – including in jurisdictions outside the US – are unclear. This study addresses this gap by evaluating GenAI’s performance against students who took the ‘Criminal Law’ final exam at an Australian law school in Spring 2023. Various AI models and prompt engineering techniques were used to generate 10 distinct answers to the exam question. Five criminal law tutors, unaware of AI involvement, graded a mix of AI-generated and student responses. Then, the tutors were briefed on the AI-generated papers they marked and engaged in reflective semi-structured interviews. The study found that GenAI performed below the student average in questions that required detailed legal and critical analysis. However, all GenAI papers performed better than students in open-ended questions and essay writing tasks. These results provide a benchmark for the capabilities and limitations of GenAI in higher education and provide insights into the potential implications of its application to legal assessments and education, curriculum development, and the future workforce.

AI models, particularly generative AI and large language models, reshape digital information ecosystems by curating and amplifying content through user engagement metrics. Despite their capacity to reduce bias and promote inclusivity, these models simultaneously amplify cognitive biases, entrench filter bubbles, and spread misinformation. The intensification of human–machine interaction and hyper-industrialization complicates this further, as large language models increasingly mediate how information is produced and consumed. Socio-technical agency describes how AI systems co-construct human behavior and societal norms through their design, yet their effects remain understudied in regions with limited technological infrastructure. This paper investigates AI’s influence on information dissemination, cognitive biases, and user agency across digital media environments in key regions of the Global South. Drawing on qualitative interviews and a survey of 580 media technology users in South Africa, Indonesia, India, the Philippines, and Brazil it examines how generative AI affects emotional engagement, exposure to content, and perceptions of digital truth. Framed by media ecology theory, the study evaluates AI as a cognitive extension that can both reinforce and challenge digital biases. The study proposes strategies for using generative AI to support information integrity while addressing the risks of polarization and exclusion. By centering perspectives from regions in the Global South, it contributes to more equitable discourse on AI governance, advocating regulatory and design solutions responsive to diverse media ecologies.

This paper delves into the realm of recent advancements in artificial intelligence, with a particular focus on Generative AI. Generative AI, an emerging field within AI, leverages machine learning algorithms and neural networks to generate original content across various mediums such as images, music, speech, and text. Its potential to revolutionize industries like advertising, gaming, and healthcare through personalized content creation, task automation, and enhanced accuracy in complex endeavors like drug discovery and medical diagnosis is profound. We explore different models of Generative AI, highlighting their strengths and limitations. Despite being in its early stages, Generative AI presents a promising avenue for research and development, offering numerous unexplored opportunities. Examples of prominent Generative AI models such as ChatGPT and DALL-E are provided, elucidating their applications across diverse domains. Looking forward, the potential applications of Generative AI are vast, including the development of virtual assistants for human interaction, bolstering cybersecurity, and designing intelligent robots for industrial tasks. As Generative AI continues to advance, it holds the promise of driving innovation and transformation across industries, paving the way for growth and progress in the future. Key Words: Generative AI, artificial intelligence, content generation, machine learning, neural networks, industry applications, innovation.

Generative AI has emerged as a transformative field within artificial intelligence, enabling the creation of new data that mimics real-world information and expands the boundaries of what machines can autonomously generate. This study discuss the various models of generative AI, focusing on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Auto-Regressive models, each offering distinct approaches and strengths in data generation. VAEs excel in learning latent representations, making them ideal for applications like anomaly detection and data imputation. GANs, renowned for their high-quality image synthesis, have found extensive use in tasks ranging from text-to-image conversion to super-resolution. Auto-Regressive models, on the other hand, are particularly effective in sequential data generation, such as text generation, music composition, and time series prediction. The paper highlights key applications of these models across diverse domains, including image synthesis, text generation, drug discovery, and simulation tasks in fields like healthcare, finance, and entertainment. Additionally, the study emphasizes the evaluation metrics are also called the comparitive parameters crucial for assessing the performance of generative models, such as perceptual quality metrics, Inception Score (IS), and Fréchet Inception Distance (FID), which provide quantitative insights into the quality and diversity of generated data. This study employs a systematic methodology comprising a comprehensive literature review, strategic search queries, and thematic data synthesis to explore generative AI. Key areas of focus include models (VAE, GAN, auto-regressive, flow-based), applications, evaluation techniques, challenges, and recent advances. The analysis identifies emerging trends, novel methods, and critical gaps in the field. This study also compares the performance of three Gen –AI models along with the comparative parameters like data type, Data Type, Applications, Training Complexity, Output Quality, Interpretability, Limitations, Advantages, Computational Cost and Scalability. Generative AI raises ethical concerns, including biases in training data that perpetuate stereotypes and marginalization. It can be misused for harmful purposes like creating deepfakes or spreading misinformation, impacting trust and privacy. Questions of accountability and ownership arise when AI-generated content infringes on intellectual property or causes harm. Addressing these issues is essential for responsible AI deployment.

Generative artificial intelligence has become prevalent in discussions of educational technology, particularly in the context of mathematics education. These AI models can engage in human‐like conversation and generate answers to complex questions in real‐time, with education reports accentuating their potential to make teachers' work more efficient and improve student learning. This paper provides a review of the current literature on generative AI in mathematics education, focusing on four areas: generative AI for mathematics problem‐solving, generative AI for mathematics tutoring and feedback, generative AI to adapt mathematical tasks, and generative AI to assist mathematics teachers in planning. The paper discusses ethical and logistical issues that arise with the application of generative AI in mathematics education, and closes with some observations, recommendations, and future directions.

Generative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent controversies of AI models generating near-replicas of copyrighted material highlight the need to adapt current legal frameworks and develop technical methods to mitigate copyright infringement risks. This task requires understanding the intersection between computational concepts such as large-scale data scraping and probabilistic content generation, legal definitions of originality and fair use, and economic impacts on intellectual property (IP) rights holders. However, most existing research on copyright in AI takes a purely computer science or law-based approach, leaving a gap in coordinating these approaches that only multidisciplinary e!orts can e!ectively address. To bridge this gap, our survey adopts a comprehensive approach synthesizing insights from law, policy, economics, and computer science. It begins by discussing the foundational goals and considerations that should be applied to copyright in generative AI, followed by methods for detecting and assessing potential violations in AI system outputs. Next, it explores various regulatory options influenced by legal, policy, and economic frameworks to manage and mitigate copyright concerns associated with generative AI and reconcile the interests of IP rights holders with that of generative AI producers. The discussion then introduces techniques to safeguard individual creative works from unauthorized replication, such as watermarking and cryptographic protections. Finally, it describes advanced training strategies designed to prevent AI models from reproducing protected content. In doing so, we highlight key opportunities for action and o!er actionable strategies that creators, developers, and policymakers can use in navigating the evolving copyright landscape.

In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.