ChatGPT or Bard: Who is a better Certified Ethical Hacker?

Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities

Distribution network design is one of the most essential strategic decisions in logistics systems and supply chains, as it structures the network by determining the number of echelons and specifying the types, quantities, and locations of various facilities. This chapter presents decision-support models for distribution and logistics network design, covering both continuous location models (gravity location models) and discrete network design models (capacitated/uncapacitated and their relevant variants). Comprehensive numerical experiments based on the same case study offer a direct comparison of these different network design approaches. Furthermore, as one of the most disruptive technologies in Industry 5.0, the impact of generative AI (GenAI) and large language models (LLMs) on the research and education of distribution network design is thoroughly analyzed. Our experiments demonstrate that GenAI and LLMs are powerful tools in providing structured introductions to existing problems, guiding model selection and explanation, assisting with coding and code clarification, facilitating model comparison, and generating initial ideas for new research questions in distribution and logistics network design. However, they also face reliability and accuracy challenges, exhibit limited critical thinking capabilities, and struggle to address emerging research questions. Our findings offer valuable guidance for incorporating GenAI tools and LLMs into research and educational processes in distribution and logistics network design.

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.

Burnout has become a significant concern in high-stress industries, impacting mental health and productivity. Traditional burnout management solutions lack real-time adaptability and personalized intervention strategies. This research explores an AI-driven burnout management mechanism leveraging Generative AI, MongoDB Atlas, Python, and Large Language Models (LLMs) to provide real-time detection, personalized coping strategies, and predictive analytics. The system collects and stores user responses, behavioral patterns, and sentiment data in MongoDB Atlas, while LLMs analyze and generate tailored burnout mitigation recommendations. Experimental results demonstrate the effectiveness of the proposed approach in early burnout detection, personalized intervention, and user engagement, showing improvements in stress reduction and work-life balance. The proof of concept for AI-powered burnout detection and intervention demonstrates the technical feasibility of using Large Language Models (LLMs), Generative AI, MongoDB Atlas, and Python-based automation for real-time mental health support. The system successfully classifies burnout severity using LLMs trained on sentiment analysis, achieving an accuracy of 91.2%, which significantly outperforms traditional rule-based sentiment models. The use of MongoDB Atlas for scalable data storage ensures real-time burnout tracking, while FastAPI-based backend automation allows for seamless integration across platforms. Furthermore, Generative AI provides personalized stress interventions, dynamically adapting to user feedback and improving engagement. These technical validations highlight how AI-driven burnout management can be scalable, real-time, and enterprise-ready, paving the way for AI-powered diagnostics in mental health applications across various industries.

ABSTRACTThere is mounting global interest in the revolutionary potential of AI tools. However, its use in healthcare carries certain risks. Some argue that opaque (‘black box’) AI systems in particular undermine patients' informed consent. While interpretable models offer an alternative, this approach may be impossible with generative AI and large language models (LLMs). Thus, we propose that AI tools should be evaluated for clinical use based on their implementation risk, rather than interpretability. We introduce a practical decision algorithm for the clinical implementation of black‐box AI by evaluating its risk of implementation. Applied to the case of an LLM for surgical informed consent, we assess a system's implementation risk by evaluating: (1) technical robustness, (2) implementation feasibility and (3) analysis of harms and benefits. Accordingly, the system is categorised as minimal‐risk (standard use), moderate‐risk (innovative use) or high‐risk (experimental use). Recommendations for implementation are proportional to risk, requiring more oversight for higher‐risk categories. The algorithm also considers the system's cost‐effectiveness and patients' informed consent.

Generative AI is designed to create new content from trained parameters. Learning from large amounts of data, many of these models aim to simulate human conversation. Generative AI is being applied to many different sectors. Within healthcare there has been innovation specifically towards generative AI models trained on electronic medical record data. A recent review characterizes these models, their strengths, and weaknesses. Inspired by that work, we present our evaluation checklist for generative AI models applied to electronic medical records.

The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges, necessitating innovative regulatory approaches. In this perspective, we discuss the risks of GenAI and LLM-based medical devices, the limitations of current medical device regulation frameworks when applied to GenAI or LLMs, and advocate for global collaboration in regulatory science research through engaging multidisciplinary expertise and focusing on the needs of diverse populations.

The TrollLabs Open dataset includes comprehensive information that offers a comparison of design practices and outcomes between human participants and Generative AI during a hackathon event. The dataset was curated through the running of a prototyping hackathon designed to assess the abilities and performance of generative AI, specifically ChatGPT, in the early stages of engineering design. This assessment involved comparing ChatGPT's performance to that of experienced engineering students in a hackathon setting, where participants competed by making a prototype that fires a NERF dart as far as possible. In this setup, all ideas, concepts, strategies, and actions undertaken by the AI-controlled team were autonomously generated by the ChatGPT, without human intervention or guidance, but implemented by two participants. Five self-directed baseline teams competed against the AI team. The dataset comprises 116 prototype entries and 433 edges (connection) that enable comparative analysis of design practices and performance between the team instructed solely by generative AI and baseline teams of experienced engineering design students. Prototypes and their attribute data were captured using Pro2booth, an online prototype capture platform running on participants' phones and computers. The dataset includes a transcript of the conversation between ChatGPT and the team responsible for implementing its recommendations, featuring 97 exchanges of prompts and responses. It contains the initial prompt used to instruct the AI, the objective and rules of the hackathon and the objective performance of teams, showing the ChatGPT team finishing 2nd among six teams. To the authors' knowledge, the TrollLabs Open dataset is the first and only open resource that directly compares the performance of generative AI with human teams in an engineering design context. Thus, it is intended to be a valuable resource to design researchers, engineering and design students, educators, and industry professionals seeking to find strategies for implementing generative AI tools in their design processes. By offering a comprehensive data collection, the dataset enables external researchers to conduct in-depth analyses that could highlight the practical implications of integrating generative AI in design practices, possibly providing an overview of its limitations and presenting recommendations for improved integration in the design process.

Blockchain-based auditing of legal decisions supported by explainable AI and generative AI tools

CAMERON VU, Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443, and DARIA PANOVA, Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443, and JOSIAH KOWALSKI, Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443, and Dr. W. LIAO (Faculty Advisor), Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443, and Dr. O. Guzide (Faculty Advisor), Dept of Computer Science and Math & ENGR, Shepherd University, Shepherdstown, WV, 25443. Smart Parking Space Detection with Generative Artificial Intelligence and Large Language Models. The increasing relevance of generative AI and large language models is reshaping various sectors of modern society. These advancements have spurred notable progress in fields such as healthcare, finance, and education. Yet, the application of AI extends beyond expert domains, offering simplified solutions to everyday tasks for the general populace. This project harnesses the power of generative artificial intelligence and large language models to develop a practical application: smart parking space detection. By leveraging these technologies, individuals can effortlessly ascertain the availability of parking spots in monitored lots via camera or photographic monitoring, facilitated by a straightforward algorithm. The overarching objective is twofold: to engineer a user-friendly system utilizing generative AI principles and to demonstrate the potential for such technologies to enhance the daily experiences of ordinary individuals.

BackgroundNIAGADS is a national data repository that offers qualified investigators access to genomic data for Alzheimer’s disease (AD) and related dementia. In addition, NIAGADS has made substantial effort to curate, harmonize, standardize, and disseminate AD‐relevant variant, gene, and sequence annotations from publications, functional genomics datasets, and summary statistics deposited at NIAGADS. These results are made available to the public in a collection of interactive knowledgebases (AD Variant Portal, FILER Functional Genomics Repository, VariXam, Alzheimer’s GenomicsDB & Genome Browser), all of which are accessible programmatically via the NIAGADS API. However, as these offerings grow, navigating them can be challenging. Here, we introduce AI‐based enhancements to NIAGADS sites to help guide researchers and facilitate data discovery.MethodWe leverage OpenAI’s generative AI to build and train three large language models (LLMs) based on NIAGADS documentation, step‐by‐step recipes for data‐access requests, subject‐specific vocabularies, and the OpenAPI specification defining the NIAGADS API that allows programmatic access to the NIAGADS knowledgebases. For users of the API and to enhance search interfaces, we build on the LLMs to construct a framework for handling complex natural language instructions that decomposes an inquiry into tasks and subtasks and then plans, selects, and optionally executes API calls and parses the results.ResultDeveloping these LLMs allows NIAGADS to improve user experiences by integrating topic‐specific chatbots and generative AI search tools into NIAGADS sites. Rule‐based chatbots that leverage conversational AI on the NIAGADS portal and Data Sharing Service will respond to inquiries with answers inferred from the LLMs, with responses improving with user feedback. These bots will also supplement help requests, suggesting solutions to common inquiries. Planner‐enhanced generative AI based on the API‐specification trained LLMs will be tied to knowledgebase searches and filters in resources such as the GenomicsDB and FILER to allow users to leverage natural language processing to ask sophisticated questions that require multiple API calls to resolve the answer.ConclusionIntroducing AI‐enhanced search creates an interactive opportunity for NIAGADS users to learn new information or discover resources and tools they can use to supplement their research, which, in turn, improves NIAGADS ability to support AD genetics research.

Large language models (LLMs), and generative AI generally, raise significant concerns regarding human rights. Their promise in finding insights in patterns of data have to be weighed against potential risks to individuals and societies. The typical perspective, which emphasizes accuracy, capability, or scope of such systems, overlooks the fact that generative AI technologies exploit massive collections of data about human behaviors, thoughts, and ideas. The datafication of human life should be examined through the lens of human rights, specifically with regard to autonomy, dignity, equality, and community. This piece argues that discussions about LLMs and generative AI are inherently linked to data originated from individuals, whose information are embedded in the training data. Data are human rights issues because information about individuals are buried in the data. Technical solutions alone are insufficient to address the human rights distortions produced by LLMs. Policy should focus instead on the fact that data are collected on rights-bearing individuals and groups who have been given very little leeway to discuss the implications of or choose to be in the enterprise of creating generative AI.

This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education.

Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.

In this paper, we propose and demonstrate a prototype for leveraging Generative AI (GenAI) in financial risk analysis, specifically focusing on fine-tuning GPT models with proprietary data. Financial risk modeling, development, validation, and approval require not only advanced AI techniques but also careful implementation, given the vast and complex datasets involved in such tasks. The research underscores the critical importance of human oversight in mitigating potential failures that can arise from fully automated mathematical models. The study explores the application of Large Language Models (LLMs) in various financial risk domains, such as credit risk assessment, market risk forecasting, and anomaly detection. While synthetic data generation is excluded from this work, the research highlights the use of zero-shot classification leveraging Hugging Face models and OpenAI tools. ChatGPT achieved over 70% accuracy in generating relevant questions and demonstrated 60% correctness in classification tasks. Additionally, we present a prototype pipeline that integrates GenAI capabilities into financial workflows, which is implementable on small-scale computing systems. This includes backend testing via Flask and rapid prototyping using cURL commands, offering a practical approach to testing and deploying models. By fine-tuning GenAI with domain-specific data and optimizing decision-making processes, this research highlights the transformative potential of integrating generative AI into financial risk management. The study provides insights into enhancing model efficiency, regulatory compliance, and scalability. Moreover, it addresses critical challenges such as handling large datasets and ensuring ethical AI use in decision-making systems. This work contributes to advancing the adoption of GenAI in financial analytics, paving the way for innovative, robust, and efficient methodologies to support the evolving demands of the financial sector.