Search Results for « Generative Pre-trained Transformer (GPT) technology »

GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processes, and overcoming challenges like writer’s block. Their capabilities extend beyond conventional applications, contributing to critical analysis, data augmentation, and research design, thereby elevating the efficiency and quality of scholarly endeavors. Strategically narrowing its focus, this review explores alternative dimensions of GPT and LLM applications, specifically data augmentation and the generation of synthetic data for research. Employing a meticulous examination of 412 scholarly works, it distills a selection of 77 contributions addressing three critical research questions: (1) GPT on Generating Research data, (2) GPT on Data Analysis, and (3) GPT on Research Design. The systematic literature review adeptly highlights the central focus on data augmentation, encapsulating 48 pertinent scholarly contributions, and extends to the proactive role of GPT in critical analysis of research data and shaping research design. Pioneering a comprehensive classification framework for “GPT’s use on Research Data”, the study classifies existing literature into six categories and 14 sub-categories, providing profound insights into the multifaceted applications of GPT in research data. This study meticulously compares 54 pieces of literature, evaluating research domains, methodologies, and advantages and disadvantages, providing scholars with profound insights crucial for the seamless integration of GPT across diverse phases of their scholarly pursuits.

The impact of generative artificial intelligence (AI) on education and human development is currently unknown and may have substantial ethical implications in these fields. In this commentary, we discuss the nature of Generative Pre-trained Transformer (GPT) models that use deep learning to produce human-like text. This is an effort to understand how GPT models can support students, educators, and human development professionals to enhance learning while assisting in developing their professions. We conclude by outlining the need for policy decisions and ethical considerations on incorporating these technologies into educational and human development settings.

Purpose: The absence of digitisation in construction project management, particularly in areas such as dispute resolution, project scheduling, resource allocation, and risk management, remains a critical unresolved issue. This deficiency leads to workflow inefficiencies, increased project costs, and prolonged project timelines. To address these challenges, this study explores the integration of Generative Pre-trained Transformers, an AI technology, into the role of an artificially intelligent construction project manager. It develops a novel hybrid work model between a PrCPM and the CPM-AI model, leveraging digital innovation to specifically target and mitigate these inefficiencies. Theoretical Contributions: The Artificial Pedagogy Phenomenology Theory (APPt) is introduced to elucidate observed phenomena. The theory posits that leveraging both AI capabilities and human insights can optimise future hybrid work models, improving construction project outcomes. It encompasses and addresses ethical concerns associated with AI implementation, such as balancing accountability and job displacement, with an applied focus on Engineering and Built Environment disciplines. Methodology and Design: This study employs a positivist research philosophy and a quantitative deductive approach, utilising pedagogical techniques and secondary data to replicate the decision-making processes of construction project managers. At its core, the pedagogical training of the transformer algorithm uses queries to identify current project issues, keys to integrate relevant historical data for context from historic project training data, values to derive actionable insights from past scenarios, and the dimension of key vectors to ensure balanced and effective decision-making. Findings Based on Empirical Research: Pre-alpha unit testing of the CPM-AI model trained to address dispute resolution, project scheduling, resource allocation, and risk management demonstrated statistically significant results in performance validation testing, the model achieved an accuracy rate of over *88.6% (p = 0.035) (n = 150) in its performance test on decision-making tasks pertaining to dispute resolution. It showed (x) efficiency in programme scheduling, and resource allocation, with (x) results appearing *x% (p = x) (n = x) of the time in its response rate (testing is set to commence in June 2024). The model further demonstrated exemplary efficiency in its ability to answer standardised historical Project Management Professional Exam questions, achieving a *92.8% (p = 0.015) (n = 120) aggregate score across all examination questions tested in relation to risk management. These findings establish standards for developing bespoke BE-AI models in the built environment and demonstrate seamless integration with existing tools and practices. They highlight the potential for broader AI applications across the construction industry and pave the way for future research into other built environment disciplines. Research Limitations: Research limitations include reliance on secondary data quality and potential AI biases to the South African context, requiring ongoing refinement and validation to ensure model accuracy and applicability in diverse construction environments globally, however, the researchers propose regional versioning to mitigate this effect. Practical Implications: Practical implications include enhanced project management efficiency, decision accuracy, workflow efficiencies, reduced project costs, and streamlined project timelines. Value to the Conference: The research offers crucial insights into integrating AI in construction project management, in line with the conference's theme, “Disrupting Tradition.” It introduces an innovative AI application that can redefine built environment practices, making a significant contribution to the dialogue on technological advancements within the built environment profession.

The deleterious impact of erosion due to high-velocity particle impingement adversely affects a variety of engineering/industrial systems, resulting in irreversible mechanical wear of materials/components. Brute force computational fluid dynamics (CFD) calculations are commonly used to predict surface erosion by directly solving the Navier Stokes equations for the fluid and particle dynamics; however, these numerical approaches often require significant computational resources. In contrast, recent data-driven approaches using machine learning (ML) have shown immense promise for more efficient and accurate predictions to sidestep the computationally demanding CFD calculations. To this end, we have developed FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer), a new hybrid ML architecture for accurately predicting particle trajectories and erosion on an industrial-scale steam header geometry. Our FLUID-GPT approach utilizes a Generative Pre-Trained Transformer 2 (GPT-2) with a Convolutional Neural Network (CNN) for the first time to predict surface erosion using only information from five initial conditions: particle size, main-inlet speed, main-inlet pressure, sub-inlet speed, and sub-inlet pressure. Compared to the Long- and Short-Term Memory (LSTM) ML techniques used in previous work, our FLUID-GPT model is much more accurate (a 54% decrease in mean squared error) and efficient (70% less training time). Our work demonstrates that FLUID-GPT is an accurate and efficient ML approach for predicting time-series trajectories and their subsequent spatial erosion patterns in these complex dynamic systems.