Old Dominion University
Southern Methodist University
Bowling Green State University
The College of Wooster
University of Pennsylvania
Northern Illinois University
James Madison University
San Jose State University
Perception Of Bias In Chatgpt: Analysis Of Social Media Data,
2023
Slippery Rock University of Pennsylvania
Perception Of Bias In Chatgpt: Analysis Of Social Media Data, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Ahmed El Noshokaty, Tareq Nasralah
Computer Information Systems Faculty Publications
In this study, we aim to analyze the public perception of Twitter users with respect to the use of ChatGPT and the potential bias in its responses. Sentiment and emotion analysis were also analyzed. Analysis of 5,962 English tweets showed that Twitter users were concerned about six main types of biases, namely: political, ideological, data & algorithmic, gender, racial, cultural, and confirmation biases. Sentiment analysis showed that most of the users reflected a neutral sentiment, followed by negative and positive sentiment. Emotion analysis mainly reflected anger, disgust, and sadness with respect to bias concerns with ChatGPT use.
Explainable Artificial Intelligence: Approaching It From The Lowest Level,
2023
University of South Alabama
Explainable Artificial Intelligence: Approaching It From The Lowest Level, Ralf P. Riedel
Theses and Dissertations
The increasing complexity of artificial intelligence models has given rise to extensive work toward understanding the inner workings of neural networks. Much of that work, however, has focused on manipulating input data feeding the network to assess their affects on network output or pruning model components after the often-extensive time-consuming training. It is postulated in this study that understanding of neural network can benefit from model structure simplification. In turn, it is shown that model simplification can benefit from investigating network node, the most fundamental unit of neural networks, evolving trends during training. Whereas studies on simplification of model structure …
Towards Understanding The Geospatial Skills Of Chatgpt: Taking A Geographic Information Systems (Gis) Exam,
2023
National University of Ireland, Maynooth
Towards Understanding The Geospatial Skills Of Chatgpt: Taking A Geographic Information Systems (Gis) Exam, Peter Mooney, Wencong Cui, Boyuan Guan, Levente Juhasz
GIS Center
This paper examines the performance of ChatGPT, a large language model (LLM), in a geographic information systems (GIS) exam. As LLMs like ChatGPT become increasingly prevalent in various domains, including education, it is important to understand their capabilities and limitations in specialized subject areas such as GIS. Human learning of spatial concepts significantly differs from LLM training methodologies. Therefore, this study aims to assess ChatGPT's performance and ability to grasp geospatial concepts by challenging it with a real GIS exam. By analyzing ChatGPT's responses and evaluating its understanding of GIS principles, we gain insights into the potential applications and challenges …
Physics-Informed Neural Networks For Agent-Based Epidemiological Model Calibration,
2023
Harvard University
Physics-Informed Neural Networks For Agent-Based Epidemiological Model Calibration, Alvan C. Arulandu, Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Disease Informed Neural Network And Mathematical Modeling Of Covid-19 With Human Intervention,
2023
Inter American University of Puerto Rico-San German
Disease Informed Neural Network And Mathematical Modeling Of Covid-19 With Human Intervention, Jeremis Morales-Morales, Alonso Gabriel Ogueda, Carmen Caiseda, Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Designing Depaul,
2023
DePaul University
Designing Depaul
DePaul Magazine
DePaul’s comprehensive, collaborative plan creates a road map that positions the university for monumental impact.
Executive Order On The Safe, Secure, And Trustworthy Development And Use Of Artificial Intelligence,
2023
United States Office of the President
Executive Order On The Safe, Secure, And Trustworthy Development And Use Of Artificial Intelligence, Joseph R. Biden
Copyright, Fair Use, Scholarly Communication, etc.
Section 1. Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative, and secure. At the same time, irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security. Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.
My Administration places the highest urgency …
Ai And The Creative Process: Part Three,
2023
Lindenwood University
Ai And The Creative Process: Part Three, James Hutson
Faculty Scholarship
Article discussing the effects of artificial intelligence on the creative process in the art world.
Ai As A License Review Assistant,
2023
Minnesota State University, Mankato
Ai As A License Review Assistant, Nat Gustafson-Sundell
Library Services Publications
I will present the steps we have taken to develop a prototype AI assistant for license review. I’ll explain our criteria for the selection of an AI tool for this project. We reviewed ChatGPT, Claude 2, Bard, and PDF readers. My goal was to develop an initial prototype in a Jupyter Notebook environment so I could easily re-load context information, including a license checklist, but I’ll explain why I revised this goal, instead to linger over license review interactions with ChatBots. I’ll discuss early results, demonstrate example license review interactions, and outline my next steps.
Local Model Agnostic Xai Methodologies Applied To Breast Cancer Malignancy Predictions,
2023
Western University
Local Model Agnostic Xai Methodologies Applied To Breast Cancer Malignancy Predictions, Heather Hartley
Electronic Thesis and Dissertation Repository
This thesis examines current state-of-the-art Explainable Artificial Intelligence (XAI) methodologies applicable to breast cancer diagnostics, as well as local model-agnostic XAI methodologies more broadly. It is well known that AI is underutilized in healthcare due to the fact that black box AI methods are largely uninterpretable. The potential for AI to positively affect health care outcomes is massive, and AI adoption by medical practitioners and the community at large will translate to more desirable patient outcomes. The development of XAI is crucial to furthering the integration of AI within healthcare, as it will allow medical practitioners and regulatory bodies to …
Artificial Intelligence And Human Hope,
2023
Seattle Pacific University
Artificial Intelligence And Human Hope, Michael Paulus
SPU Works
Slides from a book talk at Folio: The Seattle Atheneum on Artificial Intelligence and the Apocalyptic Imagination: Artificial Agency and Human Hope.
Evocative And Provocative Image-Making In The Age Of Generative Ai,
2023
University of Nevada, Las Vegas
Evocative And Provocative Image-Making In The Age Of Generative Ai, Julian Kilker
Tradition Innovations in Arts, Design, and Media Higher Education
Editorial for inaugural AI-focused special issue of Tradition-Innovations in Arts, Design, and Media Higher Education, published under the auspices of the Alliance for the Arts in Research Universities (a2ru). Discusses three articles by five authors in this issue: (1) Choreographing Shadows: Interdisciplinary collaboration to orchestrate ethical image-making by Mark Burchick and Diana Pasulka; (2) Giving Up Control: Hybrid AI-augmented workflows for image-making by Joshua Vermillion; and (3) Hands are Hard: Unlearning how we talk about machine learning in the arts by Adam Hyland and Oscar Keyes.
Editing this special issue explored several key questions: What does “innovation” mean when …
Healthcare Ai: A Revised Quebec Framework For Nursing Education,
2023
McGill University
Healthcare Ai: A Revised Quebec Framework For Nursing Education, Maggie Lattuca, Diane Maratta, Ute Beffert, Annie Chevrier, Laura Winer
Quality Advancement in Nursing Education - Avancées en formation infirmière
Artificial Intelligence Health Technologies (AIHT) are taking their place in the practice of nursing. However, the curricula have not evolved to include competencies required of nursing graduates to incorporate their impact on theory and practice. This project was born of an identified need by nurse educators to articulate new competencies grounded in the literature and expert knowledge. Based on extensive literature reviews and an iterative process of expert validation, this paper provides recommendations for five new competencies that will be needed for nurses to use AIHT responsibly, ethically, and intelligently in the best interests of patient care. The methodology started …
Semantic Lung Segmentation From Chest X-Ray Images Using Seg-Net Deep Cnn Model,
2023
Duhok Polytechnic University College of Health and Medical Technology-Shekhan Duhok - Iraq
Semantic Lung Segmentation From Chest X-Ray Images Using Seg-Net Deep Cnn Model, Dathar Abas Hasan, Umed Hayder Jader
Polytechnic Journal
Implementing an accurate image segmentation to extract the lung shape from X-ray images is a vital step in designing a CAD system that diagnoses various types of chest diseases. Lung segmentation is a complex process due to the blurred regions that separate the lung area and the rest of the image. The conventional image segmentation techniques do not meet the ambitions to achieve precise lung segmentation. In this paper, we utilized the Seg-Net semantic segmentation model as a practical approach to distinguish the lung region pixels in X-ray images. The model involves an encoder network that extracts the data from …
Peatmoss: Mining Pre-Trained Models In Open-Source Software,
2023
Purdue University
Peatmoss: Mining Pre-Trained Models In Open-Source Software, Wenxin Jiang, Jason Jones, Jerin Yasmin, Nicholas Synovic, Rajiv Sashti, Sophie Chen, George K. Thiruvathukal, Yuan Tian, James C. Davis
Computer Science: Faculty Publications and Other Works
Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks. Despite the widespread use of PTMs, we know little about the corresponding software engineering behaviors and challenges. To enable the study of software engineering with PTMs, we present the PeaTMOSS dataset: Pre-Trained Models in Open-Source Software. PeaTMOSS has three parts: a snapshot of (1) 281,638 PTMs, (2) 27,270 open-source software repositories that use PTMs, and (3) a mapping between PTMs and the projects that use them. We challenge PeaTMOSS miners to discover software engineering …
Synthesizing Sentience: Integrating Large Language Models And Autonomous Agents For Emulating Human Cognitive Complexity,
2023
Lindenwood University
Synthesizing Sentience: Integrating Large Language Models And Autonomous Agents For Emulating Human Cognitive Complexity, Jay Ratican, James Hutson, Daniel Plate
Faculty Scholarship
The paper aims to present a novel methodology for emulating the intricacies of human cognitive complexity by ingeniously integrating large language models with autonomous agents. Grounded in the theoretical framework of the modular mind theory-originally espoused by Fodor and later refined by scholars such as Joanna Bryson—the study seeks to venture into the untapped potential of large language models and autonomous agents in mirroring human cognition. Recent advancements in artificial intelligence, exemplified by the inception of autonomous agents like Age in GPT, auto GPT, and baby AGI, underscore the transformative capacities of these technologies in diverse applications. Moreover, empirical studies …
Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering,
2023
Singapore Management University
Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering, Wen Song, Yi Liu, Zhiguang Cao, Yaoxin Wu, Qiqiang Li
Research Collection School Of Computing and Information Systems
Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense …
Machine Learning Prediction Of Hea Properties,
2023
California Polytechnic State University, San Luis Obispo
Machine Learning Prediction Of Hea Properties, Nicholas J. Beaver, Nathaniel Melisso, Travis Murphy
College of Engineering Summer Undergraduate Research Program
High-entropy alloys (HEA) are a very new development in the field of metallurgical materials. They are made up of multiple principle atoms unlike traditional alloys, which contributes to their high configurational entropy. The microstructure and properties of HEAs are are not well predicted with the models developed for more common engineering alloys, and there is not enough data available on HEAs to fully represent the complex behavior of these alloys. To that end, we explore how the use of machine learning models can be used to model the complex, high dimensional behavior in the HEA composition space. Based on our …
Ethics And Social Justice For Ai In Data Science,
2023
California Polytechnic State University, San Luis Obispo
Ethics And Social Justice For Ai In Data Science, Arya Ramchander, Kylene Nicole Landenberger
College of Engineering Summer Undergraduate Research Program
The advances of AI raise several critical questions about human values and ethics, highlighting the need for researchers and developers to consider the ethical implications and the risks of neglecting them. In the past few years, student researchers have developed an AI model that allows users to test their surveys for possible breaches of subject confidentiality. This allows the users to gauge the ethicality of their proposal. This summer, we have expanded on this research and launched an interactive model for students and researches to assess their current work for ethical and social justice implications. Using Langchain and Figma, we …
Exploring Approaches To Engage K-12 Students In Learning Computational Thinking Using Collaborative Robots,
2023
California Polytechnic State University, San Luis Obispo
Exploring Approaches To Engage K-12 Students In Learning Computational Thinking Using Collaborative Robots, Zoila Anuri Kanu
College of Engineering Summer Undergraduate Research Program
Minority students are largely underrepresented in the STEM field. The goal for this project was to develop a program which promotes the inclusion of computation skills among students and help them work collaboratively with the use of human – robot interaction. Robots are such a strong tool that can be used to enhance computational thinking and engage students towards a technical field. Through workshops and readings about computational thinking we worked on building a block-based program that introduces the uses of robots as teaching tool for computational thinking.
