Physical Sciences and Mathematics Commons^™

Emulating Future Neurotechnology Using Magic, Jay A. Olson, Mariève Cyr, Despina Z. Artenie, Thomas Strandberg, Lars Hall, Matthew L. Tompkins, Amir Raz, Petter Johansson

Psychology Faculty Articles and Research

Recent developments in neuroscience and artificial intelligence have allowed machines to decode mental processes with growing accuracy. Neuroethicists have speculated that perfecting these technologies may result in reactions ranging from an invasion of privacy to an increase in self-understanding. Yet, evaluating these predictions is difficult given that people are poor at forecasting their reactions. To address this, we developed a paradigm using elements of performance magic to emulate future neurotechnologies. We led 59 participants to believe that a (sham) neurotechnological machine could infer their preferences, detect their errors, and reveal their deep-seated attitudes. The machine gave participants randomly assigned positive …

Predicting The Outcomes Of Internet-Based Cognitive Behavioral Therapy For Tinnitus: Applications Of Artificial Neural Network And Support Vector Machine, Hansapani Rodrigo, Eldré W. Beukes, Gerhard Andersson, Vinaya Manchaiah

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Purpose:

Internet-based cognitive behavioral therapy (ICBT) has been found to be effective for tinnitus management, although there is limited understanding about who will benefit the most from ICBT. Traditional statistical models have largely failed to identify the nonlinear associations and hence find strong predictors of success with ICBT. This study aimed at examining the use of an artificial neural network (ANN) and support vector machine (SVM) to identify variables associated with treatment success in ICBT for tinnitus.

Method:

The study involved a secondary analysis of data from 228 individuals who had completed ICBT in previous intervention studies. A 13-point reduction …

An Empirical Study Of Artifacts And Security Risks In The Pre-Trained Model Supply Chain, Wenxin Jiang, Nicholas Synovic, Rohan Sethi, Aryan Indarapu, Matt Hyattt, Taylor R. Schorlemmer, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Deep neural networks achieve state-of-the-art performance on many tasks, but require increasingly complex architectures and costly training procedures. Engineers can reduce costs by reusing a pre-trained model (PTM) and fine-tuning it for their own tasks. To facilitate software reuse, engineers collaborate around model hubs, collections of PTMs and datasets organized by problem domain. Although model hubs are now comparable in popularity and size to other software ecosystems, the associated PTM supply chain has not yet been examined from a software engineering perspective.

We present an empirical study of artifacts and security features in 8 model hubs. We indicate the potential …

Open-Source Clinical Machine Learning Models: Critical Appraisal Of Feasibility, Advantages, And Challenges, Keerthi B. Harish, W. Nicholson Price Ii, Yindalon Aphinyanaphongs

Articles

Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and …

From Machine Learning To Deep Learning: A Comprehensive Study Of Alcohol And Drug Use Disorder, Banafsheh Rekabdar, David L. Albright, Haelim Jeong, Sameerah Talafha

Computer Science Faculty Publications and Presentations

This study aims to train and validate machine learning and deep learning models to identify patients with risky alcohol and drug misuse in a Screening, Brief Intervention, and Referral to Treatment (SBIRT) program. An observational cohort of 6978 adults was admitted in the western region of Alabama at three medical facilities between January and December of 2019. Data were cleaned and pre-processed using data imputation techniques and an augmented sampling data method. The primary analysis involved the multi-class classification of alcohol and drug misuse. Our study shows that accurate identification of alcohol and drug use screening instrument scores was best …

Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael

Publications and Research

What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …

Multi-Functional Job Roles To Support Operations In A Multi-Faceted Jewel Enabled By Ai And Digital Transformation, Steven M. Miller

Research Collection School Of Computing and Information Systems

In this story, we highlight the way in which the use of AI enabled support systems, together with work process digital transformation and innovative approaches to job redesign, have combined to dramatically change the nature of the work of the front-line service staff who protect and support the facility and visitors at the world’s most iconic airport mall and lifestyle destination.

What Machines Can't Do (Yet) In Real Work Settings, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

AI systems may perform well in the research lab or under highly controlled application settings, but they still needed human help in the types of real world work settings we researched for a new book, Working With AI: Real Stories of Human-Machine Collaboration. Human workers were very much in evidence across our 30 case studies. In this article, we use those examples to illustrate our list of AI-enabled activities that still require human assistance. These are activities where organizations need to continue to invest in human capital, and where practitioners can expect job continuity for the immediate future

Explainable Artificial Intelligence Applications In Cyber Security: State-Of-The-Art In Research, Zhibo Zhang, Hussam Al Hamadi, Ernesto Damiani, Chan Yeob Yeun, Fatma Taher

All Works

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in …

Artificial Intelligence And Human Employment, Singapore Management University

Perspectives@SMU

AI will replace humans in repetitive tasks. Greater value can be created when it augments and complements the jobs people do

Singapore Public Sector Ai Applications Emphasizing Public Engagement: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

This article provides an overview of six examples of public sector AI applications in Singapore that illustrate different ways of enhancing engagement with the public. These applications demonstrate ways of enhancing engagement with the public by providing greater accessibility to government services (access anywhere, anytime) and speedier responses to public processes and feedback. Some applications make it substantially easier for members of the public to do things or make choices, while others reduce waiting time, either across an entire public infrastructure, or for an individual transaction. Some provide highly individualized coaching to guide a person through the process of doing …

Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virginia B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai

Electrical & Computer Engineering Faculty Research

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …

Directed Acyclic Graph-Based Neural Networks For Tunable Low-Power Computer Vision, Abhinav Goel, Caleb Tung, Nick Eliopoulos, Xiao Hu, George K. Thiruvathukal, James C. Davis, Yung-Hisang Lu

Computer Science: Faculty Publications and Other Works

Processing visual data on mobile devices has many applications, e.g., emergency response and tracking. State-of-the-art computer vision techniques rely on large Deep Neural Networks (DNNs) that are usually too power-hungry to be deployed on resource-constrained edge devices. Many techniques improve DNN efficiency of DNNs by compromising accuracy. However, the accuracy and efficiency of these techniques cannot be adapted for diverse edge applications with different hardware constraints and accuracy requirements. This paper demonstrates that a recent, efficient tree-based DNN architecture, called the hierarchical DNN, can be converted into a Directed Acyclic Graph-based (DAG) architecture to provide tunable accuracy-efficiency tradeoff options. We …

Application In Medicine: Has Artificial Intelligence Stood The Test Of Time, Mir Ibrahim Sajid, Shaheer Ahmed, Usama Waqar, Javeria Tariq, Mohsin Chundrigar, Samira Shabbir Balouch, Sajid Abaidullah

Medical College Documents

Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers …

Biometrics And An Ai Bill Of Rights, Margaret Hu

Faculty Publications

This Article contends that an informed discussion on an AI Bill of Rights requires grappling with biometric data collection and its integration into emerging AI systems. Biometric AI systems serve a wide range of governmental purposes, including policing, border security and immigration enforcement, and biometric cyberintelligence and biometric-enabled warfare. These systems are increasingly categorized as "high-risk" when deployed in ways that may impact fundamental constitutional rights and human rights. There is growing recognition that high-risk biometric AI systems, such as facial recognition identification, can pose unprecedented challenges to criminal procedure rights. This Article concludes that a failure to recognize these …

Gradient-Free Method For Heavily Constrained Nonconvex Optimization, Wanli Shi, Hongchang Gao, Bin Gu

Machine Learning Faculty Publications

Zeroth-order (ZO) method has been shown to be a powerful method for solving the optimization problem where explicit expression of the gradients is difficult or infeasible to obtain. Recently, due to the practical value of the constrained problems, a lot of ZO Frank-Wolfe or projected ZO methods have been proposed. However, in many applications, we may have a very large number of nonconvex white/black-box constraints, which makes the existing zeroth-order methods extremely inefficient (or even not working) since they need to inquire function value of all the constraints and project the solution to the complicated feasible set. In this paper, …

Identification Of Linear Non-Gaussian Latent Hierarchical Structure, Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang

Machine Learning Faculty Publications

Traditional causal discovery methods mainly focus on estimating causal relations among measured variables, but in many real-world problems, such as questionnaire-based psychometric studies, measured variables are generated by latent variables that are causally related. Accordingly, this paper investigates the problem of discovering the hidden causal variables and estimating the causal structure, including both the causal relations among latent variables and those between latent and measured variables. We relax the frequently-used measurement assumption and allow the children of latent variables to be latent as well, and hence deal with a specific type of latent hierarchical causal structure. In particular, we define …

What’S So Artificial And Intelligent About Artificial Intelligence? A Conceptual Framework For Ai, Rebekah L. H. Rice

SPU Works

There is currently a good deal of attention being focused on artificial intelligence, broadly speaking, and deep learning, specifically. The attention is warranted, as these technologies are predicted to affect our collective lives in innumerable ways even beyond their already expansive social reach. There is much to consider regarding the benefits and potential harms of AI. And of course there are the apocalyptic musings about super-intelligent machines running amok, bringing science fiction scenarios uncomfortably close to anticipated reality. But productively engaging in discussions about the ethical and social implications of AI, and about which sorts of futures it is reasonable …

A Theological Framework For Reflection On Artificial Intelligence, Michael D. Langford

SPU Works

The theological questions before us in a digital age are pressing. What does God think of AI? Is AI good or evil? Will AI save us? What sort of future will AI give us? In what follows, I want to briefly introduce a few theological concepts that will hopefully help equip us for theological reflection on AI. We will begin with the question of epistemology, or how it is that we come by knowledge; in the realm of theology, this centers on revelation. We will then touch on the doctrine of creation, including the understanding of what it means to …

Artificial Intelligence And Theological Personhood, Michael D. Langford

SPU Works

Can AI be a person? What does God tell us about humanity and personhood? These are questions of theological anthropology and involve inquiring after the nature of humanity as God’s creation and what God wills for human personhood.

To address these inquiries, we will look at three biblical texts that bear on issues of theological anthropology, hopefully garnering some theological resources to consider the anthropological status of AI. Specifically, we will look at three “creation” texts that necessarily deal with the nature of human personhood within the divine economy of salvation history. The first is Genesis 1 and 2, which …

Reinforcement In The Information Revolution, Phillip M. Baker

SPU Works

This chapter will outline what it means to be a behaving human and how AI makes sense of these concepts. It will then explore possible near-future implications of our remarkable progress in understanding how human behavior works with the assistance of AI from a neurobiological basis. A focus on understanding the reinforcement mechanisms of the brain will reveal the consequences of ceding control of so much of our brain-environment interactions to AI. It will conclude by offering a potential Christian response to this digital reality from a uniquely Anabaptist perspective.

An Introduction To Artificial Intelligence, Carlos R. Arias

SPU Works

This chapter explores the evolution of artificial intelligence, starting with the first ideas of Alan Turing, going through the promises of its inception, and landing in our current state, when AI invokes a sense of power and awe. Next, the chapter will provide a summary of different technologies related to AI and machine learning, such as deep neural networks, to help the reader distinguish different terminologies. The chapter will end with a discussion of some potential tendencies concerning how AI may be used or evolve in the near future, and some questions about the technology in the long term.

Sin And Grace, Bruce D. Baker

SPU Works

The theological lens of sin and grace gives a broader and deeper viewpoint than mere ethics. Ethical analysis is of course useful and necessary, but ethics alone is not enough. Ethics apart from a robust, holistic understanding of humans as persons-in-communion will remain mired in reductionist thinking about human dignity and morality. Therefore, this final chapter addresses the ethical issues of AI through the lens of sin and grace.

Epilogue: A Litany For Faithful Engagement With Artificial Intelligence, Bruce D. Baker

SPU Works

A litany is a thoughtfully organized prayer for use in public worship by the church, or as a personal devotional practice by individuals. This seems a fitting way to close our reflection on AI, faith, and the future. Prayer will be essential to our faithful response to the new opportunities and challenges AI brings. Our hope is that this litany will serve as a practical guide to thoughtful invocation of the Holy Spirit in prayers for wisdom and discernment, and in the daily disciplines of spiritual growth.

An Overview Of Technologies Deployed In Gcc Countries To Combat Covid-19, Samia Loucif, Murad Al-Rajab, Reem Salem, Nadine Akkila

All Works

Since December 2019, COVID-19 and all of its variants continue to ravage the planet with consequent negative impact that has completely changed our lives within a short period of time after the outbreak of the Virus. On March 11, 2020, COVID-19 was declared a global pandemic by the World Health Organization. Since then, a group of new COVID-19 variants has emerged posing a greater danger to humanity. By the start of August 2021, the reported COVID-19 related death toll across the globe has rocketed to 4,233,139. To deal with the COVID-19 pandemic, countries across the world have rushed to develop …

Integrating Cultural Knowledge Into Artificially Intelligent Systems: Human Experiments And Computational Implementations, Anurag Acharya

FIU Electronic Theses and Dissertations

With the advancement of Artificial Intelligence, it seems as if every aspect of our lives is impacted by AI in one way or the other. As AI is used for everything from driving vehicles to criminal justice, it becomes crucial that it overcome any biases that might hinder its fair application. We are constantly trying to make AI be more like humans. But most AI systems so far fail to address one of the main aspects of humanity: our culture and the differences between cultures. We cannot truly consider AI to have understood human reasoning without understanding culture. So it …

Deep-Learning-Incorporated Augmented Reality Application For Engineering Lab Training, John Estrada, Sidike Paheding, Xiaoli Yang, Quamar Niyaz

Michigan Tech Publications

Deep learning (DL) algorithms have achieved significantly high performance in object detection tasks. At the same time, augmented reality (AR) techniques are transforming the ways that we work and connect with people. With the increasing popularity of online and hybrid learning, we propose a new framework for improving students’ learning experiences with electrical engineering lab equipment by incorporating the abovementioned technologies. The DL powered automatic object detection component integrated into the AR application is designed to recognize equipment such as multimeter, oscilloscope, wave generator, and power supply. A deep neural network model, namely MobileNet-SSD v2, is implemented for equipment detection …

How Can Generative Adversarial Networks Impact Computer Generated Art? Insights From Poetry To Melody Conversion, Sakib Shahriar, Noora Al Roken

All Works

Recent advances in deep learning and generative adversarial networks (GANs), in particular, has enabled interesting applications including photorealistic image generation, image translation, and automatic caption generation. This has opened up possibilities for many cross-domain applications in computer generated arts and literature. Although there are existing software-based approaches for generating musical accompaniment of a given poetry, there are no existing implementation using GANs. This work proposes a novel poetry to melody generation conditioned on poem emotion using GANs. A dataset containing pairs of poetry and melody based on three emotion categories is introduced. Furthermore, various GAN architectures including SpecGAN and WaveGAN …

Communicating With Culture: How Humans And Machines Detect Narrative Elements, Wolfgang Victor H. Yarlott

FIU Electronic Theses and Dissertations

To understand how people communicate, we must understand how they leverage shared stories and all the knowledge, information, and associations contained within those stories. I examine three classes of narrative elements that convey a wealth of cultural knowledge: Propp's morphology, motifs, and discourse structure. Propp's morphology communicates how roles and actions drive a narrative forward; motifs fill those roles and actions with specific, remarkable events; discourse groups these into a coherent structure to convey a point.

My thesis has three aims: first, to demonstrate that people can reliably detect and identify all three of these narrative elements; second, to develop …

Volume Introduction, I. Glenn Cohen, Timo Minssen, W. Nicholson Price Ii, Christopher Robertson, Carmel Shachar

Other Publications

Medical devices have historically been less regulated than their drug and biologic counterparts. A benefit of this less demanding regulatory regime is facilitating innovation by making new devices available to consumers in a timely fashion. Nevertheless, there is increasing concern that this approach raises serious public health and safety concerns. The Institute of Medicine in 2011 published a critique of the American pathway allowing moderate-risk devices to be brought to the market through the less-rigorous 501(k) pathway,1 flagging a need for increased postmarket review and surveillance. High-profile recalls of medical devices, such as vaginal mesh products, along with reports globally …