Artificial Intelligence and Robotics Commons^™

Context-Driven: Logic And Pathway Of Industrial Intelligence To Accelerate Development Of New Quality Productive Forces, Ximing Yin, Yaxin Su, Tailun Chen, Jin Chen, Jiang Yu

Bulletin of Chinese Academy of Sciences (Chinese Version)

The development of new productive forces hinges on industrial intelligence, which is driven by the deep integration of technological innovation and industrial innovation, and the accelerated construction of a modern industrial system. Industrial intelligence refers to the process of transforming traditional industrial development models and cultivating new pillar industries through the empowerment of intelligent technologies and data elements. It serves as a pivotal engine for promoting new industrialization and developing new productive forces. However, existing research has largely overlooked the critical theoretical and practical issues of how to leverage China’s ultra-large market and its vast contextual advantages to improve the …

Integration Of Digital And Real Economies To Shape New Advantages In Development: New Form Of Human-Cyber-Physical Ternary Fusion, Jiaofeng Pan, Jing Wu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Facing the accelerating of scientific and technological revolution and industrial transformation, expediting the deep integration of digital economy and real economy is a crucial pathway to enhance industrial competitiveness and address social development challenges. The integration of digital and real economies, with the linkage of human-cyber-physical ternary fusion, brings about an increase in production factors and a reduction in uncertainty. This integration permeates through the whole process and channels of the real economy, including R&D innovation, manufacturing, collaborative integration, and supply services, triggering systemic transformations in the real economy. Looking to the future, the deep connection of technology, data, and …

Building Engineering Ecology For Industrial Change In Digital Age, Zhengzhong Xu, Jian Chan

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the digital civilization era, scientific and technological innovation has emerged as a crucial pathway to unleash new quality productive forces and spearhead the ascendancy of great powers. It has become a vital pillar for major nations to engage in international competition and reshape the global order. Furthermore, it acts as a key instrument to smooth out economic cycles and overcome the limitations imposed by these cycles. The rise model, marked by advanced scientific and technological innovation and a commitment to scientific self-sufficiency and enhancement, significantly boosts the viability and acceptance of China’s approach to international governance. At the same …

Study On Data Mining Of Hydrogen Energy Policy In China Based On Natural Language Processing Technology, Dongling Huang, Yuan Liu, Xiaoshuai Yuan, Guozhong Jin, Yuanhang Cai, Li Liu, Heng Cao, Wanjun Li, Rui Cai

Bulletin of Chinese Academy of Sciences (Chinese Version)

Report to the 20th National Congress of the CPC emphasized the importance of “working actively and prudently towards the goals of reaching peak carbon emissions and carbon neutrality”, as well as “speeding up the planning and development of a system for new energy sources”. As a green and low-carbon secondary energy source, hydrogen energy has multiple applications in promoting the large-scale and efficient use of renewable energy as well as energy substitution in the field of transportation. It can also accelerate decarbonization in industry, and as such, is an indispensable part of building a new energy system, reaching peak carbon …

A Pathway For Digital Economy To Enable The Development Of Low-Carbon Transition Under “Technology-Organization-Environment” Framework, Wendong Wei, Yang Sun, Bei Liu, Hui Wang, Yong Geng

Bulletin of Chinese Academy of Sciences (Chinese Version)

The booming development of the digital economy has provided robust technical, management, and institutional means for the in-depth promotion of low-carbon transition. However, at present, there are still problems, such as the limited level of digital technological innovation, the insufficient supply of digital talents, and the urgent need for a sound digital governance system, which constrains the empowering effect of the digital economy on the development of China’s low-carbon transition. Based on the Technology-Organization-Environment (TOE) framework, this study discusses the technology iteration, management transform, and system optimization of the influence of digital economy on the development of low carbon transformation. …

Comparative Analysis And Insights Into R&D Mode Of Top Artificial Intelligence Companies In China And The Us, Xiyi Yang, Jia Jia, Xiaoyu Zhou, Shouyang Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is currently one of the most prominent fields in the technology industry, with China and the US being two global centers for AI research and development. However, the two countries differ in their development levels of the AI industry. In particular, the emergence of ChatGPT in 2022 has sparked extensive discussions regarding the capabilities and competitiveness of Chinese AI companies. This study analyzes over 120 000 AI invention patents approved in the past five years in both China and the US. Firstly, it constructs a multidimensional index based on AI patent features to identify the top 10 …

Reinforcement Learning For Robotic Tasks: Analyzing And Understanding The Learning Process Using Explainable Artificial Intelligence Methods, Brian J. Campana

Theses and Dissertations

As deep reinforcement learning (RL) models gain traction across more industries, there is a growing need for reliable agent-explanation techniques to understand these models. Researchers have developed explainable artificial intelligence (XAI) methods to help understand these 'black boxes'. While these models have been tested on many supervised learning tasks, there is a lack of examination of how these well these methods can explain hard reinforcement learning problems like robotic control. The sequential nature of learning RL policies and testing episodes create fundamentally different policies over time compared to more traditional supervised learning models. In this thesis, two important questions are …

Liability For Use Of Artificial Intelligence In Medicine, Nicholson W. Price Ii, Sara Gerke, I. Glenn Cohen

Book Chapters

While artificial intelligence (AI) has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving healthcare providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability. At the outset, we note a few issues that shape our analysis.

Scaling Expertise: A Note On Homophily In Online Discourse And Content Moderation, Dylan Weber

New England Journal of Public Policy

It is now empirically clear that the structure of online discourse tends toward homophily; users strongly prefer to interact with content and other users that are similar to them. I review the evidence for the ubiquity of homophily in discourse and highlight some of its worst effects including narrowed information landscape for users and increased spread of misinformation. I then discuss the current state of moderation frameworks at large social media platforms and how they are ill-equipped to deal with structural trends in discourse such as homophily. Finally, I sketch a moderation framework based on a principal of “scaling expertise” …

Henna Chatbot Capstone Review, Kobe Norcross

University Honors Theses

This thesis reviews the development of the Henna Chatbot, an AI-powered DEI consultant designed to provide personalized feedback to organizations. Sponsored by DEI consultant Arsh Haque, the project aims to address gaps in current DEI software, which often lacks team-specific feedback. The Henna Chatbot leverages GPT-3.5 Turbo to create an affordable SaaS platform where organizations can train Henna with their DEI values, and Henna will help organizations stay aligned with those values. The project spanned twenty weeks and was completed by a team of eight computer science students at Portland State University. The development process followed Agile methodologies, emphasizing effective …

Bridging Design And Perception: Novel Tools And Technologies For Creating Effective Human-Robot Interactions, Benjamin Dossett

Electronic Theses and Dissertations

This thesis explores human perception of robots through the use of novel tools and technologies. First, the impact of Augmented Reality (AR) data presentation on human perception of robots is investigated. A study conducted with the AR human-robot teaming system found that robot performance significantly influenced participants’ perceptions, overshadowing the impact of matching or mismatching robot confidence feedback. Second, the DU Want to Build-A-Bot platform is presented, which enables participatory robot design and opens the door for novel research of how robot design affects human perception. The Build-A-Bot platform enables the collection of diverse robot designs, facilitating machine learning analysis …

Data Visualization, Licensing, And Other Generative Ai Initiatives At Minnesota State University Mankato, Evan Rusch, Nat Gustafson-Sundell

Library Services Publications

At Minnesota State University Mankato (MNSU), we’ve undertaken several experiments and initiatives focused on Generative Artificial Intelligence. At the start of the fall semester, we collaborated with university Information Technology Services to present a professional development session for returning faculty through the MNSU Center for Excellence in Teaching & Learning on “5 Tips for Teaching with AI.” We also presented to librarians across the regional consortium, Minitex, on “The Library & Generative AI.” This presentation included several demonstrations. It was offered as an introduction to Generative AI focused on topics most relevant to librarians, including information literacy, as well as …

Machine Learning Multimodal Framework For Fake News Detection And Mitigation, Nada A. Gaballah

Theses and Dissertations

Social media has become our new reality, people wake up every morning and the first thing they do before getting out of bed, is check their social media. Nowadays, people rarely read newspapers, they even rarely watch TV news or listen to radio broadcasts. In recent years, we have witnessed lots of fake news roaming social media every second, with people simply believing it and spreading it even more without checking the credibility of this news. This fake news affected several domains like what happened in the US election in 2016 and again in 2020, the false information about Covid-19 …

Achieving Domain-Independent Certified Robustness Via Knowledge Continuity, Alan Wenyuan Sun

Computer Science Senior Theses

We present knowledge continuity, a novel definition inspired by Lipschitz continuity which aims to certify the robustness of neural networks across input domains (such as continuous and discrete domains in vision and language, respectively). Most existing approaches that seek to certify robustness, especially Lipschitz continuity, lie within the continuous domain with norm and distribution-dependent guarantees. In contrast, our proposed definition yields certification guarantees that depend only on the loss function and the intermediate learned metric spaces of the neural network. These bounds are independent of domain modality, norms, and distribution. We further demonstrate that the expressiveness of a model …

Artificial Intelligence As The Next Front In The Class War, Christopher Hill

Dissertations and Theses

For many years, artificial intelligence has been confined to the realm of science fiction, and while the technology has been in development, predicting the effects AI will have on our society has been a challenging endeavor. The release of ChatGPT in 2022, the subsequent mass adoption of the AI chatbot, and the response by other private firms in the field announced AI's permanent entrance into the public sphere. These recent strides made in the field of artificial intelligence reveal that the pace of technological development has outstripped the rate at which we are able to politically examine and understand these …

A Meta-Ensemble Predictive Model For The Risk Of Lung Cancer, Sideeqoh Oluwaseun Olawale-Shosanya, Olayinka Olufunmilayo Olusanya, Adeyemi Omotayo Joseph, Kabir Oluwatobi Idowu, Oyelade Babatunde Eriwa, Adedeji Oladimeji Adebare, Morufat Adebola Usman

Al-Bahir

The lungs play a vital role in supplying oxygen to every cell, filtering air to prevent harmful substances, and supporting defense mechanisms. However, they remain susceptible to the risk of diseases such as infections, inflammation, and cancer that affect the lungs. Meta-ensemble techniques are prominent methods used in machine learning to enhance the accuracy of classifier learning systems in making predictions. This work proposes a robust predictive model using a meta-ensemble method to identify high-risk individuals with lung cancer, thereby taking early action to prevent long-term problems benchmarked upon the Kaggle Machine Learning practitioners' Lung Cancer Dataset. Three machine learning …

Ai's Ethical Frontier

DePaul Magazine

Artificial intelligence (AI) is affecting every aspect of the university and society. Experts from across DePaul share their insights on artificial intelligence's advantages and pitfalls. Learn about DePaul's new Artificial Intelligence Institute and research projects that use AI for societal benefit.

Machines Of The Absurd: Leveraging Generative Ai For Creativity, Humor, And Playfulness, Tyler Sanders

College of Computing and Digital Media Dissertations

Machines of The Absurd is a collection of four projects exploring how generative AI can be leveraged for creativity, humor and playfulness.

1. neverOS — A node-based visual playground for interacting with large language models.

2. Other Calc — An iOS app with a calculator interface, where players can “calculate” text instead of numbers.

3. What Must Burn — An experiment where players type in text that can be dragged into a campfire to produce contextually appropriate sound effects.

4. Jazz vs Waffles — A turn-based comedy game, where players battle anything they type in.

Together, these projects make the …

Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco

Theses and Dissertations

Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …

Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee

Faculty Scholarship

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, …

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

Confronting Algorithms: Conscience Catching In The Criminal Trial And Beyond, Sherman J. Clark

University of Michigan Journal of Law Reform

Using the question of how to treat algorithmic evidence under the Confrontation Clause as an entry point, I argue that the use of AI in ethically salient situations presents a risk. It may cause us to avoid confronting our own responsibility. This matters because facing up to what we do, including what we delegate, can help us grow and thrive. Bearing responsibility can help us nurture vital capacities, including forms of empathy, honesty, and dignity. In the language of ethics, these are eudaimonist virtues—traits and capacities that can help us live well and fully. We should thus find ways of …

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between …

Combinatorial Creativity: Knowledge Graphs And Idea Generation In Crowdsourcing Innovation, Zhi Wei Vincent Mack

Dissertations and Theses Collection (Open Access)

This dissertation explores the dynamic interplay between combinatorial creativity and technology-driven innovation within various knowledge-intensive fields. It critically examines the role of combinatorial creativity in generating groundbreaking innovations by amalgamating existing ideas and technologies. This research incorporates a detailed examination of how knowledge, whether tacit or explicit, can be transformed into actionable data to foster innovation in crowdsourcing contexts. Chapter 2 provides an overview of the relevant literature on how Artificial Intelligence and Knowledge Management Systems can support combinatorial creativity. The study further delves into the transformative impact of knowledge management systems, particularly focusing on crowdsourcing platforms that leverage collective …

Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance …

Design And Implementation Of A Vision-Based Deep-Learning Protocol For Kinematic Feature Extraction With Application To Stroke Rehabilitation, Juan Diego Luna Inga

Master's Theses

Stroke is a leading cause of long-term disability, affecting thousands of individuals annually and significantly impairing their mobility, independence, and quality of life. Traditional methods for assessing motor impairments are often costly and invasive, creating substantial barriers to effective rehabilitation. This thesis explores the use of DeepLabCut (DLC), a deep-learning-based pose estimation tool, to extract clinically meaningful kinematic features from video data of stroke survivors with upper-extremity (UE) impairments.

To conduct this investigation, a specialized protocol was developed to tailor DLC for analyzing movements characteristic of UE impairments in stroke survivors. This protocol was validated through comparative analysis using peak …

Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson

Faculty Scholarship

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their …

Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale

Master's Theses

We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation …

Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson

Faculty Scholarship

The AI8-Point Model, derived from extensive experience in technology, AI, and higher education administration, addresses the critical need for cost-effective, high-impact strategies tailored to higher education. Despite the transformative potential of AI in enhancing student engagement, optimizing processes, and improving educational outcomes, institutions often struggle with practical implementation. The AI8-Point Model fills this gap by offering strategies that balance cost and impact. Visualized as a circle divided into four quadrants, the model encompasses phases of student engagement and institutional interaction: pre-enrollment beyond institutional control, pre-enrollment within institutional control, post-enrollment within institutional control, and post-enrollment beyond institutional control. Each quadrant contains …