Artificial Intelligence and Robotics Commons^™

A Statistical Mechanics Approach To Reinforcement Learning, Jacob Adamczyk

Graduate Doctoral Dissertations

Reinforcement learning (RL), the study of optimal decision-making over long timescales in stochastic systems, has recently seen remarkable advances due in large part to the efforts of the deep learning community. RL has witnessed great success in solving problems in video games, robotics, biological control, and language modeling. However, a unified statistical mechanics framework to understand and develop the corresponding algorithms is lacking. To address this issue, we begin by showing that the reinforcement learning problem can be formulated and solved using the tools of statistical mechanics. Drawing on physical principles of free energy minimization and invariance, we address important …

Nonlinear System Identification Based On Fuzzy Radial Basis Neural Network With Multi-Connected Weight Connections, Kabul Khudaybergenov

Chemical Technology, Control and Management

This paper builds on our earlier radial basis function network with multiple connections (RBFMC) by placing it within a fuzzy inference framework for nonlinear system identification. The idea is inspired by the diversity of neurotransmitters found in biological neurons: instead of a single hidden-to-output weight, RBFMC gives each hidden unit a multi-dimensional connection whose components act as independent filters. Once fuzzy logic is added, each hidden neuron becomes a fuzzy rule, and its antecedent is built from several Gaussian membership functions, one per connection. The resulting Fuzzy RBFMC produces an interpretable, multi-filter description of local regions of the input space …

Ai, The Liberal Arts, And Indigenous Languages: Forming Code Into Language, Christina Graebner

Summer Research Showcase

During the Summer, Spanish Professor Adam Coon and I worked on creating an annotated biography on AI and Indigenous languages for the Digital Well at the UMN Morris Library. Through this project, we have dived into conversations and research focusing on using AI as a translator. In recent years, the conversation around AI has created a surge of studies and research around the relationship between Indigenous languages and artificial intelligence. AI will only continue to expand, and it creates new ways to open communication but creates new ethical guidelines needed to be followed. Our project gathers research articles, podcasts, and …

Artificial Intelligence And Social Equities: Navigating The Intersectionalities In A Digital Age (Editorial), Daisuke Akiba, Julie Albright

Publications and Research

This editorial article introduces and synthesizes the Special Issue, “Artificial intelligence and social equities: navigating the intersectionalities in a digital age,” which examines how AI systems intersect with race, ethnicity, and interconnected identity dimensions across global contexts. The eight contributions span healthcare, digital media, higher education, organizational communication, and speculative futures, addressing anti-racist psychiatric algorithms, AI-generated visual disinformation, epistemic injustice between the Global North and South, algorithmically mediated rural–urban divides, culturally untranslated technology transfer, accessibility auditing across the AI lifecycle, and the tension between mechanical objectivity and empathic understanding. Read together, they show that AI is neither inherently …

Nasa’S Ecostress Satellite Reveals Widespread Midday Depression In Ecosystem Evapotranspiration, Jingyi Bu, Jingfeng Xiao, Joshua B. Fisher, Yiqi Luo

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Plants often exhibit a midday depression in water use (i.e., transpiration), reflecting a constraint on their ability to sustain maximum water transport, which may occur at the cost of reduced photosynthesis. Eddy covariance observations and geostationary satellites cannot quantify this widespread phenomenon globally while resolving fine-scale spatial variability. Using evapotranspiration measurements from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and machine learning, we quantify the global distribution of midday depression in evapotranspiration. Midday depression primarily occurs during peak-growing seasons in temperate zones and dry periods in the tropics, with a morning shift of the peak evapotranspiration time …

Machine Learning For Functional Outcome Prediction After Vestibular Schwannoma Surgery: A Systematic Review And Diagnostic Test Accuracy Meta-Analysis, Shiva Nischal, Shaan Patel, Musa China, Kush Kale, Yi Hein Chai, Santosh Guru, William Muirhead, Patrick Grover

Department of Neurosurgery Faculty Papers

PURPOSE: Machine learning (ML) models have been increasingly applied to predict postoperative facial nerve dysfunction and hearing preservation after vestibular schwannoma (VS) surgery. However, reported performance varies substantially, and the overall diagnostic accuracy and clinical reliability of these models remain uncertain. We conducted a systematic review and diagnostic test accuracy meta-analysis to characterise the current state and methodological readiness of ML-based prediction of these outcomes.

METHODS: PubMed, Embase, and CENTRAL were searched from inception to February 2026. Studies evaluating ML-based prediction of facial nerve function or hearing preservation following VS surgery were included. Diagnostic performance metrics were pooled using random-effects …

Energy Security Strategy Empowered By Artificial Intelligence, Qiang Ji, Jiaofeng Pan, Yu Song

Bulletin of Chinese Academy of Sciences (Chinese Version)

Against the backdrop of unprecedented changes in a century, geopolitical restructuring has led to the fragmentation of energy game camps, climate change has impacted the resilience of energy infrastructure, and energy transformation has promoted the multidimensional and coordinated expansion of security connotations. Artificial intelligence, with its core advantages such as optimizing geopolitical risk prevention and control, enhancing infrastructure protection, improving energy system efficiency, and accelerating the integration of renewable energy, has promoted the shift of energy security strategy from experience driven to data-driven intelligence, achieving comprehensive risk identification, dynamic evaluation, collaborative response, and full chain monitoring, significantly improving the efficiency, …

Large Models Empowering Cybersecurity: Opportunities And Challenges, Zhuofeng He, Dongbin Hu, Yige Yuan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Cybersecurity serves as a critical pillar for national security and social stability. Large models in cybersecurity are emerging as key enablers for the intelligent transformation of cyber offense and defense systems. As one of the most advanced core technologies in artificial intelligence, large models are introducing new research directions and application paradigms in the cybersecurity domain. This study systematically reviews the current landscape of cybersecurity-oriented large model applications and products, and explores their deployment scenarios in practice. It further analyzes the development trends in model capabilities, industry ecosystems, and trustworthiness, while identifying major practical challenges such as data privacy protection, …

Critical Core Technology Breakthroughs In Large-Scale Models: Industrialization Strategies And Policy Implications, Zhongqi Wu, Yinshan Liu, Tao Dai, Xiaolong Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

As a pivotal direction for breakthroughs in key core technologies within the artificial intelligence domain, large-scale models hold strategic significance in securing national scientific and technological sovereignty. This study employs a multidimensional framework encompassing “technological breakthroughs, industrial transformation, and governance policies” to systematically investigate the developmental trajectories and industrialization bottlenecks of large-scale models. At the technological level, while large-scale models exhibit exponential growth in parameter scale and computing power demands, they face critical challenges including the scarcity of high-quality data, insufficient transfer learning capabilities, and reliability-explainability trade-offs. Industrially, these models are reshaping the global industrial chain landscape through a dual-track …

Evolutionary Trajectory Of Ai And Robotics Integration, Jiannan Zhu, Jianfeng Guo, Siyao Liu, Qi Cao

Bulletin of Chinese Academy of Sciences (Chinese Version)

The convergence of artificial intelligence (AI) and robotics is a cornerstone for the intelligent transformation of the physical world. This study conducts a systematic analysis of over 230,000 publications from the Web of Science and WIPO databases between 1982 and 2025 to dissect the evolutionary trajectory of AI and robotics integration since the 20th century. The findings identify four distinct developmental stages—independent exploration, functional coupling, primary intelligence, and intelligent symbiosis—while delineating the key technological breakthroughs and paradigm characteristics of each phase. Furthermore, seven core research thrusts are distilled, including motion planning and autonomous decision-making and perception & environmental understanding. An …

Trust, Delegation, And Alignment In Human-Ai Decision Making, Erik O. Kimbrough, Brennan Mcdavid, Diba Vazirian

ESI Working Papers

This paper studies delegation to artificial intelligence in a setting where human principals retain the consequences of delegated choices. Participants wrote prompts instructing ChatGPT-4o mini how to choose on their behalf in three canonical economic domains: risky choice, intertemporal choice, and social allocation. We then elicited the compensation participants required to let the AI’s choices count for payment and compared participants’ own choices to choices generated from their prompts. The design produces two central empirical objects: a revealed measure of reluctance to delegate, captured by willingness to accept compensation for AI delegation, and a behavioral measure of alignment, captured by …

The Self-Aware Room: A Framework For Operational Self-Awareness In Evolvable Blended Environments, David B. Smith

Publications and Research

The Self-Aware Room (SAR) is a room-scale research environment developed within the larger Balanced Blended Space and Blended Reality Performance System research trajectory. Rather than treating the room as a conventional “smart” environment composed of fixed automation technologies, SAR approaches it as an evolvable blended environment made from physical, virtual, conceptual, sensory, computational, and performative relationships. Its defining feature is not any particular sensor, model, or output device, but the set of transformations through which physical activity becomes structured observation, bounded representation, interpreted state, governed decision, and mediated response.

This paper develops the conceptual and methodological foundations of SAR as …

The Honored Children Of Adam: A Muslim Reading Of Magnifica Humanitas In The Age Of Artificial Intelligence, Iqbal Akhtar

The Journal of Social Encounters

No abstract provided.

Partial Coalitions And Peacemaking As Metaphor, Julie Hawke

The Journal of Social Encounters

No abstract provided.

Magnifica Humanitas, The ‘Universal Destination Of Goods,’ And Social Movements, John Sniegocki

The Journal of Social Encounters

No abstract provided.

Leo’S Choices, Katherine G. Schmidt

The Journal of Social Encounters

No abstract provided.

Magnifica Humanitas And The Obsolescence Of Just War, Noreen Herzfeld

The Journal of Social Encounters

No abstract provided.

Magnifica Humanitas For The Marginalized, Nabila Feroz Bhatti

The Journal of Social Encounters

No abstract provided.

Humanity’S Grandeur In The Balance: Theological Reflections On Magnifica Humanitas, William L. Portier

The Journal of Social Encounters

No abstract provided.

Magnifica Humanitas: Not Just About Artificial Intelligence, John Ashworth

The Journal of Social Encounters

No abstract provided.

The Fridge And The Drone: A Reflection From The Perspective Of Palestinian Liberation Theology On Magnifica Humanitas, Omar Haramy

The Journal of Social Encounters

No abstract provided.

Artificial Intelligence And The Rediscovery Of The Human: An Islamic Reflection On Magnifica Humanitas, Mazhar Ali Bari, Biliana Popova

The Journal of Social Encounters

No abstract provided.

Riyadh Charter On Artificial Intelligence For The Islamic World, - Islamic World Educational, Scientific And Cultural Organization (Icesco), - Saudi Data And Artificial Intelligence Authority (Sdaia)

The Journal of Social Encounters

The Riyadh Charter on Artificial Intelligence for the Islamic World was launched by the Islamic World Educational, Scientific and Cultural Organization (ICESCO) and the Saudi Data and Artificial Intelligence Authority (SDAIA) in September 2024, in collaboration with the Saudi National Commission for Education, Culture, and Science, during the third Global AI Summit (GAIN Summit) in Riyadh.

The Charter has been widely recognized and endorsed by Islamic scholars and policymakers. During the 26th Session of the Council of the International Islamic Fiqh Academy, it was highlighted as a moral and strategic compass for AI technologies, addressing the limitations of existing international …

Preliminary Report - Evidence-Based Assessment Of Opportunities, Risks And Impacts Of Artificial Intelligence, - United Nations Independent International Scientific Panel On Ai

The Journal of Social Encounters

No abstract provided.

Encyclical Letter "Magnifica Humanitas", - Pope Leo Xiv

The Journal of Social Encounters

No abstract provided.

Introduction: Essays On Artificial Intelligence And Magnifica Humanitas, Ron Pagnucco

The Journal of Social Encounters

No abstract provided.

Ai And The Danger Of Ontological Confusion, Derek Schuurman

University Faculty Publications and Creative Works

There is much in Jonathan Barlow’s piece that resonates with me. I appreciated how he cites some seminal thinkers like Neil Postman, Jacques Ellul, E.F. Schumacher, and Ivan Illich as he frames his argument. In particular, I think Barlow is insightful in identifying the pitfall of “commensurability” when comparing humans and AI in terms of performance. I am reminded of the words of the famous computer scientist Edsgar Dijkstra, who suggested that the question as to whether machines can think is about as relevant as the question of whether “submarines can swim.”

Ai And The Environment: Solutions For Advancing Technology Safely, Kaitlynn Baker

Discovery Day - Daytona Beach

Since 2022, the world of Artificial Intelligence (AI) has boomed. AI went from a special and rare entity to a commonly used resource available to all through web sites, and phone apps. AI has benefitted everyday activities by making office, class, and personal tasks easier through grammar help, informational citations, and as someone to bounce ideas off of. Additionally, many companies have begun utilizing AI to improve customer service and experience, and train workers more efficiently, therefore, saving thousands of dollars. Despite the benefits humans reap from its use, AI has been harming our environment at growing rates. Data centers …

Assessing Attachment To Ai: Understanding The Theoretical Correlations And Consequences, Brianna Broderick

Discovery Day - Daytona Beach

Current research on Artificial Intelligence (AI) focuses on its capabilities and our understanding of it as an instrumental tool (i.e., utility completing tasks). However, as its ability to replicate natural language improves through both text and voice, an ever-growing number of users have turned to AI for emotional companionship. Concern grows as prior research on technology dependency suggests AI bonding may lead to less interaction with others and, in extreme circumstances, has already led to cases of suicide and divorce. Kasturiaratna & Hartanto (2025) developed the AI Attachment (AIA) scale, consisting of three factors, which include: emotional closeness (i.e., personal …

High-Level Trajectory Learning For Non-Prehensile Object Manipulation With Hierarchical Reinforcement Learning, Gulsum Tuba Cibuk Girgin

Discovery Day - Daytona Beach

Site exploration requires in-situ resource utilization when the physical properties of resources are unknown. Therefore, a generalizable object manipulation method is crucial for extraterrestrial environments. Existing studies develop reinforcement learning policies that enable interaction with objects, in which quadruped robots learn to reach commanded goals with one foot while balancing with the remaining legs. However, in these studies, goal-oriented task execution relies on high-level trajectories provided by human experts, which limits autonomous robotic operations. In this study, we propose a hierarchical DRL in which a high-level pedipulation policy outputs commands for a low-level reach policy, enabling autonomous, smooth and affordable …