Collaborative AI Research Articles

Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives

This research examines how artificial intelligence, human capabilities, and task types influence organizational outcomes. By leveraging the frameworks of the Resource-Based View and Task Technology Fit theories, we executed two distinct studies to assess the effectiveness of a generative AI tool in aiding task performance across a spectrum of task complexities and creative demands. The initial study tested the utility of generative AI across diverse tasks and the significance of AI-related skills enhancement. The subsequent study explored interactions between humans and AI, analyzing emotional tone, sentence structure, and word choice. Our results indicate that incorporating AI can significantly improve organizational task performance in areas such as automation, support, creative endeavors, and innovation processes. We also observed that generative AI generally presents more positive sentiment, utilizes simpler language, and has a narrower vocabulary than human counterparts. These insights contribute to a broader understanding of AI's strengths and weaknesses in organizational settings and guide the strategic implementation of AI systems.

CycleGAN*: Collaborative AI Learning With Improved Adversarial Neural Networks for Multimodalities Data

CycleGAN*: Collaborative AI Learning With Improved Adversarial Neural Networks for Multimodalities Data

Knowledge Coupling, Collaborative Innovation Network and AI Technological Generality

Knowledge Coupling, Collaborative Innovation Network and AI Technological Generality

LifeGuardAI-Artificial Intelligence for Predicting Mortality Due to Sepsis

The LifeGuardAI project is a groundbreaking initiative that aims to utilize artificial intelligence to predict mortality rates associated with sepsis. The project utilizes Multilayer Perceptron (MLP) models and collaborative AI development techniques to provide healthcare professionals with advanced, AI-driven insights for preemptive intervention, ultimately enhancing patient- centered care. The project framework involves a comprehensive approach that begins with defining the problem statement focused on leveraging AI to improve sepsis-related outcomes. The dataset for this project is sourced from the Kaggle Prediction of Sepsis dataset, which contains crucial information related to patient health, such as vital signs, laboratory values, and demographic information

AI‐CARING: National AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups

AbstractOver 13 million Americans aged 65 and older are currently living with a diagnosis of mild cognitive impairment (MCI), a common precursor to dementia. These individuals largely rely on a network of informal caregivers—family, friends, and community members—who work together with professional healthcare and social service providers to provide care and support in home settings. The AI‐CARING Institute contributes foundational AI research focused on developing personalized collaborative AI systems that improve the quality of life and independence of aging adults living at home.

Collaborative AI in the Geometry Friends Game Competition

Collaborative AI in the Geometry Friends Game Competition

Collaborative AI & immersive VR simulation for workplace layout optimization in manufacturing applications

Workplace layouts significantly affect operator well-being and productivity in manufacturing. Traditional methods often neglect to incorporate operator feedback until expensive prototypes are built, leading to costly redesigns. This paper presents an approach that leverages collaborative AI and immersive virtual reality (VR) simulation for early engagement of relevant stakeholders. The proposed approach employs Artificial Intelligence algorithms, particularly genetic algorithms, to recommend layouts to minimize travel distance. The approach considers spatial constraints, workstation characteristics, and task sequences using an A-based pathfinding algorithm. This algorithm estimates the fitness score of each investigated layout plan while ensuring that collision-free operator paths, incorporating travelling to all the required workstations, are evaluated each time. These layouts are evaluated by humans through VR task simulations. In the VR environment, factors such as accessibility, reachability, posture, and occlusions are assessed alongside travel distance. Intuitive and informed layout updates are enabled within VR. This approach was tested in two use cases inspired by in-situ repairing of composite materials and post-processing of additive manufacturing parts. Results demonstrated increased design efficiency, allowing for a comprehensive exploration of alternatives while incorporating end-user feedback. Finally, informed decision-making improved user satisfaction and reduced stress during design.

Securing Collaborative Medical AI by Using Differential Privacy: Domain Transfer for Classification of Chest Radiographs

Securing Collaborative Medical AI by Using Differential Privacy: Domain Transfer for Classification of Chest Radiographs

Collaborative AI Enabled Place Branding: A Hybrid-Narrative Review and Prospects for Future Research

Collaborative AI Enabled Place Branding: A Hybrid-Narrative Review and Prospects for Future Research

Privacy-preserving collaborative AI for distributed deep learning with cross-sectional data

Privacy-preserving collaborative AI for distributed deep learning with cross-sectional data

Guest Editorial Trustworthy and Collaborative AI for Personalised Healthcare Through Edge-of-Things

Guest Editorial Trustworthy and Collaborative AI for Personalised Healthcare Through Edge-of-Things

A Scoping Review of Collaborative Information Behavior

Many researchers have mischaracterized collaborative information behavior (CIB) with its sub-processes, including collaborative information seeking (CIS) and collaborative information retrieval (CIR). To learn about the most prominent CIB definitions, the phases involved in the CIB process, and the various collaboration tools that facilitate CIB, we sifted through a wide range of LIS research papers. Using a scoping review to correspond with the research questions, we conducted several iterations to exclude irrelevant studies while concurrently including pertinent ones. The “C5 model of collaboration” developed by Shah provides a framework for coding our data during content analysis and concept mapping. Our findings indicate that CIB has no generally adopted definition. Additionally, traditional workplace tools and technologies are the collaborative tools utilized for various collaborative activities. More CIB research is needed in the information science field to stay ahead of the rapid growth of collaborative AI technologies, particularly those that foster human-AI collaborations.

Utilization of Collaborative Robot and AI at Manufacturing Process

Utilization of Collaborative Robot and AI at Manufacturing Process

AI-Assisted Enhancement of Student Presentation Skills: Challenges and Opportunities

Oral presentation is a popular type of assessment in undergraduate degree programs. However, presentation delivery and grading pose considerable challenges to students and faculty alike. For the former, many students who learn English as an additional language may fear giving oral presentations in English due to a lack of confidence. For the latter, faculty who teach multiple classes and have many students may find it difficult to spend adequate time helping students refine their communication skills. This study examines an AI-assisted presentation platform that was built to offer students more opportunities for presentation training without the need for faculty intervention. Surveys with students and teachers were conducted to inform the design of the platform. After a preliminary platform was developed, two methods were employed to evaluate its reliability: a beta test with 24 students and a comparison of AI and human scoring of the presentation performance of 36 students. It was found that students are highly receptive to the platform, but there are noticeable differences between AI and human scoring abilities. The results reveal some limitations of AI and human raters, and emphasize the potential benefit of exploring collaborative AI–human intelligence.

Writing Futures: Collaborative, Algorithmic, Autonomous: Ann Hill Duin and Isabel Pedersen: [Book Review

The authors of the book present a broad survey of technologies and applications of AI as they particularly impact technical and professional communications. This book presents a broad treatment of its subjects, particularly given the framework of social implications, necessary literacy, and civic engagement that the authors use to explore the three facets of writing futures: collaboration, algorithms, and autonomous agents. The work is a survey of many technologies, applications, and developments, any of which may or may not play a substantial future role in the future of writing. Some of the authors’ examples may seem tangential to the TPC profession, but one cannot always predict future effects. The authors situate the book as “positioning scholars, instructors, and practitioners to plan for rapidly evolving technological and social contexts.” With its broad coverage of emerging technologies, rich citations, and wealth of backing resources, Writing Futures provides a launching point for deeper, focused study in the myriad areas of collaborative technologies, autonomous agents, and AI as they profoundly impact the TPC profession and the human experience.

Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery

Materials scientists are facing increasingly challenging multi-objective performance requirements to meet the needs of modern systems such as lighter-weight and more fuel-efficient aircraft engines, and higher heat and oxidation-resistant steam turbines. While so-called second wave statistical machine learning techniques are beginning to accelerate the materials development cycle, most materials science applications are data-deprived when compared to the vastness and complexity of the search space of possible solutions. In line with DARPA’s vision of third wave AI approaches, we believe a combination of data-driven statistical machine learning and domain knowledge will be required to achieve a true revolution in materials discovery. To that end, we envision and have begun reducing to practice a system that fuses three forms of knowledge—factual scientific knowledge, physics-based and/or data-driven analytical models, and domain expert knowledge—into a single ‘Compound Knowledge Graph’ in which contextual reasoning and adaptation can be performed to answer increasingly complex questions. We believe this Compound Knowledge Graph-based system can be the nucleus of a collaborative AI assistant that supports stateful natural language back-and-forth dialogs between materials scientists and the AI to accelerate the development and discovery of new materials. This paper details our vision, summarizes our progress to date on a steam turbine blade coating use case, and outlines our thoughts on the key challenges in making this vision a reality.

Blockchain-Governed Federated Transfer Learning for Secure Internet of Drones Networks

Internet of Drones (IoD) is a critical paradigm of future networks used for complex cooperative tasks. Due to its heterogeneity, high mobility, and exposed connectivity, it inevitably suffers from the crucial security threats of nodes failure, malicious tampering, and anonymous attacks. Specifically, federated transfer learning (FTL) and blockchain are seen as promising technologies for network security. FTL utilizes the distinctive transfer of prior knowledge to facilitate security applications in various networks. It strengthens privacy protection and the resilience of AI, which will be crucial in more complicated circumstances. Blockchain (BC) technology attracts attention due to its inherent characteristics of security authentication and tamper-resistant storage. In this article, the superiority of FTL and BC are intergraded to overcome the cyber-security concerns associated with IoD networks. We propose FTLchain, a blockchain-empowered FTL architecture. To significantly boost the cyber-security of collaborative AI while preserving privacy, we empower the architecture with lightweight safety technology and detail the relationship between the critical components in the security design. Then, we devise a hierarchical consensus mechanism integrating the FTL aggregation with the blockchain consensus to ensure the architecture's scalability. The FTLchain's robustness is explained and proved with numerical simulations.

Collaborative Artificial Intelligence Needs Stronger Assurances Driven by Risks

Collaborative AI systems (CAISs) aim at working together with humans in a shared space to achieve a common goal. This critical setting yields hazardous circumstances that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements, domain-specific standards and regulations is of greatest importance. Only few scale impact has been reported so far for such systems since much work remains to manage possible risks. We identify emerging problems in this context and then we report our vision, as well as the progress of our multidisciplinary research team composed of software/systems, and mechatronics engineers to develop a risk-driven assurance process for CAISs.

A Game AI Competition to Foster Collaborative AI Research and Development

Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A competition is also a way to motivate new researchers to study these challenges. In this document, we present the Geometry Friends Game AI Competition. Geometry Friends is a two-player cooperative physics-based puzzle platformer computer game. The concept of the game is simple, though its solving has proven to be difficult. While the main and apparent focus of the game is cooperation, it also relies on other AI-related problems such as planning, plan execution, and motion control, all connected to situational awareness. All of these must be solved in real-time. In this paper, we discuss the competition and the challenges it brings, and present an overview of the current solutions.

Effects of Deep Neural Networks on the Perceived Creative Autonomy of a Generative Musical System

Collaborative AI agents allow for human-computer collaboration in interactive software. In creative spaces such as musical performance, they are able to exhibit creative autonomy through independent actions and decision-making. These systems, called co-creative systems, autonomously control some aspects of the creative process while a human musician manages others. When users perceive a co-creative system to be more autonomous, they may be willing to cede more creative control to it, leading to an experience that users may find more expressive and engaging. This paper describes the design and implementation of a co-creative musical system that captures gestural motion and uses that motion to filter pre-existing audio content. The system hosts two neural network architectures, enabling comparison of their use as a collaborative musical agent. This paper also presents a preliminary study in which subjects recorded short musical performances using this software while alternating between deep and shallow models. The analysis includes a comparison of users' perceptions of the two models' creative roles and the models' impact on the subjects' sense of self-expression.