AI Theory Research Articles

Integrating artificial intelligence into team decision‐making: Toward a theory of AI–human team effectiveness

AbstractArtificial intelligence (AI), a technological advancement radically affecting the workplace of the future, offers benefits for decision‐making processes. The most critical organizational decisions typically occur through teams. However, team theories are fundamentally psychosocial, and AI disrupts this context by introducing a non‐human actor. Therefore, negative implications of AI's role must also be understood. Toward theory on the effective integration of AI into team decision‐making, we synthesize the literatures on team effectiveness and team decision‐making with research from cognate disciplines on human–technology interaction and teaming. Based on this synthesis, we offer propositions highlighting key variable relationships and negative side effects that must be accounted for in AI–human team decision‐making and follow with practical suggestions for management's adaption to this new context. Overall, our analysis emphasizes critical themes, constructs, and relationships valuable for further research aimed at modernizing theory and practice in the face of this emerging technological shift.

AI and Blockchain Integration in Business Aviation: Securing Supply Chain and Enhancing Traceability

Purpose: The research looks at how AI converges with the blockchain technology in business aviation to better the domains considered most important: maintenance, operations, flight optimization, and observation of regulatory standards. This is aimed at understanding how these technologies bring solutions that will mitigate the specific challenges to aviation related to the security of supply chains, fraud detection, and customer experience improvement. Methodology: The research adopts a qualitative and quantitative approach, reviewing case studies from leading aviation companies such as Airbus and Boeing, alongside real-world data analysis. The integration of AI for predictive maintenance, flight optimization, and in-flight entertainment (IFE) was examined, while blockchain’s role in securing data management, regulatory compliance, and fraud prevention was assessed through documented applications and industry reports. Findings: The identification of predictive maintenance, as ensured through AI, optimizes aircraft performance, having reduced downtime. Moreover, blockchain increases supply chain transparency and security, reduces fraud, and ensures immutable records. On top of that, AI-powered flight planning and in-flight operations significantly help in operational efficiency and customer satisfaction. Moreover, blockchain helps in compliance with regulations because of its secured and traceable data; hence, it serves as a very viable tool in aviation operations. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theory of AI and blockchain integration by presenting a comprehensive framework for their application in aviation. It provides new insights into how AI and blockchain can not only enhance operational efficiency but also address fraud and compliance issues, which are pivotal for policy development. Practically, it offers aviation companies a roadmap for leveraging these technologies to secure their supply chains, optimize operations, and enhance customer experiences, ultimately advancing the entire industry.

A Review of the Latest Research Achievements in the Basic Theory of Generative AI and Artificial General Intelligence (AGI)

A Review of the Latest Research Achievements in the Basic Theory of Generative AI and Artificial General Intelligence (AGI)

Correlating Histopathological Microscopic Images of Creutzfeldt–Jakob Disease with Clinical Typology Using Graph Theory and Artificial Intelligence

Creutzfeldt–Jakob disease (CJD) is a rare, degenerative, and fatal brain disorder caused by abnormal proteins called prions. This research introduces a novel approach combining AI and graph theory to analyze histopathological microscopic images of brain tissues affected by CJD. The detection and quantification of spongiosis, characterized by the presence of vacuoles in the brain tissue, plays a crucial role in aiding the accurate diagnosis of CJD. The proposed methodology employs image processing techniques to identify these pathological features in high-resolution medical images. By developing an automatic pipeline for the detection of spongiosis, we aim to overcome some limitations of manual feature extraction. The results demonstrate that our method correctly identifies and characterize spongiosis and allows the extraction of features that will help to better understand the spongiosis patterns in different CJD patients.

Advancing financial crisis prediction: Big data, AI, and economic theories

This paper explores the theoretical foundations of financial crisis early warning systems, focusing on the integration of big data, ensemble learning, and various financial crisis theories. It begins with defining key concepts such as big data, financial crisis, and ensemble learning, highlighting the evolution and significance of these terms in the context of financial crisis management. The literature review covers extensive research on financial crisis early warning indicators and models, tracing the development from traditional statistical methods to advanced artificial intelligence approaches, including neural networks, decision trees, and machine learning algorithms. The paper critically assesses the current methodologies and emphasizes the necessity of incorporating big data and machine learning for more accurate and comprehensive early warning systems. Theoretical foundations related to financial crises, such as information asymmetry, behavioral economics, economic cycle theory, and contingency theory, are discussed to understand their impact on financial crisis prediction and management. The conclusion synthesizes the findings and suggests future research directions, emphasizing the integration of diverse methodologies and interdisciplinary approaches to enhance the early warning capabilities of enterprises.

The impacts of AI futurism: an unfiltered look at AI's true effects on the climate crisis

This paper provides an in-depth analysis of the impact of AI technologies on the climate crisis beyond their mere resource consumption. To critically examine this impact, I introduce the concept of AI futurism. With this term I capture the ideology behind AI, and argue that this ideology is inherently connected to the climate crisis. This is because AI futurism construes a socio-material environment overly fixated on AI and technological progress, to the extent that it loses sight of the existential threats ahead. In that way, the perceived significance of the planetary reality is softened, and the unsustainable paths charted by the AI industry remain opaque. To make this argument the paper unfolds in two main sections. (1) It delves into the ideology of AI futurism and its discursive impact on the societal perception of the climate crisis. (2) It employs a materialist perspective to elucidate the tangible effects of the AI industry on the climate crisis, and to show what is going on behind the façade of AI futurism. Adding to a critical theory of AI and the climate crisis, this paper starts a realistic conversation about the challenges posed by the intersection of these transformations.

Selection of a Neural Network Architecture for Implementation of Cognitive Functions of Network Software for Control of a Group of Interacting Small Space Vehicles

The purpose of the research is to substantiate and select the architecture of a neural network for the possibility of implementing the cognitive functions of network software for controlling a grouping of interacting small spacecraft.Methods are based on the concepts of AI theory for managing the grouping of small spacecraft - the use of adaptive methods and tools that allow making decisions, similar to the mechanisms of human thinking. With regard to space communication systems with a heterogeneous structure, AI methods and technologies are aimed at the processes of predicting the state in communication channels between network nodes and automatic reconfiguration of the network of devices based on the learning processes of a neural network (NN).Results. In the learning and forecasting mode, it is necessary to use time series of parameters and coordinates of specific pairs of small spacecraft with non-zero line of sight. Especially for time series analysis, recurrent neural networks (RNN) are used, in particular, LSTM. The idea of RNN operation is to use as input data for the current forecast not only the state vectors of the SVs and their coordinates, but also the previous value of the communication quality, actual or predictive. The paper shows that the onboard computing power of a separate MSC does not allow performing forecasting and training on board. Therefore, a dedicated ground segment of forecasting and monitoring is required, which will collect a posteriori information, periodically train the cognitive model, use it to predict the quality of communication, and transmit the results to the network nodes to build data transmission routes.Conclusion. The analysis of modern solutions and the choice of neural network architecture for the implementation of the cognitive functions of the network software for controlling the grouping of interacting small spacecraft showed that the neural networks of the Transformer architecture, which are based on the mechanism of internal attention, most fully meet the requirements of the project. The Transformer architecture allows using the entirety of a priori data, has a high learning and forecasting speed.

High dimensional mislabeled learning

High-dimensional mislabeled learning is essential in AI theory and applications but rarely investigated. In this study, we present a novel learning technique to detect and rectify high-dimensional mislabeled data using proposed Feature Self-Organizing Map (fSOM) along with relevant theoretical findings. When combined with the reproducible multi-class SVM learning approach, this method forms the backbone of our proposed psychiatric map (pMAP) diagnosis algorithm. This algorithm specifically addresses the real-world challenge in psychiatry of differentiating between Schizophrenia and Bipolar disorder using SNP data. The pMAP diagnosis not only offers a more precise and dependable mechanism for identifying misdiagnoses compared to state-of-the-art deep learning and machine learning models but also unveils previously unreported latent psychiatry subtypes. Furthermore, this research sheds new light on the pathology of psychiatric disorders. By mapping the evolution and internal transitions of psychiatric states and analyzing the relative entropies between various psychiatric maps, we unveil avenues to both augment and refine traditional psychiatric research. To our knowledge, this represents the inaugural study in high-dimensional mislabeled learning, poised to inspire further exploration in the domain.

Construction and Evaluation of Credit Risk Early Warning Indicator System of Internet Financial Enterprises Based On AI and Knowledge Graph Theory

Through in-depth analysis of credit risk in the context of the development of Internet finance, the study recognized that its diversification and complexity not only include financial risks, but also involve the impact of non-financial factors. Based on this, combined with ESG factors, principal component analysis and grey correlation method are used to optimize the indicators, ensuring the comprehensiveness and accuracy of the indicator system. Using Convolutional Neural Network (CNN) as the warning model, considering the dynamic and static characteristics of data, a sub convolutional network was designed for training different types of data. At the same time, the credit rating division and warning threshold selection methods were optimized, improving the accuracy and practicality of the model. Through experimental verification, we found that the CNN model has a high accuracy in credit risk warning, and it also shows excellent classification performance and comprehensive effect compared to other models. Through systematic research and application, this paper provides a complete solution for the credit risk management of Internet financial enterprises, and makes positive contributions to the stability of the financial system and risk prevention. At the same time, we also emphasize the important role of technological innovation in financial intelligent management, especially the application of innovation systems centered on AI and knowledge graphs in the field of credit management, which points out the direction for the future development of the financial industry.

How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science.

Exploratory synthesis has been the main generator of new inorganic materials for decades. However, our Edisonian and bias-prone processes of synthetic exploration alone are no longer sufficient in an age that demands rapid advances in materials development. In this work, we demonstrate an end-to-end attempt towards systematic, computer-aided discovery and laboratory synthesis of inorganic crystalline compounds as a modern alternative to purely exploratory synthesis. Our approach initializes materials discovery campaigns by autonomously mapping the synthetic feasibility of a chemical system using density functional theory with AI feedback. Following expert-driven down-selection of newly generated phases, we use solid-state synthesis and in situ characterization via hot-stage X-ray diffraction in order to realize new ternary oxide phases experimentally. We applied this strategy in six ternary transition-metal oxide chemistries previously considered well-explored, one of which culminated in the discovery of two novel phases of calcium ruthenates. Detailed characterization using room temperature X-ray powder diffraction, 4D-STEM and SQUID measurements identifies the structure and composition and confirms distinct properties, including distinct defect concentrations, of one of the new phases formed in our experimental campaigns. While the discovery of a new material guided by AI and DFT theory represents a milestone, our procedure and results also highlight a number of critical gaps in the process that can inform future efforts towards the improvement of AI-coupled methodologies.

What is critical for human-centered AI at work? - Toward an interdisciplinary theory.

Human-centered artificial intelligence (HCAI) has gained momentum in the scientific discourse but still lacks clarity. In particular, disciplinary differences regarding the scope of HCAI have become apparent and were criticized, calling for a systematic mapping of conceptualizations-especially with regard to the work context. This article compares how human factors and ergonomics (HFE), psychology, human-computer interaction (HCI), information science, and adult education view HCAI and discusses their normative, theoretical, and methodological approaches toward HCAI, as well as the implications for research and practice. It will be argued that an interdisciplinary approach is critical for developing, transferring, and implementing HCAI at work. Additionally, it will be shown that the presented disciplines are well-suited for conceptualizing HCAI and bringing it into practice since they are united in one aspect: they all place the human being in the center of their theory and research. Many critical aspects for successful HCAI, as well as minimum fields of action, were further identified, such as human capability and controllability (HFE perspective), autonomy and trust (psychology and HCI perspective), learning and teaching designs across target groups (adult education perspective), as much as information behavior and information literacy (information science perspective). As such, the article lays the ground for a theory of human-centered interdisciplinary AI, i.e., the Synergistic Human-AI Symbiosis Theory (SHAST), whose conceptual framework and founding pillars will be introduced.

Argumentation and explanation in the law.

This article investigates the conceptual connection between argumentation and explanation in the law and provides a formal account of it. To do so, the methods used are conceptual analysis from legal theory and formal argumentation from AI. The contribution and results are twofold. On the one hand, we offer a critical reconstruction of the concept of legal argument, justification, and explanation of decision-making as it has been elaborated in legal theory and, above all, in AI and law. On the other hand, we propose some definitions of explanation in the context of formal legal argumentation, showing a connection between formal justification and explanation. We also investigate the notion of stable normative explanation developed elsewhere in Defeasible Logic and extend some complexity results. Our contribution is thus mainly conceptual, and it is meant to show how notions of explanation from literature on explainable AI and legal theory can be modeled in an argumentation framework with structured arguments.

Cross layer protocol architecture for spectrum‐based routing in cognitive radio networks

AbstractNew cell phone services and apps consume more spectrum. Wireless spectrum allows services and apps to communicate with one another. Wi‐Fi quality is improved via smart spectrum usage and new CRT services. The use of spectrum is beneficial. Cross‐layer architecture improves the energy efficiency of wireless networks. System performance is improved by connecting protocol layers. Cross‐layer configuration does not introduce layer functionality into a network. By protecting networks, cross‐layer design increases communication. C‐LNRD uses self‐determined time slots to promote communication. Agents that collect information. At each level, the monitoring agent monitors traffic, time, and topology. Each layer of agents has its own database. Data is received by the network, MAC, and physical layers. Based on its measurements, each node grants trust. Routes were altered. PR ATTACK does not have RTS, CTS, or RREQ to reduce false positives. Spectrum allocation is improved via cognitive radio and learning technologies. Adaptive Cognitive Radio Networks are created using AI, GA, Fuzzy Logic, and Game Theory (ACRN). DSA creates high‐bandwidth MCRNs. This research looks at MCRNs in order to optimise spectrum usage, throughput, routing delay, and overhead. Multihop, the proposed approach by CRN takes into account spectrum awareness, quality route establishment, and route maintenance in the event that a connection fails due to spectrum or a node transfer. New strategies improve the cross‐layer network protocols of MCRN. Learners gain from spectrum models. Sensors and routers are linked by layers. The proposed routing improves both performance and spectrum use.

Application of artificial intelligence technologies in HR management

Objective. The purpose of the study is to analyze the possibility of using AI technologies to solve problems related to the selection of employees in project teams, based on production indicators and HR metrics of personnel.Method. Based on the fact that in the field of AI, methods mean algorithms by which tasks are solved, the following number of methods related to AI theory were identified: neural networks, fuzzy logic, expert systems, evolutionary modeling, Machine Learning.Result. An example of the use of AI is given in a situation where it is necessary to recruit personnel for a project based on the length of service and the degree of workload (where a scale with values from “highly loaded” to “not loaded” is used for workload). For the described example, an explanation is given that reveals the use of AI technologies (such as question-and-answer systems) in order to form HR metrics and production indicators. Additionally, the process of applying the objective function to obtain a numerical coefficient based on individual metrics or a combination of them is described in order to make a decision corresponding to its value based on the resulting indicator of the function.Conclusion. Within the framework of the conducted research, the historical patterns that led the field of personnel management to transformation and which made the use of AI technologies relevant in this area were considered. Continuous development and implementation of intelligent tools in the practice of project management facilitates HR processes and increases the efficiency of employee management. The use of AI technologies considered in the study will help to successfully monitor the state of both the project and the project team, which will have a positive impact on the productivity and profit of the enterprise.

A Generalization of the Shortest Path Problem to Graphs with Multiple Edge-Cost Estimates (Student Abstract)

The shortest path problem in graphs is a cornerstone of AI theory and applications. Existing algorithms generally ignore edge weight computation time. In this paper we present a generalized framework for weighted directed graphs, where edge weight can be computed (estimated) multiple times, at increasing accuracy and run-time expense. This raises a generalized shortest path problem that optimizes different aspects of path cost and its uncertainty. We describe in high-level a complete anytime algorithm for the generalized problem and discuss possible future extensions.

AI and self-regulated learning theory: What could be on the horizon?

AI and self-regulated learning theory: What could be on the horizon?

AWARENESS INTEGRATION THEORY CASE REPORT: THERAPUTIC INTERVENTION FOR ANXIETY AND DEPRESSION IN A TRANSEXUAL MALE COLLEGE STUDENT

One clinical psychotherapist's case report about a 19-year-old Caucasian transexual male college student with above-average intelligence is presented. Client had previously been diagnosed with generalized anxiety disorder, gender dysmorphia, and obsessive-compulsive disorder tendencies. The client sought therapeutic services due to low self-esteem that promoted feelings of awkwardness in social situations, worry that others are mad at him, and a desire for deeper relationships with friends. Therapist followed the six phases of Zeine's model of awareness integration therapy (AIT) based on AI theory. AIT has been found to successfully reduced anxiety symptoms in clients diagnosed with generalized anxiety and other disorders. AIT aims to enhance one's self-awareness about internal processes, increase self-esteem, release past traumas and psychological blocks, reduce anxiety symptoms, and regulate thoughts, emotions, attentions, intentions, and actions. Therapist's treatment plan and client's goals aligned with the six phases of AIT by: (a) increasing insight into the historical and current sources of low self-esteem; (b) decreasing the frequency of negative self-descriptive statements; (c) increasing the frequency of positive self-descriptive reports; (d) decreasing anxiety level while increasing the ability to function daily and learn; (e) implementing mindfulness exercises to reduce anxiety and worry; and (f) taking committed actions toward living a valued life. Therapist helped client access and gain awareness of the correlation between the way he perceives the world and how he relates to and behaves towards others. Client enhanced his abilities to incorporate new understanding of how he viewed the world following therapy, and made better decisions regarding himself and his personal identity. Client's scores on the posttraumatic stress disorder checklist (for DMS-5), the generalized anxiety disorder screen (GAD-7), and the patient health questionnaire (PHQ-9) decreased signicantly following 10 months of AIT. Client reported his anxiety had decreased, and he felt condent about initiating and participating in social interactions. (word count: 300)

Toward a critical social theory of AI: Knowledge, information, and intelligence in the later works of André Gorz

In his last scholarly work, L’Immatériel, André Gorz grapples with the emergence of the new cognitive capitalism based on immaterial labor and capital and, crucially, he seeks to comprehend how advanced technologies—such as nanotechnology, biotechnology, ICTs, and specifically, AI—reshape the very nature of the human subject. Despite its vital contributions, his conventional form of humanism—one that draws clear lines between organic human culture and inorganic machinic systems—is questioned and challenged by the increasingly complex and pervasive interplay between the two domains. Focusing on his later writings, this essay critically examines Gorz’s social theory of cognitive capitalism with particular reference to knowledge, information and intelligence. In doing so, the essay draws out some theoretical implications of Gorz’s defense of the humanities against post-human civilization for the development of a critical social theory of AI.

CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage Assessment

Disaster damage assessment (DDA) has emerged as an important application in disaster response and management, which aims to assess the damage severity of an affected area by leveraging AI (e.g., deep learning) techniques to examine the imagery data posted on social media during a disaster event. In this paper, we focus on a crowd-guided neural architecture searching (NAS) problem in DDA applications. Our goal is to leverage human intelligence from crowdsourcing systems to guide the discovery of the optimal neural network architecture in the design space to achieve the desirable damage assessment performance. Our work is motivated by the limitation that the deep neural network architectures in current DDA solutions are mainly designed by AI experts, which is known to be both time-consuming and error-prone. Two critical technical challenges exist in solving our problem: i) it is challenging to design a manageable NAS space for crowd-based solutions; ii) it is non-trivial to transfer the imperfect crowd knowledge to effective decisions in identifying the optimal neural network architecture of a DDA application. To address the above challenges, we develop CrowdNAS, a crowd-guided NAS framework that develops novel techniques inspired by AI, crowdsourcing, and estimation theory to address the NAS problem. The evaluation results from two real-world DDA applications show that CrowdNAS consistently outperforms the state-of-the-art AI-only, crowd-AI, and NAS baselines by achieving the highest classification accuracy in the damage assessment while maintaining a low computational cost under various evaluation scenarios.

Preface

The 2022 3rd International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation (IoTAIMA 2022) was held virtually on July 22-24, 2022 in Shanghai, China. The conference provided a communication forum for the international experts and researchers in the fields of Internet of Things, Artificial Intelligence and Mechanical Automation to share research achievements, explore solutions to research challenges, encourage international collaborations, and boost development of science and technology.In the conference, we were greatly honored to have Prof. Ke Zhang from Shanghai Institute of Technology, China to serve as our Conference Chair. 120 individuals around the world took part in the conference. Divided into three parts, the conference agenda included keynote speeches, oral presentations and online Q&A discussion. Primarily, the keynote speakers were each allocated 30-45 minutes to address their speeches. Then in the next part, oral presentations, the excellent papers we had selected were presented by their authors one by one.Four distinguished professors were invited to hold keynote speeches during the conference. Among them, Prof. Xiaodong Liu from Dalian University of Technology performed a thought-provoking speech on Knowledge Discovery and Semantic Learning in the Framework of AFS Theory. Under the background that AFS theory gains increasing interest from various research fields, this survey aimed to report the current developments of theoretical research and practical advances on AFS theory. Firstly some notions and foundations of AFS theory with an illustrative example were reviewed and then various extensions of AFS theory for knowledge discovery were analyzed. And then Prof. Yudong Zhang from University of Leicester addressed a speech: Artificial Intelligence Theories and Methods for COVID-19 Recognition. In this research, CT scan, a medical imaging technique used in radiology to get detailed images of the body noninvasively for diagnostic purposes, was applied to discuss the recent AI theories and methods in chest CT-based COVID-19 diagnosis. Two other chest-related diseases: secondary pulmonary tuberculosis and community-acquired pneumonia, were covered in the study. Their insightful speeches had triggered heated discussion. And every participant praised this conference for disseminating useful and insightful knowledge.We are glad to share with you that we received lots of submissions from the conference and we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewing them. These papers feature but are not limited to the following topics: Radio Frequency Identification, New Sensing Technology, Intelligent Perception Systems, Unmanned System Control Technology, Automation etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.We would like to acknowledge all of those who supported IoTAIMA 2022 and made it a great success. Particularly, we would like to thank the Journal of Physics: Conference Series, for all the efforts of its colleagues in publishing this paper volume. We sincerely hope that the IoTAIMA 2022 turned out to be a forum for excellent discussions that enable new ideas to come about, promoting collaborative research.Committee of IoTAIMA 2022List of Committee member is available in this pdf.