Ubiquitous Artificial Intelligence Research Articles

To Become an Object Among Objects: Generative Artificial “Intelligence,” Writing, and Linguistic White Supremacy

AbstractThis paper critically explores the implications of generative artificial “intelligence” (GAI) technologies for literacy theory and practice through a case study of the author's use of OpenAI's ChatGPT. The study opens with an overview of recent literature surrounding the pedagogical implications of using GAI with a focus on issues of racial justice, outlining an abolitionist political ecology approach to literacy that extends relational theories of mediation to machine‐aided writing. The framework is then applied to data from a cognitive autoethnography of GAI use over a 6‐month period, which included a digital ethnography of ChatGPT and an extended semistructured “interview” with the GAI chatbot. Racial justice issues were found, especially linguistic and other biases. As such, soon‐to‐be ubiquitous artificial intelligence (AI) technologies require profound reconsideration of the productive value of literacy exploited by GAI, which will inevitably be pursued through an acquiescence or fundamental rupture with the dystopian visions of the technology's creators.

AI-based security functions co optimization in the SFC

Dealing with large-scale attack traffic and complex attack scenarios can be challenging for a single attack detection system in the 6G era. The ubiquitous artificial intelligence security services enabled by the AI-based Security Functions (AISF) and Service Function Chain (SFC) become strong candidate to solve this problem. However, supervised learning-based AISF requires a significant amount of manually labeled data and is unable to adapt to changing attack scenarios once it is deployed. To this end, we propose an AISF co-optimization method in the SFC. The goal is to use unlabeled network traffic samples to update the model based on pseudo-labeling and co-training. First, we model the AISF chain composed of AISFs with various detection targets and feature subspaces. We also define its detection capability evaluation metrics and final detection result. Then, we design an AISF co-optimization flow including online detection and online optimization workflows. In online optimization, we design the dynamic threshold of normalized comprehensive confidence to generate pseudo-labels for network traffic samples and combine labeled and pseudo-labeled samples to train the new models of AISFs. These models replace the previous ones and continue to detect and optimize. Experimental results in a prototype system show that compared with a single AISF, the AISF chain has higher detection capabilities that can even detect unknown attacks to a certain extent, and co-optimization can make better use of unlabeled network traffic samples than individual optimization.

The mediocrity of AI

PurposeThis paper aims to challenge the fashion of ubiquitous artificial intelligence (AI) and the effects which it will have upon society. In doing so it argues that the effects of AI will be minimal but important.Design/methodology/approachThis argument is based upon the Socratic method and explores the Utilitarian background in which AI is based while drawing upon classical literature and other examples to illustrate the argument.FindingsThe findings are encompassed in the argument and show that we need to be more open and careful when considering AI and its effects. We also need to be more realistic when considering potential benefits.Practical implicationsThis argument has significant implications for the adoption of AI.Social implicationsThe social implications are equally profound and will impact upon our application of AI solutions to current problems and upon humanity more generally.Originality/valueThis is the first paper which relates AI to human successes.

The Erosion of Competencies in Managing Innovation Projects due to the Impact of Ubiquitous Artificial Intelligence Systems

The rapid advancement of artificial intelligence (AI) systems has had a profound impact on various aspects of business and technology. One area greatly influenced by AI is innovation project management, which plays a critical role in driving organizational growth and success. The purpose of the paper examines the erosion of competencies in innovation project management as a consequence of the increasing reliance on AI systems. The object is innovation project management relying on the expertise, experience, and decision-making capabilities of human managers. However, the introduction of AI systems in project management processes has brought about significant changes. AI-powered tools and algorithms can automate tasks, analyse vast amounts of data, and provide valuable insights and recommendations. While this presents numerous benefits, it also raises concerns about the diminishing role of human competencies in managing innovation projects. AI systems require vast amounts of training data to operate effectively. In the process, they might overlook or undervalue the tacit knowledge and domain expertise possessed by human project managers. The results emphasize the need for project managers to develop new competencies, such as understanding AI capabilities, interpreting AI-generated insights, and effectively integrating AI systems into project workflows. To mitigate the erosion of competencies, organizations should invest in training programs that equip project managers with the necessary skills to work alongside AI systems. Recognizing these challenges and proactively addressing them through training, collaboration, and a balanced approach to human-AI interaction is crucial for organizations seeking to leverage AI while maintaining a high level of project management expertise.

Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities.

The sanctity of the doctor-patient relationship is deeply embedded in tradition - the Hippocratic oath, medical ethics, professional codes of conduct, and legislation - all of which are being disrupted by big data and 'artificial' intelligence (AI). The transition from paper-based records to electronic health records, wearables, mobile health applications and mobile phone data has created new opportunities to scale up data collection. Databases of unimaginable magnitude can be harnessed to develop algorithms for AI and to refine machine learning. Complex neural networks now lie at the core of ubiquitous AI systems in healthcare. A transformed healthcare environment enhanced by innovation, robotics, digital technology, and improved diagnostics and therapeutics is plagued by ethical, legal and social challenges. Global guidelines are emerging to ensure governance in AI, but many low- and middle-income countries have yet to develop context- specific frameworks. Legislation must be developed to frame liability and account for negligence due to robotics in the same way human healthcare providers are held accountable. The digital divide between high- and low-income settings is significant and has the potential to exacerbate health inequities globally.

A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models.

Terahertz (THz) band will play an important role in enabling sixth generation (6G) envisioned applications. Compared with lower frequency signals, THz waves are severely attenuated by the atmosphere temperature, pressure, and humidity. Thus, designing a THz communication system must take into account how to circumvent or diminish those issues to achieve a sufficient quality of service. Different solutions are being analyzed: intelligent communication environments, ubiquitous artificial intelligence, extensive network automation, and dynamic spectrum access, among others. This survey focuses on the benefits of integrating intelligent surfaces (ISs) and THz communication systems by providing an overview of IS in wireless communications with the scanning of the recent developments, a description of the architecture, and an explanation of the operation. The survey also covers THz channel models, differentiating them based on deterministic and statistical channel modeling. The IS-aided THz channels are elucidated at the end of the survey. Finally, discussions and research directions are given to help enrich the IS field of research and guide the reader through open issues.

AI Empowered Net-RCA for 6G

6G is envisioned to offer higher data rate, improved reliability, ubiquitous AI services, and support massive scale of connected devices. As a consequence, 6G will be much more complex than its predecessors. The growth of the system scale and complexity as well as the coexistence with the legacy networks and the diversified service requirements will inevitably incur huge maintenance cost and efforts for future 6G networks. Network Root Cause Analysis (Net-RCA) plays a critical role in identifying root causes of network faults. In this article, we first give an introduction about the envisioned 6G networks. Next, we discuss the challenges and potential solutions of 6G network operation and management, and comprehensively survey existing RCA methods. Then we propose an artificial intelligence (AI)- empowered Net-RCA framework for 6G. Performance comparisons on both synthetic and real-world network data are carried out to demonstrate that the proposed method outperforms the existing method considerably.

Large Language Models: An Emerging Technology in Accounting

ABSTRACT This commentary discusses how large language models like ChatGPT hold transformative potential in accounting, including education, research, and professional auditing. In the educational sphere, the advent of ubiquitous artificial intelligence (AI) tutors could potentially solve Bloom’s Two Sigma Problem, heralding a new era of personalized learning. Accounting research stands to benefit immensely, particularly in tasks that rely heavily on natural language processing. In the professional auditing domain, the capabilities of ChatGPT to create broad outlines of risks inherent in certain accounts and assertions can enable engagement teams to create more risk-responsive audit plans. However, although the advantages are remarkable, they are accompanied by potential pitfalls that necessitate cautious navigation. Even with these challenges, AI’s impending transformation in personal and professional lives cannot be overlooked, as accounting stands on the brink of significant change. JEL Classifications: M40; M42; O33.

Can Emotional Intelligence Increase the Positive Psychological Capital and Life Satisfaction of Chinese University Students?

The rise of artificial intelligence (AI) has led to dramatic changes in the learning environment and living conditions of college students, who face enormous psychological challenges in the ubiquitous AI environment. Modern student-management research has focused on developing mechanisms for enhancing life satisfaction, alleviating emotional anxiety, and improving self-confidence. This study aims to investigate the influence of participation in cultural and artistic activities on college students' life satisfaction. Through a questionnaire survey and data analysis of 708 college students, this study found a significant positive relationship between participation in cultural and artistic activities and college students' life satisfaction. Further mediation analysis showed that positive psychological capital played a mediating role between participation in cultural and artistic activities and life satisfaction. Additionally, emotional intelligence was found to play a moderating role in this relationship, and college students with higher emotional intelligence had a more significant effect on positive psychological capital in terms of participation in cultural and artistic activities. The results of this study herein provide a new understanding of life satisfaction research among college students and offer practical guidance for promoting college students' mental health and psychological well-being. This research also demonstrates the importance of participation in cultural and artistic activities and encourages college students to be active therein to enhance their psychological capital and improve life satisfaction. Furthermore, the cultivation and enhancement of emotional intelligence is emphasized as a key factor for college students to improve their psychological well-being through cultural and artistic activities.

AI-Bazaar: A Cloud-Edge Computing Power Trading Framework for Ubiquitous AI Services

Driven by the burgeoning growth of the Internet of Everything and the substantial breakthroughs in deep learning (DL) algorithms, a booming of artificial intelligence (AI) applications keep emerging. Meanwhile, the advance in existing computing paradigms, i.e., cloud computing and edge computing, provide assorted computing solutions to satisfy the increasingly high requirements for ubiquitous AI services. Nevertheless, there are some non-trivial issues in the computing frameworks, including the underutilization of computing power, the self-interest of computing-power trading mechanism, and the inefficiency of AI services management. To tackle the above issues, we propose a computing-power trading framework based on blockchain, also named AI-Bazaar. In AI-Bazaar, the AI consumers play multiple roles and feel free to contribute the computing power rented from the computing-power provider (CPP) for blockchain mining and AI services. Accordingly, we formulate the computing trading problem as a Stackelberg game. Based on the win or learn fast principle (WoLF), we design a profit-balanced multi-agent reinforcement learning (PB-MARL) algorithm to search the AI-Bazaar equilibrium, while finding the balanced profits for AI consumers and CPP. Numerical simulations are carried out to demonstrate the satisfactory performance and effectiveness of the proposed framework.

Optimization framework for splitting DNN inference jobs over computing networks

Ubiquitous artificial intelligence (AI) is considered one of the key services in 6G systems. AI services typically rely on deep neural network (DNN) requiring heavy computation. Hence, in order to support ubiquitous AI, it is crucial to provide a solution for offloading or distributing computational burden due to DNN, especially at end devices with limited resources. We develop an optimization framework for assigning the tasks of DNN layer computations to computing resources in the network, so as to reduce the inference latency. To this end, we propose a layered graph model with which simple conventional routing jointly solves the problem of selecting nodes for computation and paths for data transfer between nodes. We show that using our model, the existing approaches to splitting DNN layer computations can be equivalently reformulated as a routing problem that possesses better numerical properties. We also apply the proposed framework to derive algorithms for minimizing the end-to-end inference latency. We show through numerical evaluations that our new formulation can find a solution for DNN inference job distribution much faster than the existing formulation, and that our algorithms can select computing nodes and data paths adaptively to the computational attributes of given DNN inference jobs.

The Role of Optical Transport Networks in 6G and Beyond: A Vision and Call to Action

As next-generation networks begin to take shape, the necessity of Optical Transport Networks (OTNs) in helping achieve the performance requirements of future networks is evident. Future networks are characterized as being data-centric and are expected to have ubiquitous artificial intelligence integration and deployment. To this end, the efficient and timely transportation of fresh data from producer to consumer is critical. The work presented in this paper outlines the role of OTNs in future networking generations. Furthermore, key emerging OTN technologies are discussed. Additionally, the role intelligence will play in the Management and Orchestration (MANO) of next-generation OTNs is discussed. Moreover, a set of challenges and opportunities for innovation to guide the development of future OTNs is considered. Finally, a use case illustrating the impact of network dynamicity and demand uncertainty on OTN MANO decisions is presented.

AceFL: Federated Learning Accelerating in 6G-Enabled Mobile Edge Computing Networks

6G is envisioned to achieve ubiquitous Artificial Intelligence (AI) in heterogeneous and massive-scale networks, where FEderated Edge Learning (FEEL) is an effective way to assist AI technology to be implemented at the network edge. FEEL enables training a machine learning model through communication among multiple edge devices based on data from a large number of users. However, the heterogeneity among distributed edge devices and the limitation of resources may degrade the training efficiency of FEEL over 6G-enabled mobile edge computing (MEC) networks. Taking this challenge into account, a novel federated learning scheme is proposed in this paper to accelerate the training process. In particular, we formulate a training efficiency maximization problem, where a novel analytical relation among the training loss, resource consumption and heterogeneity is inferred by convergence analysis. Then, we propose a searching algorithm named IFBA to obtain the optimal inexactness of local models and frequency band allocation for edge devices in order to mitigate the straggler effect caused by the heterogeneity and resource limitation of devices. As a result, the training efficiency can be improved by adapting the inexactness of local models and frequency band allocation for edge devices on-demand. Simulation results validate that AceFL has advantages in training efficiency improvement and heterogeneity adaption, e.g., accelerating the training by <inline-formula><tex-math notation="LaTeX">$5\%$</tex-math></inline-formula> - <inline-formula><tex-math notation="LaTeX">$15\%$</tex-math></inline-formula> under different heterogeneity.

Security and Privacy in Artificial Intelligence-Enabled 6G

The sixth-generation (6G) mobile communication network is expected to provide world-connected smart and autonomous services by leveraging artificial intelligence (AI) technologies. In particular, federated learning (FL) is advocated to promote the implementations of ubiquitous AI applications in 6G, where massive distributed data is utilized to cooperatively train AI models as well as protect privacy and reduce resource consumption. However, due to the large number of interactions among mobile devices or infrastructures in FL, the AI inevitably suffers from a series of potential privacy and security risks. In this article, we investigate the security and privacy of AI-enabled 6G. Specifically, we first present the AI-enabled 6G architecture, including the space-air-ground-ocean integrated network (SAGOIN) architecture and two different FL-based AI model training frameworks in 6G. Furthermore, we discuss in detail the privacy and security threats, including cheating attack, low-quality local model training attack, and privacy stealing attack. Afterwards, we present a case study to design the security and privacy preservation scheme by integrating the trust evaluation, Q-learning based incentive, and local differential privacy (LDP) mechanism. Finally, we give out several future research directions in promising AI-enabled 6G.

Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.

Enhancing human agency through redress in Artificial Intelligence Systems.

Recently, scholars across disciplines raised ethical, legal and social concerns about the notion of human intervention, control, and oversight over Artificial Intelligence (AI) systems. This observation becomes particularly important in the age of ubiquitous computing and the increasing adoption of AI in everyday communication infrastructures. We apply Nicholas Garnham's conceptual perspective on mediation to users who are challenged both individually and societally when interacting with AI-enabled systems. One way to increase user agency are mechanisms to contest faulty or flawed AI systems and their decisions, as well as to request redress. Currently, however, users structurally lack such mechanisms, which increases risks for vulnerable communities, for instance patients interacting with AI healthcare chatbots. To empower users in AI-mediated communication processes, this article introduces the concept of active human agency. We link our concept to contestability and redress mechanism examples and explain why these are necessary to strengthen active human agency. We argue that AI policy should introduce rights for users to swiftly contest or rectify an AI-enabled decision. This right would empower individual autonomy and strengthen fundamental rights in the digital age. We conclude by identifying routes for future theoretical and empirical research on active human agency in times of ubiquitous AI.

Space-air-ground integrated network (SAGIN) for 6G: Requirements, architecture and challenges

As the fifth-generation (5G) mobile communication network may not meet the requirements of emerging technologies and applications, including ubiquitous coverage, industrial internet of things (IIoT), ubiquitous artificial intelligence (AI), digital twins (DT), etc., this paper aims to explore a novel space-air-ground integrated network (SAGIN) architecture to support these new requirements for the sixth-generation (6G) mobile communication network in a flexible, low-latency and efficient manner. Specifically, we first review the evolution of the mobile communication network, followed by the application and technology requirements of 6G. Then the current 5G non-terrestrial network (NTN) architecture in supporting the new requirements is deeply analyzed. After that, we proposes a new flexible, low-latency and flat SAGIN architecture, and presents corresponding use cases. Finally, the future research directions are discussed.

Spears and shields: attacking and defending deep model co-inference in vehicular crowdsensing networks

Vehicular CrowdSensing (VCS) network is one of the key scenarios for future 6G ubiquitous artificial intelligence. In a VCS network, vehicles are recruited for collecting urban data and performing deep model inference. Due to the limited computing power of vehicles, we deploy a device-edge co-inference paradigm to improve the inference efficiency in the VCS network. Specifically, the vehicular device and the edge server keep a part of the deep model separately, but work together to perform the inference through sharing intermediate results. Although vehicles keep the raw data locally, privacy issues still exist once attackers obtain the shared intermediate results and recover the raw data in some way. In this paper, we validate the possibility by conducting a systematic study on the privacy attack and defense in the co-inference of VCS network. The main contributions are threefold: (1) We take the road sign classification task as an example to demonstrate how an attacker reconstructs the raw data without any knowledge of deep models. (2) We propose a model-perturbation defense to defend against such attacks by injecting some random Laplace noise into the deep model. A theoretical analysis is given to show that the proposed defense mechanism achieves epsilon-differential privacy. (3) We further propose a Stackelberg game-based incentive mechanism to attract the vehicles to participate in the co-inference by compensating their privacy loss in a satisfactory way. The simulation results show that our proposed defense mechanism can significantly reduce the effects of the attacks and the proposed incentive mechanism is very effective.

Adaptive Artificial Intelligence for Resource-Constrained Connected Vehicles in Cybertwin-Driven 6G Network

The emerging technology of cybertwin is expected to bring revolutionary benefits to the sixth-generation (6G) network in respect of communication, resources allocation, and digital asset management. Empowered by ubiquitous artificial intelligence (AI), cybertwin is capable of adjusting the requests for computing resources to support network services by analyzing user’s demands for quality of experience and resource scarcity in the market. For resource-constrained applications, such as connected vehicles in the 6G network, cybertwin can intelligently determine the time-varying requests of computing resources for various vehicles at different times. However, the current service architecture executes AI algorithms with universal configurations for all vehicles. This causes the difficulty of customizing the complexity of AI algorithms to maintain adaptive to cybertwin’s decisions on dynamic resources allocation. In this article, we propose an adaptive AI framework based on efficient feature selection to cooperate with cybertwin’s resource allocation. This proposed framework can adaptively customizing AI model complexity with available computing resources. Specifically, we systematically characterize the aggregated impacts of all feature combinations on the modeling outcomes of AI algorithms. By utilizing nonadditive measures, the interactions among features can be quantified to indicate their contributions to the modeling process. Then, we propose an efficient algorithm to obtain accurate interaction measures for adaptive feature selection to balance the tradeoff between modeling accuracy and computational overhead. Finally, extensive simulations are conducted to validate that our proposed framework substantially reduces the overhead of AI algorithms while guaranteeing desired modeling accuracy for cybertwin-driven connected vehicles in 6G.

Bidirectional Green Promotion of 6G and AI: Architecture, Solutions, and Platform

The exponential growth of mobile devices has led to an explosion of network traffic in various fields, and application services have also emerged bloomingly in the Internet of Everything era. However, existing networks are not sufficiently intelligent to support rapid dynamic changes in topologies. Simultaneously, it is challenging to coordinate end-edge-cloud communication and computing. and thus green, energy-efficient, and sustainable development cannot be achieved. As a result, experts and scholars have paid attention to the future 6th generation (6G) technology. In response to the above challenges, this article explores artificial intelligence (AI) methods to integrate with 6G to realize the two-way promotion of AI and 6G. Ubiquitous AI will take root in 6G. Likewise, 6G will support new AI applications. We propose a green architecture for air-space-ground-sea integrated networks, oriented to all scenarios. Then, to integrate communication and computing, we put forward green solutions for network perception of radio map and traffic map to advance AI technologies and realize green fusion efficiently. Second, we propose a green wireless management platform based on KubeEdge Wireless, which containerizes nodes and expands the wireless connections to realize joint scheduling and orchestration of resources. Finally, we outline some open research topics and potential solutions/technologies for the two-way promotion of AI and 6G in the future.