Search

Implantable bioelectronics hold tremendous potential in the field of healthcare, yet the performance of these systems heavily relies on the interfaces between artificial machines and living tissues. In this paper, we discuss the recent developments of tethered interfaces, as well as those of non-tethered interfaces. Among them, systems that study neural activity receive significant attention due to their innovative developments and high relevance in contemporary research, but other functional types of interface systems are also explored to provide a comprehensive overview of the field. We also analyze the key considerations, including perforation site selection, fixing strategies, long-term retention, and wireless communication, highlighting the challenges and opportunities with stable, effective, and biocompatible interfaces. Furthermore, we propose a primitive model of biocompatible electrical and optical interfaces for implantable systems, which simultaneously possesses biocompatibility, stability, and convenience. Finally, we point out the future directions of interfacing strategies. [ABSTRACT FROM AUTHOR]

Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the results of 12 citizen science projects that used machine learning to optimize classification tasks. We examined the distribution of tasks between citizen scientists, experts, and algorithms, and how epistemic agency was enacted in terms of whose knowledge shapes the distribution of tasks, who decides what knowledge is relevant to the classification, and who validates it. In our descriptive results, we found that experts, who include professional scientists and algorithm developers, are involved in every aspect of a project, from annotating or labelling data to giving data to algorithms to train them to make decisions from predictions. Experts also test and validate models to improve their accuracy by scoring their outputs when algorithms fail to make correct decisions. Experts are mostly the humans involved in a loop, but when algorithms encounter problems, citizens are also involved at several stages. In this paper, we present three main examples of citizens-in-the-loop: (a) when algorithms provide incorrect suggestions; (b) when algorithms fail to know how to perform classification; and (c) when algorithms pose queries. We consider the implications of the emphasis on optimization on the ideal of science and the role of citizen scientists from a perspective informed by Science and Technology Studies (STS) and Information Systems (IS). Based on our findings, we conclude that ML in CS classification projects, far from being deterministic in its nature and effects, may be open to question. There is no guarantee that these technologies can replace citizen scientists, nor any guarantee that they can provide citizens with opportunities for more interesting tasks. [ABSTRACT FROM AUTHOR]

Accepted by: Aris Syntetos Launched in a post pandemic world primed for technological solutions, Gen AI marked the beginning of a transformational period. Uses, norms, standards and values embedded in artificial intelligence (AI) technology are evolving at a dizzying pace and are in flux. The current period—termed 'the Inter-AI Period'—presents an opportunity for researchers in management mathematics to shape an AI-enabled future. This window of opportunity will be short, after which mathematical principles and decision processes embedded in AI algorithms will harden. Technological evolution will certainly continue thereafter. But the views, norms, practices and strategies would have been embedded in the technology. It is up to researchers to act now to shape an AI-enabled future—one that harnesses the powers of AI but with guardrails to protect users and humanity. This invited essay is based on research involving interviews of dozens of business leaders and scholars and is intended as a call to action for researchers to grasp the gravity of this period and act now. [ABSTRACT FROM AUTHOR]

Implantable bioelectronics hold tremendous potential in the field of healthcare, yet the performance of these systems heavily relies on the interfaces between artificial machines and living tissues. In this paper, we discuss the recent developments of tethered interfaces, as well as those of non-tethered interfaces. Among them, systems that study neural activity receive significant attention due to their innovative developments and high relevance in contemporary research, but other functional types of interface systems are also explored to provide a comprehensive overview of the field. We also analyze the key considerations, including perforation site selection, fixing strategies, long-term retention, and wireless communication, highlighting the challenges and opportunities with stable, effective, and biocompatible interfaces. Furthermore, we propose a primitive model of biocompatible electrical and optical interfaces for implantable systems, which simultaneously possesses biocompatibility, stability, and convenience. Finally, we point out the future directions of interfacing strategies.

The integration of AI technology with digital transformation has profoundly shaped the evolution towards digital triplet architecture, grounded in human-centric methodologies. By infusing human intellectual activities into both physical and cyberspace, innovative links between humans and machines are established. Despite limitations in transitioning from tangible human presence to the digital realm in cyberspace, extensive efforts are underway to harness emotional, visual, and oral responses, thereby enhancing the reasoning and predictive capabilities of digital twins. These advancements aim to elevate real-time human interactions with physical and virtual systems by integrating intelligent AI algorithms and cognitive computing systems into digital twins. This paper meticulously analyses recent trends in digital twins, tracing their evolution from traditional concepts and applications to a nuanced digital triplet hierarchy that incorporates human intuition, knowledge, and creativity within cyberspace. we delve into the hierarchical framework of the digital triplet, resonating with maturity, domination, and volition levels, enhances cognitive and perceptual capabilities in cyberspace. The study provides a systematic overview of the development of ultra-realistic digital models, incorporating real-time data-driven artefacts that integrate intelligent activities with multidomain, multiphysics, and multiscale simulations. The research scope is focused on augmenting the perceptive and heuristic capabilities of the digital triplet framework by utilizing AI in data analytics, retrieving heterogeneous data from virtual entities using semantic artificial intelligence technologies, and amalgamating AI and machine learning with human insight and perceptual knowledge. The proposed digital triplet hierarchy aims to enhance cyberspace's capacity for learning, cognitive skills, and knowledge transfer. It can be a guideline for the researcher to promote cognitive augmentation of the human brain through brain-machine/computer interface, virtual, augmented, and extended reality, fostering a symbiotic relationship between humans and machines in the industrial metaverse and industry 5.0. The paper discusses future directions for research and the challenges involved in developing intelligent digital twins towards the digital triplet paradigm, aiming to embody intelligent activities and cognitive capabilities within the framework of human–machine symbiosis.

Artificial intelligence is making extraordinary progress with an unprecedented rate, reaching and surpassing human capabilities in many tasks previously considered unattainable by machines, as language translation, music composition, object detection, medical diagnoses, software programming, and many others. Some people are excited about these results, while others are raising serious concerns for possible negative impacts in our society. This article addresses several questions that are often raised about intelligent machines: Will machines ever surpass human intellectual capacities? What will happen next? What will be the impact in our society? What are the jobs that artificial intelligence puts at risk? Reasoning about these questions is of fundamental importance to predict possible future scenarios and prepare ourselves to face the consequences.

Recent years have witnessed a rapidly-growing research agenda that explores the combined, integrated, and collective intelligence of humans and machines working together as a team. This paper contributes to the same line of research with three main objectives: a) to introduce the concept of the SMV (Symbols-Meaning-Value) space for describing, understanding, and representing human/machine perception, cognition, and action, b) to revisit the notion of human-machine symbiosis, and c) to outline a conceptual framework of human-machine co-intelligence (i.e., the third intelligence) through human-machine symbiosis in the SMV space. By following the principle of three-way decision as thinking in threes, triads of three things are used for building an easy-to-understand, simple-to-remember, and practical-to-use framework. The three elements of the SMV space, namely, Symbols, Meaning, and Value, are closely related to the three basic human/machine functions of perception, cognition, and action, which can be metaphorically described as the seeing-knowing-doing triad or concretely interpreted as the data-knowledge-wisdom (DKW) hierarchy. Human-machine co-intelligence emerges from human-machine symbiosis in the SMV space. As the third intelligence, human-machine co-intelligence relies on and combines human intelligence and machine intelligence, is a higher level of intelligence above either human intelligence or machine intelligence alone, and is greater than the sum of human intelligence and machine intelligence. There are three basic principles of human-machine symbiosis, i.e., unified oneness, division of labor, and coevolution, for nurturing human-machine co-intelligence. [ABSTRACT FROM AUTHOR]

Copyright of Refractories / Naihuo Cailiao is the property of Naihuo Cailia (Refractories) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research projects that actively involve 'crowds' or non-professional 'citizen scientists' are attracting growing attention. Such projects promise to increase scientific productivity while also connecting science with the general public. We make three contributions. First, we argue that the largely separate literatures on 'Crowd Science' and 'Citizen Science' investigate strongly overlapping sets of projects but take different disciplinary lenses. Closer integration can enrich research on Crowd and Citizen Science (CS). Second, we propose a framework to profile projects with respect to four types of crowd contributions: activities, knowledge, resources, and decisions. This framework also accommodates machines and algorithms, which increasingly complement or replace professional and non-professional researchers as a third actor. Finally, we outline a research agenda anchored on important underlying organisational challenges of CS projects. This agenda can advance our understanding of Crowd and Citizen Science, yield practical recommendations for project design, and contribute to the broader organisational literature. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While full automation of road vehicles remains a future goal, shared-control and semiautonomous driving—involving transitions of control between the human and the machine—are more feasible objectives in the near term. These alternative driving modes will benefit from new research toward novel steering control devices, more suitably where machine intelligence only partially controls the vehicle. In this article, it is proposed that when the human shares the control of a vehicle with an autonomous or semiautonomous system, a force control, or nondisplacement steering wheel (i.e., a steering wheel which does not rotate but detects the applied torque by the human driver) can be advantageous under certain schemes: tight rein or loose rein modes according to the $H$ -metaphor. We support this proposition with the first experiments to the best of our knowledge, in which human participants drove in a simulated road scene with a force control steering wheel (FCSW). The experiments exhibited that humans can adapt promptly to force control steering and are able to control the vehicle smoothly. Different transfer functions are tested, which translate the applied torque at the FCSW to the steering angle at the wheels of the vehicle; it is shown that fractional order transfer functions increment steering stability and control accuracy when using a force control device. The transition of control experiments is also performed with both: a conventional and an FCSW. This prototypical steering system can be realized via steer-by-wire controls, which are already incorporated in commercially available vehicles. [ABSTRACT FROM AUTHOR]

The integration of AI technology with digital transformation has profoundly shaped the evolution towards digital triplet architecture, grounded in human-centric methodologies. By infusing human intellectual activities into both physical and cyberspace, innovative links between humans and machines are established. Despite limitations in transitioning from tangible human presence to the digital realm in cyberspace, extensive efforts are underway to harness emotional, visual, and oral responses, thereby enhancing the reasoning and predictive capabilities of digital twins. These advancements aim to elevate real-time human interactions with physical and virtual systems by integrating intelligent AI algorithms and cognitive computing systems into digital twins. This paper meticulously analyses recent trends in digital twins, tracing their evolution from traditional concepts and applications to a nuanced digital triplet hierarchy that incorporates human intuition, knowledge, and creativity within cyberspace. we delve into the hierarchical framework of the digital triplet, resonating with maturity, domination, and volition levels, enhances cognitive and perceptual capabilities in cyberspace. The study provides a systematic overview of the development of ultra-realistic digital models, incorporating real-time data-driven artefacts that integrate intelligent activities with multidomain, multiphysics, and multiscale simulations. The research scope is focused on augmenting the perceptive and heuristic capabilities of the digital triplet framework by utilizing AI in data analytics, retrieving heterogeneous data from virtual entities using semantic artificial intelligence technologies, and amalgamating AI and machine learning with human insight and perceptual knowledge. The proposed digital triplet hierarchy aims to enhance cyberspace's capacity for learning, cognitive skills, and knowledge transfer. It can be a guideline for the researcher to promote cognitive augmentation of the human brain through brain-machine/computer interface, virtual, augmented, and extended reality, fostering a symbiotic relationship between humans and machines in the industrial metaverse and industry 5.0. The paper discusses future directions for research and the challenges involved in developing intelligent digital twins towards the digital triplet paradigm, aiming to embody intelligent activities and cognitive capabilities within the framework of human–machine symbiosis. [ABSTRACT FROM AUTHOR]

The relationship between humans and technology has attracted increasing attention with the advent of ever stronger models of artificial intelligence. Humans and technology are intertwined within multiple autopoietic loops of stress, care, and intelligence. This paper suggests that technology should not be seen as a mere tool serving humans' needs, but rather as a partner in a rich relationship with humans. Our model for understanding autopoietic systems applies equally to biological, technological, and hybrid systems. Regardless of their substrates, all intelligent agents can be understood as needing to respond to a perceived mismatch between what is and what should be. We take this observation, which is evidence of intrinsic links between ontology and ethics, as the basis for proposing a stress-care-intelligence feedback loop (SCI loop for short). We note that the SCI loop provides a perspective on agency that does not require recourse to explanatorily burdensome notions of permanent and singular essences. SCI loops can be seen as individuals only by virtue of their dynamics, and are thus intrinsically integrative and transformational. We begin by considering the transition from poiesis to autopoiesis in Heidegger and the subsequent enactivist tradition, and on this basis formulate and explain the SCI loop. In an acknowledgment of Maturana's and Varela's project, our findings are considered against the backdrop of a classic Buddhist model for the cultivation of intelligence, known as the bodhisattva. We conclude by noting that SCI loops of human and technological agency can be seen as mutually integrative by noticing the stress-transfers between them. The loop framework thus acknowledges encounters and interactions between humans and technology in a way that does not relegate one to the subservience of the other (neither in ontological nor in ethical terms), suggesting instead integration and mutual respect as the default for their engagements. Moreover, an acknowledgment of diverse, multiscale embodiments of intelligence suggests an expansive model of ethics not bound by artificial, limited criteria based on privileged composition or history of an agent. The implications for our journey into the future appear numerous. [ABSTRACT FROM AUTHOR]

Artificial intelligence is making extraordinary progress with an unprecedented rate, reaching and surpassing human capabilities in many tasks previously considered unattainable by machines, as language translation, music composition, object detection, medical diagnoses, software programming, and many others. Some people are excited about these results, while others are raising serious concerns for possible negative impacts in our society. This article addresses several questions that are often raised about intelligent machines: Will machines ever surpass human intellectual capacities? What will happen next? What will be the impact in our society? What are the jobs that artificial intelligence puts at risk? Reasoning about these questions is of fundamental importance to predict possible future scenarios and prepare ourselves to face the consequences., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Buttazzo.)

This article defines a category of human-robot interaction in which human and robotic actors work as a single unified system. We survey work from various fields including human augmentation systems such as extra fingers and arms, and other robots that operate in close proximity to the user. The discussed works highlight a close interplay between human and robotic actions where control decisions are made by both actors. Such a dyadic configuration can yield a synergistic outcome but require that close attention be paid to the coordination between them. Using case studies from our own work, we discuss two main questions that must be addressed when designing such closely collaborative human-robot integrations: type of support and degree of control. The different choices that can be adopted for each of these design questions define a framework or classification that is useful for surveying existing and future research. [ABSTRACT FROM AUTHOR]

Industry 4.0 technologies are reconfiguring energy value chains and changing how we understand efficiency in energy production. Using Scopus, this review identifies research gaps in Industry 4.0 technologies in the energy sectors of Germany, China and South Africa, by technology type, effect and impact. Globally, research on the energy sector is skewed toward the Internet of things, Artificial Intelligence, and Big, Real-Time Data. Publications on Machine-Human-Integration and Robotics are limited. China outnumbers Germany in research publications and, South Africa produces the least, leading to the assumption is that such technologies are missing in energy production in South Africa. The literature presents clear content foci: i. using Industry 4.0 technologies to reduce energy intensity through better process-related materials, thereby shrinking the carbon footprint; ii. growing efficiencies through smart energy systems; iii. rethinking energy policy to incentivise research in Industry 4.0 technologies, and iv. managing network securities in within energy systems. Firm-specific, empirical research on South Africa, although valuable, is scarce regarding if, where and how Industry 4.0 technologies affect energy production, increased plant productivity and revenue. The review encourages implementing firm-based, empirical studies, using actual data on Industry 4.0 technologies in energy production in South Africa. Such studies provide improved understanding of a South African energy sector facing a Fourth Industrial Revolution and in crisis, identifying how Industry 4.0 technologies may be leveraged to increase energy productivity and efficiency. Such studies will contribute to evidence-based policy on expanding Industry 4.0 technologies to create an efficient and sustainable South African energy sector. • Research on energy sector technologies is skewed towards the Internet of things, Artificial Intelligence and Big, Real-Time Data. • Research gaps exist for Robotics and Human-Machine Integration in energy production. • China leads in publication numbers for Industry 4.0 technologies in energy production. • No publications emerged on how Industry 4.0 technologies impact energy production in South Africa. • There are gaps in firm-level data on how Industry 4.0 technologies affect energy production. [ABSTRACT FROM AUTHOR]

This paper reviews the state-of-the-art methodologies for automating design with intelligent human–machine integration from the perspectives of ontology and epistemology. The human–machine integrated automating design paradigm is reviewed systematically based on a proposed prototype of human–machine integrated design, from the aspects of ontology-based knowledge management with local-to-global ontology transitions, and epistemology-based upward-spiral cognitive process of coupled design ideation. Particularly, imaginal thinking frame is proposed as the foundation of intelligent human–machine interaction that puts human and machine on an equal platform. Further, this paper presents implementations and applications of the automating design paradigm and concludes with the identification of future trend. [ABSTRACT FROM AUTHOR]

Human errors cause the majority of aviation accidents. Augmented cognition and automation systems enhance pilot performance by evaluating system limitations and flight precision and performance. This study examines the human-machine interface in cockpit design using the tenets of augmented cognition and automation systems theory in terms of task allocation, attentional resources, and situational awareness. The study compares how these principles apply to and interact with each other and with a human/pilot in a closed-loop system. We present a method for integrating augmented cognition systems into airplane flight management systems. We demonstrate systems enhancement with an experiment in which test pilots flew two simulated flights, once without and once with an augmented cognition system. We measured pilot and airplane performance, pilots' situational awareness, workload management, pilots' use of cockpit checklists, and flight precision along four axes: (1) altitude, (2) course, (3) radial/bearing and heading, and (4) airspeed. [ABSTRACT FROM AUTHOR]

This article provides a general ergonomic framework of cooperative guidance and control for vehicles with an emphasis on the cooperation between a human and a highly automated vehicle. In the twenty-first century, mobility and automation technologies are increasingly fused. In the sky, highly automated aircraft are flying with a high safety record. On the ground, a variety of driver assistance systems are being developed, and highly automated vehicles with increasingly autonomous capabilities are becoming possible. Human-centred automation has paved the way for a better cooperation between automation and humans. How can these highly automated systems be structured so that they can be easily understood, how will they cooperate with the human? The presented research was conducted using the methods of iterative build-up and refinement of framework by triangulation, i.e. by instantiating and testing the framework with at least two derived concepts and prototypes. This article sketches a general, conceptual ergonomic framework of cooperative guidance and control of highly automated vehicles, two concepts derived from the framework, prototypes and pilot data. Cooperation is exemplified in a list of aspects and related to levels of the driving task. With the concept ‘Conduct-by-Wire', cooperation happens mainly on the guidance level, where the driver can delegate manoeuvres to the automation with a specialised manoeuvre interface. With H-Mode, a haptic-multimodal interaction with highly automated vehicles based on the H(orse)-Metaphor, cooperation is mainly done on guidance and control with a haptically active interface. Cooperativeness should be a key aspect for future human–automation systems. Especially for highly automated vehicles, cooperative guidance and control is a research direction with already promising concepts and prototypes that should be further explored. The application of the presented approach is every human–machine system that moves and includes high levels of assistance/automation. Practitioner Summary:The aim of this article is to sketch a general ergonomic framework of cooperative guidance and control. After a general description, the framework is illustrated through descriptions of two examples for which prototype embodiments have been developed and preliminary evaluations conducted. Major findings of pilot studies show good system performance and acceptance. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an application of recurrent neuro-fuzzy systems to fault detection and isolation in nuclear reactors. A general framework is adopted, in which a fuzzification module is linked to an inference module that is actually a neural network adapted to the recognition of the dynamic evolution of process variables and related faults. Process data is fuzzified in order to reason rather on qualitative than on quantitative values. The fuzzified attributes feed the neural network. Two different network topologies are tested over data simulated by a commissioned simulator of a nuclear reactor: a feed-forward topology and a recurrent topology, where the additional network inputs are considered as delayed activation of output units. The later approach shows better generalization performance for the detection and isolation of a number of security related faults. A graphic interface presents a qualitative representation of symptoms and diagnostic results by colored shades that evolve with time allowing a friendly and efficient communication with operators in charge of the process security. [Copyright &y& Elsevier]