Ambient Intelligence Environments

2011 ◽  
pp. 92-98 ◽  
Author(s):  
Carlos Ramos
Keyword(s):  
Artificial Intelligence ◽  
Embedded Systems ◽  
Ambient Intelligence ◽  
New World ◽  
Physical World ◽  
Human Being ◽  
Human Machine Interaction ◽  
Artificial Intelligence Research ◽  
Essential Knowledge ◽  
Machine Interaction

The trend in the direction of hardware cost reduction and miniaturization allows including computing devices in several objects and environments (embedded systems). Ambient Intelligence (AmI) deals with a new world where computing devices are spread everywhere (ubiquity), allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviours. AmI environments may be so diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, transports, touristic attractions, stores, sport installations, and music devices. Ambient Intelligence involves many different disciplines, like automation (sensors, control, and actuators), human-machine interaction and computer graphics, communication, ubiquitous computing, embedded systems, and, obviously, Artificial Intelligence. In the aims of Artificial Intelligence, research envisages to include more intelligence in the AmI environments, allowing a better support to the human being and the access to the essential knowledge to make better decisions when interacting with these environments

Ambient Intelligence Environments

2010 ◽  
pp. 137-144
Author(s):  
Carlos Ramos
Keyword(s):  
Artificial Intelligence ◽  
Embedded Systems ◽  
Ambient Intelligence ◽  
New World ◽  
Physical World ◽  
Human Being ◽  
Human Machine Interaction ◽  
Artificial Intelligence Research ◽  
Essential Knowledge ◽  
Machine Interaction

The trend in the direction of hardware cost reduction and miniaturization allows including computing devices in several objects and environments (embedded systems). Ambient Intelligence (AmI) deals with a new world where computing devices are spread everywhere (ubiquity), allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviours. AmI environments may be so diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, transports, touristic attractions, stores, sport installations, and music devices. Ambient Intelligence involves many different disciplines, like automation (sensors, control, and actuators), human-machine interaction and computer graphics, communication, ubiquitous computing, embedded systems, and, obviously, Artificial Intelligence. In the aims of Artificial Intelligence, research envisages to include more intelligence in the AmI environments, allowing a better support to the human being and the access to the essential knowledge to make better decisions when interacting with these environments.

scholarly journals Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 834
Author(s):  
Magbool Alelyani ◽  
Sultan Alamri ◽  
Mohammed S. Alqahtani ◽  
Alamin Musa ◽  
Hajar Almater ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Vital Role ◽  
Human Machine Interaction ◽  
The Future ◽  
Level Of Knowledge ◽  
Electronic Questionnaires ◽  
Machine Interaction ◽  
Applications Of Ai

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

How AI chatbots have reshaped the frontline interface in China: examining the role of sales–service ambidexterity and the personalization–privacy paradox

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hua Fan ◽  
Bing Han ◽  
Wei Gao ◽  
Wenqian Li
Keyword(s):  
Artificial Intelligence ◽  
Mixed Methods Research ◽  
Online Survey ◽  
Polynomial Regression ◽  
Customer Experience ◽  
Human Machine Interaction ◽  
Content Type ◽  
Positive Effects ◽  
Privacy Paradox ◽  
Machine Interaction

PurposeThis study serves two purposes: (1) to evaluate the effects of organizational ambidexterity by examining how the balanced and the combined sales–service configurations of chatbots differ in their abilities to enhance customer experience and patronage and (2) to apply information boundary theory to assess the contingent role that chatbot sales–service ambidexterity can play in adapting to customers' personalization–privacy paradox.Design/methodology/approachAn online survey of artificial intelligence chatbots users was conducted, and a mixed-methods research design involving response surface analysis and polynomial regression was adopted to address the research aim.FindingsThe results of polynomial regressions on survey data from 507 online customers indicated that as the benefits of personalization decreased and the risk to privacy increased, the inherently negative (positive) effects of imbalanced (combined) chatbots' sales–service ambidexterity had an increasing (decreasing) influence on customer experience. Furthermore, customer experience fully mediated the association of chatbots' sales–service ambidexterity with customer patronage.Originality/valueFirst, this study enriches the literature on frontline ambidexterity and extends it to the setting of human–machine interaction. Second, the study contributes to the literature on the personalization–privacy paradox by demonstrating the importance of frontline ambidexterity for adapting to customer concerns. Third, the study examines the conduit between artificial intelligence (AI) chatbots' ambidexterity and sales performance, thereby helping to reconcile the previously inconsistent evidence regarding this relationship.

Synchronous Physiological Electrical Fields

2016 ◽  
pp. 107-127
Author(s):  
J.F. Pagel
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Processing ◽  
Current Knowledge ◽  
Human Machine Interaction ◽  
Similar Frequency ◽  
Electrical Fields ◽  
Electrical Potentials ◽  
Interface Potential ◽  
External Stimuli ◽  
Machine Interaction

Humans utilize sensory and motor systems developed genetically, physically and socially for interfacing with our external environment. We use these same systems to interface in our interactions with artificial intelligence. There are other functioning central nervous system (CNS) systems, however, involved in cognitive processing for which the function and environmental interface is less clear. The synchronous physiologic electrical field system utilizes broadcast extracellular electrical fields for a wide variety of CNS functions. The operations of this system are usually non-conscious and most apparent during sleep (especially the conscious states of sleep that include dreaming), and un-focused waking. The electrical fields of this system are altered and affected by both internal and external stimuli. These fields can be monitored and analyzed by artificial intelligence (AI) systems, and independently of human input, AI systems can utilize similar frequency based electrical potentials to convey data, communicate, supply power, and to store memory. From both human and AI perspectives, these systems have the potential to function more fully in human/machine interaction. This chapter reviews our current knowledge as to function, current interactive approaches, and interface potential for these physiological electrical fields.

An intelligent interface for online interaction

1984 ◽  
Vol 9 (1) ◽  
pp. 7-18 ◽  
Author(s):  
A. Vickery
Keyword(s):  
Artificial Intelligence ◽  
Online Interaction ◽  
Search System ◽  
Human Machine Interaction ◽  
Retrieval Systems ◽  
Intelligent Interface ◽  
Machine Interaction

In this paper, the author discusses the ways of improving the performance of online retrieval systems by introducing an automated interface between the enquirer and the system. In the first part of the paper, the main features of such human/machine interaction and the characteristics that the user would like to see incorporated in an interface, are de scribed. Then, studies in artificial intelligence that are particu larly relevant to the problems of implementing an intelligent interface, are discussed. The author concludes with a summary of automated mechanisms that will be needed to improve the quality of interaction between the user and the search system.

Human Factors and Artificial Intelligence – How can we achieve AI for social good (Preprint)

2020 ◽  
Author(s):  
Arlene Oetomo ◽  
Sahan Salim ◽  
Tatiana Bevilacqua ◽  
Kirti Sundar Sahu ◽  
Pedro Elkind Velmovitsky ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Independent Living ◽  
Relevant Literature ◽  
Technological Advancement ◽  
Human Machine Interaction ◽  
Social Good ◽  
Health And Wellbeing ◽  
Future Work ◽  
Machine Interaction

BACKGROUND Advances in technology will impact the field of human factors as new solutions change how we plan, share knowledge, perceive and act on real world problems. Human machine interaction will become more seamless until we are unable to differentiate between the human and the machine introducing issues of trust and privacy. Technological advancement has created big data and now we are able to tackle large problems with data for better evidence-based solutions and policy measures. OBJECTIVE This paper discusses the use of human factors methods when developing solutions that use artificial intelligence, including machine learning and deep learning, to tackle challenges for social good, especially related to health. METHODS We review relevant literature and present areas and example use cases. RESULTS The potential uses for artificial intelligence applied with human factors are discussed in four areas which impact human health and wellbeing: precision medicine, independent living, public health and the environment. CONCLUSIONS We hope to inspire future work in this field with a better understanding of how human factors can be applied to AI-based solutions. We make the case for the inclusion of HF experts on diverse project teams.

scholarly journals Combining Big Data and Artificial Intelligence for Managing Collective Knowledge in Unpredictable Environment—Insights from the Chinese Case in Facing COVID-19

2020 ◽  
Author(s):  
Francesca Iandolo ◽  
Francesca Loia ◽  
Irene Fulco ◽  
Chiara Nespoli ◽  
Francesco Caputo
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Knowledge Management ◽  
Business Models ◽  
New Technologies ◽  
Human Machine Interaction ◽  
Decision Making Processes ◽  
Unpredictable Environment ◽  
Definition Of ◽  
Machine Interaction

AbstractThe increasing fluidity of social and business configurations made possible by the opportunities provided by the World Wide Web and the new technologies is questioning the validity of consolidated business models and managerial approaches. New rules are emerging and multiple changes are required to both individuals and organizations engaged in dynamic and unpredictable paths.In such a scenario, the paper aims at describing the potential role of big data and artificial intelligence in the path toward a collective approach to knowledge management. Thanks to the interpretative lens provided by systems thinking, a framework able to explain human-machine interaction is depicted and its contribution to the definition of a collective approach to knowledge management in unpredictable environment is traced.Reflections herein are briefly discussed with reference to the Chinese governmental approach for managing COVID-19 spread to emphasise the support that a technology-based collective approach to knowledge management can provide to decision-making processes in unpredictable environments.

scholarly journals Artificial Intelligence Techniques for Conflict Resolution

2021 ◽  
Author(s):  
Reyhan Aydoğan ◽  
Tim Baarslag ◽  
Enrico Gerding
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Conflict Resolution ◽  
Computational Models ◽  
Group Decision ◽  
Preference Aggregation ◽  
Automated Negotiation ◽  
Special Issue ◽  
Human Machine Interaction ◽  
Machine Interaction

AbstractConflict resolution is essential to obtain cooperation in many scenarios such as politics and business, as well as our day to day life. The importance of conflict resolution has driven research in many fields like anthropology, social science, psychology, mathematics, biology and, more recently, in artificial intelligence. Computer science and artificial intelligence have, in turn, been inspired by theories and techniques from these disciplines, which has led to a variety of computational models and approaches, such as automated negotiation, group decision making, argumentation, preference aggregation, and human-machine interaction. To bring together the different research strands and disciplines in conflict resolution, the Workshop on Conflict Resolution in Decision Making (COREDEMA) was organized. This special issue benefited from the workshop series, and consists of significantly extended and revised selected papers from the ECAI 2016 COREDEMA workshop, as well as completely new contributions.

scholarly journals Explainable Artificial Intelligence for Human-Machine Interaction in Brain Tumor Localization

2021 ◽  
Vol 11 (11) ◽  
pp. 1213
Author(s):  
Morteza Esmaeili ◽  
Riyas Vettukattil ◽  
Hasan Banitalebi ◽  
Nina R. Krogh ◽  
Jonn Terje Geitung
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Training Methods ◽  
Human Machine Interaction ◽  
Localization Accuracy ◽  
Explainable Ai ◽  
Recent Developments ◽  
High Level ◽  
Machine Interaction ◽  
Selection Of

Primary malignancies in adult brains are globally fatal. Computer vision, especially recent developments in artificial intelligence (AI), have created opportunities to automatically characterize and diagnose tumor lesions in the brain. AI approaches have provided scores of unprecedented accuracy in different image analysis tasks, including differentiating tumor-containing brains from healthy brains. AI models, however, perform as a black box, concealing the rational interpretations that are an essential step towards translating AI imaging tools into clinical routine. An explainable AI approach aims to visualize the high-level features of trained models or integrate into the training process. This study aims to evaluate the performance of selected deep-learning algorithms on localizing tumor lesions and distinguishing the lesion from healthy regions in magnetic resonance imaging contrasts. Despite a significant correlation between classification and lesion localization accuracy (R = 0.46, p = 0.005), the known AI algorithms, examined in this study, classify some tumor brains based on other non-relevant features. The results suggest that explainable AI approaches can develop an intuition for model interpretability and may play an important role in the performance evaluation of deep learning models. Developing explainable AI approaches will be an essential tool to improve human–machine interactions and assist in the selection of optimal training methods.

scholarly journals Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning

2019 ◽  
Vol 22 (10) ◽  
pp. 1868-1884 ◽  
Author(s):  
Rainer Mühlhoff
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Abilities ◽  
Human Machine Interaction ◽  
Technological Transformation ◽  
Media Sociology ◽  
Media Technologies ◽  
Media Philosophy ◽  
Human Brains ◽  
Machine Interaction

Today, artificial intelligence (AI), especially machine learning, is structurally dependent on human participation. Technologies such as deep learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI.” This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control, and digital labor.

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