Innovative Intelligence Research Articles

Preliminary Research on the Social Attitudes toward AI’s Involvement in Christian Education in Vietnam: Promoting AI Technology for Religious Education

Artificial intelligence innovations, such as chatbots and specialized education suggestion tools, provide potential interactive and on-demand pedagogical engagement between non-Christians and Christians with Christianity. However, there is little empirical research on the readiness, acceptance, and adoption of religious education involvement of AI in a secular state such as Vietnam. This research addresses the literature gap by providing an entrepreneurial analysis and customer perspectives on the ideas of AI involvement in religious education. Specifically, the study explores whether the Vietnamese across different ages accept and have enough skills to adopt AI in Christian education innovation. The interview sample is 32 participants, selected based on their religious orientation (Christians and non-Christians) and age (Generation X, Generation Y, and Generation Z). Most respondents are open to AI application in religious education except for Church personnel. However, only Generation Z are fully prepared to adopt this innovation. Theoretically, the research customizes the Unified Theory of Acceptance and Use of Technology model into religious innovation context. Practically, this research acts as market research on the demand for AI’s religious innovation in Vietnam, an insight for future religious tech entrepreneurs.

The impact of knowledge sharing behaviour and social intelligence of university academics on their performance: The mediating role of competencies

Generally, university academics’ knowledge sharing behavior and social intelligence are said to be significant in improving their performance through their grasp of competencies Nevertheless, the association has yet to be thoroughly researched. Thus, this study applied the proposed mediation model among university academics in the Business School of seven Malaysian private universities. The salient findings of this research are that the competencies mediate the association among (1) knowledge sharing behavior and innovative teaching and (2) social intelligence and innovative teaching. Apart from that, it was observed that knowledge sharing behavior impacted innovative teaching and supervision, while social intelligence impacted innovative teaching, research and publication and supervision. Implications were discussed.

How data shape actor relations in artificial intelligence innovation systems: an empirical observation from China

Abstract With the rise of artificial intelligence (AI), data are widely viewed as the “new oil”. However, data substantially differ from conventional resources in the sense that they are important not only for production but also for knowledge development and public policymaking. This article explores whether and how data reshape government–industry–university relations in the era of AI. Taking China’s AI innovation system as a case, this article investigates the dynamics of actor relations in the business subsystem, knowledge subsystem, and regulatory subsystem. The change of the fundamental input from physical resources to virtual data in AI innovation systems has significantly transformed the relations among industry, state, and academia, and digital platforms are playing an increasingly important role in business value creation, knowledge generation, and regulation formation due to their control of valuable data and frontier expertise in the context of uncertainty.

The Disruptive Adaptations of Construction 4.0 and Industry 4.0 as a Pathway to a Sustainable Innovation and Inclusive Industrial Technological Development

Construction 4.0 (C4.0) has tremendously impacted construction activities worldwide in recent times. This effect was made possible on account of innovations brought about by Industry 4.0 (I4.0). Industry 4.0 has the potential to create Construction 4.0 through the integration of the design, construction and maintenance of infrastructure through useful component integration for industrial and technological development. Therefore, this study aimed to present a pathway for achieving sustainable innovations and inclusive technological and infrastructural developments. The following parameters were reviewed in this study as part of the goals and objectives set in the survey: identifying the adaptable areas of Construction 4.0 in design, planning, construction and maintenance as part of infrastructural innovation in order to study the industrial application drivers of I4.0 and C4.0 hindrances in achieving C4.0; achieving the automation dream through C4.0, benchmarking the social and economic implications of C4.0 and identifying the issues and challenges in achieving sustainable innovation through infrastructural development and documenting the disruptive tools of C4.0 in achieving a sustainable design through technological development and examining the critical factors influencing the effective adaptation of C4.0 in achieving growth. The authors utilised 200 construction firms for this study using the Cochran and Slovin’s formulas. In addition, the sample size of 150 respondents that constituted the study were construction professionals. The respondents used the simple percentage, relative index, Spearman’s rank, Mann–Whitney U test, Kendall’s Tau test, Student’s t-test, ANOVA and chi-square tools in the data processing. The study found out, among other things, the following as part of the parameters earlier proposed: the introduction of a circular economy by adopting intelligent innovation, engaging new tools, technological innovation diffusion and the vertical and horizontal integration of versatile tools like I4.0 and C4.0 for inclusive technological development. This study recommended the objective and effective adaptation of I4.0 tools to enhance C4.0 for technical development, circular economic integration and a framework for sustainable innovation and a system for the inclusive monitoring of innovations in the design and planning of construction maintenance.

Towards a responsible integration of artificial intelligence technology in the construction sector

Abstract The uptake of artificial intelligence (AI) innovations in the construction industry implies new physical and socio-political risks. Risk analysis is crucial to managing these risks, but is challenging due to the inherent complexity and lack of applied knowledge specific to construction activities. We forecast potential AI innovation trends related to construction from a keyword concurrence analysis of 974 journal articles. We use this analysis to identify potential sector-specific risks in construction activities by forming a risk map based on an analysis of ninety-four articles related to the ethics and impacts of AI technologies. We found that trends in innovation more often involve areas in construction considered ‘back-end’ tasks, as opposed to ‘on-site’. Thus, the potential risks and regulatory challenges from the use of AI technology in the construction sector will be legal, financial, and social, as opposed to risks involving physical harm to users on construction sites.

CMBA-SVM: a clinical approach for Parkinson disease diagnosis

Different intelligence models are used by researchers for an easy and successful diagnosis of neurodegenerative diseases like Parkinson’s disease (PD) but none of the adopted methods is efficient. Early-stage identification of disease and diagnosis based upon vocal measurements is important to enhance the productive lives of the patient. An innovative intelligence model with a combination of a chaos-mapped bat algorithm (CMBA) and a support vector machine (SVM) called CMBA-SVM is introduced. The coordination of the CMBA method effectively resolved the SVM parameter tuning issues. CMBA is used for the identification of featured and consider input for SVM to develop an intelligence model. The effectiveness and performance of the proposed model are compared to ECFA-SVM and CFA-SVM. The performance of the proposed model was tested with considering various parameters in terms of accuracy, sensitivity, specificity, and AUC by taking two sets of PD data set (from UCI repository and Istanbul collected data set). The performance results and perspective of this study were compared with the different intelligence methods described literature study that used the same data, and the CMBA-SVM showed better efficiency than the other. The CMBA-SVM has a very good prospect, which may provide huge expediency to the clinicians to improve quality during PD diagnosis.

The Promise and Peril of Automated Negotiators

Innovations in artificial intelligence are enabling a new class of applications that can negotiate with people through chat or spoken language. Developed in close collaboration with behavioral science research, these algorithms can detect, mimic, and leverage human psychology, enabling them to undertake such functions as the detection of common mistakes made by novice negotiators. These algorithms can simulate the cognitive processes that shape human negotiations and make use of these models to influence negotiated outcomes. This article reviews some of the scientific advances enabling this technology and discusses how it is being used to advance negotiation research, teaching, and practice.

Social aspects of innovation management in digital sector

Innovation management in the digital sector requires taking into account the institutional, socio-structural and socio-cultural context. The article discusses the thesis about unevenness of digitalization in different spheres of society and for different social groups. The actual implementation and use of innovations in everyday life depends not only on development of infrastructure and digital literacy, but also on emerging social norms, including associated with peculiarities of culture of society, belonging to the “artificial” or “natural” type. Artificial intelligence and digital innovations affect the field of health, human corporeality, bioethics, biosociality, etc. There are new sociological approaches which make it possible to study the management of innovations, for example, atypical sociology, augmented reality, concept of posthumanism, etc. An information policy is needed that allows representatives of social groups to perceive innovations more adequately.

The Effect of Medical Artificial Intelligence Innovation Locus on Consumer Adoption of New Products

The purpose of this study is to explore the effect of innovation locus on consumers’ adoption of artificial intelligence products in healthcare. Across three experiments, we demonstrate that consumers are more likely to accept medical artificial intelligence for peripheral products than for core products. This effect is mediated by individual uniqueness neglect and moderated by medical risk. Specifically, individual uniqueness neglect significantly mediates the effect of innovation loci on consumers’ adoption of medical artificial intelligence for high-risk products. In contrast, the mediating effect of individual uniqueness neglect becomes weaker for low-risk products. These findings make important theoretical contributions to the artificial intelligence and healthcare literature. We also provide some practical implications to promote the future development of medical artificial intelligence products and improve consumers’ acceptance of medical artificial intelligence.

Evaluation of service innovation capability of imported cross-border intelligent E-commerce platform

The cross-border e-commerce is born in the field of international trade within the social context of economic globalization and the rapid development of the Internet technologies. Numerous B2 C cross-border intelligent e-commerce platforms are set up one after another. The importance of service innovation is recognized gradually but is not fully known and the evaluation system of their service is not sound. Therefore, how to scientifically evaluate and improve the ability of the service innovation of cross-border intelligent e-commerce platform is a hotspot. The development mode and problems of imported e-commerce retail platform are mainly focused, but the studies on intelligent innovation of cross-border e-commerce platform and on its evaluation and analysis are relatively few. To make up for the lack of understanding, four imported cross-border intelligent e-commerce retail platforms in China were chosen as the objects. Second, the evaluation model of imported cross-border intelligent e-commerce retail platforms was established based on factor analysis to obtain the evaluation index system, and these four platforms’ service innovation abilities were calculated and ranked for discussing the causes of their differences and indicating the routes clearly and definitely for the platforms to improve their service innovation ability. The results manifest that the market share and popularity of the intelligent e-commerce platforms are in direct proportion to the service innovation capability. Luxury brand marketing and high market share can significantly enhance the innovative service ability. The conclusions have a certain guiding significance for the development imported cross-border intelligent e-commerce platforms.

Artificial Intelligence and International System Structure

Abstract Previous studies have investigated technology’s impact on international affairs, but few have analyzed the effect of artificial intelligence on the international system structure. This study integrates heterogeneous datasets and network science concepts with several power factors and artificial intelligence advances as a methodology to understand the evolution of the international system with a perspective around research, knowledge, innovation, and technology as an endogenous variable. Our findings indicate that the international fitness variable could be considered as a mechanism to interpret the system dynamics, especially when artificial intelligence interacts with different topics of the system. Overall, we provide quantitative evidence of the evolution of artificial intelligence innovations and technological power to identify system structure changes, both in central and peripheral countries.

From Automation to Autonomy: Legal and Ethical Responsibility Gaps in Artificial Intelligence Innovation

The increasing prominence of artificial intelligence (AI) systems in daily life and the evolving capacity of these systems to process data and act without human input raise important legal and ethical concerns. This article identifies three primary AI actors in the value chain (innovators, providers, and users) and three primary types of AI (automation, augmentation, and autonomy). It then considers responsibility in AI innovation from two perspectives: (i) strict liability claims arising out of the development, commercialization, and use of products with built-in AI capabilities (designated herein as “AI artifacts”); and (ii) an original research study on the ethical practices of developers and managers creating AI systems and AI artifacts. The ethical perspective is important because, at the moment, the law is poised to fall behind technological reality—if it hasn’t already. Consideration of the liability issues in tandem with ethical perspectives yields a more nuanced assessment of the likely consequences and adverse impacts of AI innovation. Companies should consider both legal and ethical strategies thinking about their own liability and ways to limit it, as well as policymakers considering AI regulation ex ante.

Personhood Revisited: Implications for Humanoids

"Technological innovations in Artificial Intelligence have reached a state where human-like robotics are endowed with rich personality and cognitive intelligence, able to engage emotionally and deeply with people. This progress in developing humanoids opens the way for a robot to obtain not just human, but superhuman attributes, such as omniscience and omnipotence, autonomy and self-awareness, freedom and interpersonality. On the other hand, this futurist situation could be considered as a possible threat to Christian anthropology, since it reaches a creation having the likeness of humanity that seem to retain a sense of personhood. This paper attempts to confront these challenges facing Christian theology today through first, revisiting Christian anthropology and the Patristic views on personhood, which look at a human being as an unfathomable mystery. Second, it presents the implications of this theology upon the arguments that consider humanoids as persons, showing that this postmodern issue is not just a crisis in anthropology, but also has its roots in a crisis in knowledge. Finally, this paper affirms that Christians are called to embrace science and technological progress. This can be done when rationalism is led by the intellect, the spiritual cognitive center of humankind. In doing so, humankind reaches epignosis, the correct or divine knowledge, the gift of true perception, or right discernment, which surpasses all rational human knowledge and algorithms, and directs all technological powers to God’s glory. Keywords: Orthodox Church, Patristics, Artificial Intelligence, Humanoids, Personhood, Christian Anthropology, Divine Knowledge"

Training Intelligent Innovation Ability of Packaging Engineering Talents

<p style="text-align: justify;"><span style="font-size:16px;font-family: 'Times New Roman','serif'">The</span> <span style="font-size:16px;font-family:'Times New Roman','serif'">21^st</span> <span style="font-size:16px;font-family:'Times New Roman','serif'">century is an era of innovation. Actively carrying out innovation and entrepreneurship education for college students is not only conducive to solving the problem of college students' employment difficulties, but also conducive to the overall development and success of students. Starting from the packaging engineering, this paper puts forward the innovative design of the packaging engineering professional studio teaching platform for the problems existing in the packaging engineering professional teaching, and explores and studies the packaging engineering professional innovation and entrepreneurial talent training, which has strong practice for training. For talents with the ability, innovation ability, entrepreneurial ability and employability, it has strong theoretical and practical value. It provides a new constructive idea for professional teaching reform and research.</span>

Innovation intelligence and its role in environmental uncertainty management: a conceptual framework

PurposeOrganizations need actionable knowledge to cope with environmental uncertainty, make effective decisions and develop innovation strategies. Since innovation evolves through generations, the present study aims to unravel and define innovation intelligence, considering this transformation, and discuss how environmental uncertainty is resolved in each one.Design/methodology/approachThis article is a conceptual paper that employs a typology and model approach in its research designFindingsContexts are categorized into ordered and unordered (according to the Cynefin framework), in which intelligence with prediction and control approaches are applied for uncertainty management, respectively. Also, the three generations of innovation management, namely, technology push, market pull and a combination of these two (hybrid), intelligence benefit from a prediction approach, and in the networked (collaborative) generation, intelligence takes advantage of a control approach.Research limitations/implicationsThe conceptual approach adopted in this research is limited to, and focused on, understanding intelligence, innovation intelligence and presenting preliminary insights into their relationship with uncertainty management.Practical implicationsThis research guides decision-makers to adopt the appropriate intelligence approach to manage uncertainty during their innovation management process and illustrate it by the industry uncertainty matrix and COVID-19 pandemic situation.Originality/valueThis study proposes a typology of intelligence based on different knowledge pyramids. Also, it introduces innovation intelligence and its relation to knowledge management and environmental uncertainty management that has not yet been clearly addressed in the literature. Moreover, it determines the uncertainty management approaches for each variant of innovation intelligence.

An Analysis on Intelligence Innovation with Spirit of Artisans Under Ubiquitous Network in Grid Enterprises

The paper discusses the relationship of spirit of artisans and intelligence Innovation. The survey shows that Intelligence Innovation has great contribution to organizational performance in an intelligent era. The survey from local state grid corporations shows that the interaction between the Artisan spirit and refinement process innovation under Ubiquitous Network in Grid Enterprises and organizational psychological empowerment acts as a significant moderator. This research provides useful tools for organizational intelligence innovation through the exertion of enterprise’ craftsmen examples.

Preface

It was a great pleasure to welcome all the participants from different countries to the 2020 International Conference on Machine Learning and Computer Application (ICMLCA2020) in Shangri-La, China during September 11-13, 2020. This three-day conference was presented by AEIC Academic Exchange Information Center. This unique international conference provided a forum for about 70 academics, researchers and practitioners from many industries to exchange ideas and shared recent developments in the fields of Machine Learning, Computer Application and Robotic. We provided an opportunity to collaborate and advance the theory and practice of major trends in related fields and as a bridge for scholars to share and inspire their opinion.The conference model was divided into two sessions, including oral presentations and keynote speeches. In the first part, some scholars, whose submissions were selected as the excellent papers, were given 15 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In this conference, we were greatly honor to have invited three distinguished experts to serve as our Conference Chairmen. They were Prof. Zhenghua Xu, from State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Prof.Wei Wei, from Xi’an University of Technology, senior member of IEEE, and Assoc. professor Dr.Wael Yafooz, from Dean, Faculty computer and information technology, al-madinah international university, Malaysia.During the conference, we invited five industry elites to present their insightful speeches. The first keynote speaker, Prof. Xuebin Chen, from North China University of Science and Technology. His research direct on Network Security, Big Data, Data Security. Prof. Zhenghua Xu, from State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology. His research focuses on topics within artificial intelligence and data mining, especially deep learning, medical artificial intelligence, health data mining, and reinforcement learning. The third keynote speaker, Prof. Ahmad T. Al-Taani, from Department of Computer Sciences, Faculty of Information Technology, Yarmouk University, IRBID, JORDAN, shared a speech on his specialty related to Part-of-Speech Tagging using Swarm Intelligence. Mr. Nirmalya Thakur presented an insightful speech on Machine Learning and its Applications for Developing Emotional Intelligence in Smart Homes. His talk aims to introduce and discuss the immense potential at the intersection of Machine Learning with Internet of Things, Human-Computer Interaction and their related disciplines. As the last keynote speaker, A.P. HAN-Teng Liao, talked about Artificial Intelligence Standardization: Implications for Machine Learning Innovation and Application. This talk summarized the challenges and opportunities, surrounding machine learning, for intelligent innovations and applications.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 reviewed them. These papers feature following topics but are not limited to: Machine Learning, Computer Applications, Robotic and other related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard.List of Committee member, Conference Chairman, Academic Committee Chairman, Organizing Committees, Academic Committees Member, International Technical Program Committees Member, Organization Committees Memberare availble in this pdf.

Artificial intelligence innovation in education: A twenty-year data-driven historical analysis

Reflecting on twenty years of educational research, we retrieved over 400 research article on the application of artificial intelligence (AI) and deep learning (DL) techniques in teaching and learning. A computerised content analysis was conducted to examine how AI and DL research themes have evolved in major educational journals. By doing so, we seek to uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade, due to the discipline’s dynamism. By examining the major research themes and historical trends from 2000 to 2019, we demonstrate that, as advanced technologies in education evolve over time, some areas of research topics seem have stood the test of time, while some others have experienced peaks and valleys. More importantly, our analysis highlights the paradigm shifts and emergent trends that are gaining prominence in the field of educational research. For instance, the results suggest the decline in conventional tech-enabled instructional design research and the flourishing of student profiling models and learning analytics. Furthermore, this paper serves to raise awareness on the opportunities and challenges behind AI and DL for pedagogical adaptation and initiate a dialogue.

The pace of artificial intelligence innovations: Speed, talent, and trial-and-error

• Investigating the pace of AI innovations has thus become increasingly relevant. Yet, few studies have managed to measure or depict this increasing velocity of innovations in AI. • A research framework and three measures, i.e., Average Time Interval (ATI), Innovation Speed (IS), and Update Speed (US), are proposed, to explore the pace of AI from the perspective of AI papers, AI players, and trial and error. The fine-grained analysis of the AI field enabled to portrait the pace of AI innovation and demonstrated that the proposed approach can be adopted to understand other fast-growing fields. • In 2019, more than 3 AI preprints were submitted to arXiv per hour, over 148 times faster than in 1994. Furthermore, there was one deep learning–related preprint submitted to arXiv every 0.87 hours in 2019, over 1,064 times faster than in 1994. • 5.26 new researchers entered into the field of AI each hour in 2019, more than 175 times faster than in the 1990s. • An updated version of the AI preprint was submitted to arXiv every 41 days, with around 33% of arXiv’s AI preprints being updated twice or more in 2019. It took about 0.2 year on average for AI preprints to get their first citations from 2014–2019, around 5 times faster than from 2000–2007. Innovations in artificial intelligence (AI) are occurring at speeds faster than ever witnessed before. However, few studies have managed to measure or depict this increasing velocity of innovations in the field of AI. In this paper, we combine data on AI from arXiv and Semantic Scholar to explore the pace of AI innovations from three perspectives: AI publications, AI players, and AI updates (trial and error). A research framework and three novel indicators, Average Time Interval (ATI), Innovation Speed (IS) and Update Speed (US), are proposed to measure the pace of innovations in the field of AI. The results show that: (1) in 2019, more than 3 AI preprints were submitted to arXiv per hour, over 148 times faster than in 1994. Furthermore, there was one deep learning–related preprint submitted to arXiv every 0.87 h in 2019, over 1064 times faster than in 1994. (2) For AI players, 5.26 new researchers entered into the field of AI each hour in 2019, more than 175 times faster than in the 1990s. (3) As for AI updates (trial and error), one updated AI preprint was submitted to arXiv every 41 days, with around 33 % of AI preprints having been updated at least twice in 2019. In addition, as reported in 2019, it took, on average, only around 0.2 year for AI preprints to receive their first citations, which is 5 times faster than 2000–2007. This swift pace in AI illustrates the increase in popularity of AI innovation. The systematic and fine-grained analysis of the AI field enabled to portrait the pace of AI innovation and demonstrated that the proposed approach can be adopted to understand other fast-growing fields such as cancer research and nano science.

LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks

The interest for patient trajectory prediction, a sort of computer-aided medicine, has steadily increased with the pace of artificial intelligence innovation. Notwithstanding, the design of effective systems able to predict clinical outcomes based on the history of a patient is far from trivial. Works so far are based on neural architectures with low performance, especially when using low-cardinality datasets; alternatively, complex inference approaches are hard to reproduce and/or extrapolate as they are designed for very specific circumstances. We introduce LIG-Doctor, an artificial neural network architecture based on two Minimal Gated Recurrent Unit networks functioning in a bidirectional parallel manner, benefiting from temporal events both forward and backward. In comparison to state-of-the-art works, consistent improvements were achieved in prognosis prediction, as assessed with metrics Recall@k, Precision@k, F1-score, and AUC-ROC. Besides the detailed delineation of our architecture, a sequence of experiments is reported with insights that progressively guided design decisions to inspire future works on similar problems. Our results shall contribute to the improvement of computer-aided medicine and, more generally, to processes related to the design of neural network architectures.