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Sekmen A, Bilgin B. Manifold-based approach for neural network robustness analysis. COMMUNICATIONS ENGINEERING 2024; 3:118. [PMID: 39182015 PMCID: PMC11344765 DOI: 10.1038/s44172-024-00263-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 08/05/2024] [Indexed: 08/27/2024]
Abstract
It is important to understand the mathematical foundations of neural networks and to include robustness in model evaluation. Here, we introduce algorithms based on manifold curvature estimation to assess neural network robustness. These algorithms rely solely on training data and do not require regular or adversarial test data. Initially, a metric is proposed to measure the curvature of discrete data manifolds by introducing weighted angles concept between subspaces. Following this, a robustness measure is introduced that is independent of network architecture or model parameters. Lastly, two additional methods are introduced, utilizing curvature estimation of special manifolds formed by using gradient vectors between output and input network layers, alongside manifold curvature estimation. A comprehensive evaluation is provided on multiple network models using the CIFAR-10 dataset. Manifold geometry-based robustness analysis may lead to the development of not only accurate but also robust neural network models.
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Affiliation(s)
- Ali Sekmen
- Department of Computer Science, Tennessee State University, Nashville, TN, USA
| | - Bahadir Bilgin
- Department of Computer Science, Tennessee State University, Nashville, TN, USA.
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2
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McGuire J, De Cremer D, Van de Cruys T. Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence. Sci Rep 2024; 14:18525. [PMID: 39122865 PMCID: PMC11316096 DOI: 10.1038/s41598-024-69423-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024] Open
Abstract
The emergence of generative AI technologies has led to an increasing number of people collaborating with AI to produce creative works. Across two experimental studies, in which we carefully designed and programmed state-of-the-art human-AI interfaces, we examine how the design of generative AI systems influences human creativity (poetry writing). First, we find that people were most creative when writing a poem on their own, compared to first receiving a poem generated by an AI system and using sophisticated tools to edit it (Study 1). Following this, we demonstrate that this creativity deficit dissipates when people co-create with-not edit-AI and establish creative self-efficacy as an important mechanism in this process (Study 2). Thus, our findings indicate that people must occupy the role of a co-creator, not an editor, to reap the benefits of generative AI in the production of creative works.
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Affiliation(s)
- Jack McGuire
- Department of Management & Organizational Development, D'Amore-McKim School of Business, Northeastern University, Hayden Hall, 101, 370 Huntington Ave, Boston, MA, 02115, USA.
- Department of Management and Organisation, National University of Singapore, Singapore, Singapore.
| | - David De Cremer
- Department of Management & Organizational Development, D'Amore-McKim School of Business, Northeastern University, Hayden Hall, 101, 370 Huntington Ave, Boston, MA, 02115, USA
- Department of Management and Organisation, National University of Singapore, Singapore, Singapore
| | - Tim Van de Cruys
- Linguistics Research Unit, Faculty of Arts, KU Leuven, Blijde-Inkomststraat 21, Box 3308, 3000, Leuven, Belgium
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Kambur H, Dolunay A. A research on copyright issues impacting artists emotional states in the framework of artificial intelligence. Front Psychol 2024; 15:1409646. [PMID: 39171225 PMCID: PMC11335679 DOI: 10.3389/fpsyg.2024.1409646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 07/23/2024] [Indexed: 08/23/2024] Open
Abstract
Art and artistic creation serve as a means for artists to communicate with their environment, society, and the external world. However, the protection of artistic creations, as forms of communication, is not only a right for artists but also serves as a crucial safeguard that nurtures them during the creative process. Beyond the traditional issues of copyright, the significant advancements in Artificial Intelligence (AI) in today's digital world have introduced a new debate regarding the ownership of copyright in artistic creations generated by AI. The question arises whether copyright belongs to the AI itself or to the individuals who guide the creative process behind it. In this study, based on the concepts of art, artistic creation, and emotional states, copyright issues will be examined. Data obtained from semi-structured in-depth interviews with artists and academic experts (eight artists, two communication experts, two law experts, and eight psychology experts) in the field will be analysed through content analysis to explore their perspectives regarding the discussion on emotional states, AI, and copyrights. The research highlights the variability of emotional states and their significant effects on individuals. Addressing the increasing trend of copyright issues, particularly within the framework of digitalization and inadequate legal regulations, it was found that artists' emotional states are negatively impacted by these problems. This negative influence can adversely affect artists' creativity and desire to produce. On the other hand, it was also identified that in artworks produced especially through AI, if artists' rights are not protected, there is a possibility of negative emotional states arising. In conclusion, suggestions are as follows: Emphasising the importance of awareness-raising educational activities nationally and internationally, national copyright law (in Northern Cyprus) needs to be revised to protect traditional copyright and be expanded to include digital copyright, especially for works produced through AI. On an international level, emphasising the need to revise international agreements to include regulations for works produced through AI or to create a new agreement based on the importance of this issue.
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Affiliation(s)
- Hüseyin Kambur
- Faculty of Communication, Near East University, Nicosia, Cyprus
| | - Ayhan Dolunay
- Faculty of Communication, Grand Library, Near East University, Nicosia, Cyprus
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Rony MKK, Numan SM, Johra FT, Akter K, Akter F, Debnath M, Mondal S, Wahiduzzaman M, Das M, Ullah M, Rahman MH, Das Bala S, Parvin MR. Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care. Health Sci Rep 2024; 7:e70006. [PMID: 39175600 PMCID: PMC11339127 DOI: 10.1002/hsr2.70006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 07/31/2024] [Accepted: 08/06/2024] [Indexed: 08/24/2024] Open
Abstract
Background With the ever-increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in-depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology. Objectives This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care. Methods This qualitative research employed a descriptive and phenomenological approach using in-depth interviews. Data were collected through a semi-structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data. Results The thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient-centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care. Conclusions This research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.
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Affiliation(s)
| | - Sharker Md. Numan
- School of Science and TechnologyBangladesh Open UniversityGazipurBangladesh
| | - Fateha tuj Johra
- Masters in Disaster ManagementUniversity of DhakaDhakaBangladesh
| | - Khadiza Akter
- Master of Public HealthDaffodil International UniversityDhakaBangladesh
| | - Fazila Akter
- Dhaka Nursing Collegeaffiliated with the University of DhakaDhakaBangladesh
| | - Mitun Debnath
- Master of Public HealthNational Institute of Preventive and Social MedicineDhakaBangladesh
| | - Sujit Mondal
- Master of Science in NursingNational Institute of Advanced Nursing Education and Research MugdaDhakaBangladesh
| | - Md. Wahiduzzaman
- School of Medical SciencesShahjalal University of Science and TechnologySylhetBangladesh
| | - Mousumi Das
- Master of Public HealthLeading UniversitySylhetBangladesh
| | - Mohammad Ullah
- College of NursingInternational University of Business Agriculture and TechnologyDhakaBangladesh
| | | | - Shuvashish Das Bala
- College of NursingInternational University of Business Agriculture and TechnologyDhakaBangladesh
| | - Mst. Rina Parvin
- Bangladesh Army (AFNS Officer)Combined Military Hospital DhakaDhakaBangladesh
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Yi A, Anantrasirichai N. A Comprehensive Study of Object Tracking in Low-Light Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:4359. [PMID: 39001140 PMCID: PMC11244102 DOI: 10.3390/s24134359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/16/2024]
Abstract
Accurate object tracking in low-light environments is crucial, particularly in surveillance, ethology applications, and biometric recognition systems. However, achieving this is significantly challenging due to the poor quality of captured sequences. Factors such as noise, color imbalance, and low contrast contribute to these challenges. This paper presents a comprehensive study examining the impact of these distortions on automatic object trackers. Additionally, we propose a solution to enhance the tracking performance by integrating denoising and low-light enhancement methods into the transformer-based object tracking system. Experimental results show that the proposed tracker, trained with low-light synthetic datasets, outperforms both the vanilla MixFormer and Siam R-CNN.
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Al-Zahrani AM. Unveiling the shadows: Beyond the hype of AI in education. Heliyon 2024; 10:e30696. [PMID: 38737255 PMCID: PMC11087970 DOI: 10.1016/j.heliyon.2024.e30696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/26/2024] [Accepted: 05/02/2024] [Indexed: 05/14/2024] Open
Abstract
Despite the wave of enthusiasm for the role of Artificial Intelligence (AI) in reshaping education, critical voices urge a more tempered approach. This study investigates the less-discussed 'shadows' of AI implementation in educational settings, focusing on potential negatives that may accompany its integration. Through a multi-phased exploration consisting of content analysis and survey research, the study develops and validates a theoretical model that pinpoints several areas of concern. The initial phase, a systematic literature review, yielded 56 relevant studies from which the model was crafted. The subsequent survey with 260 participants from a Saudi Arabian university aimed to validate the model. Findings confirm concerns about human connection, data privacy and security, algorithmic bias, transparency, critical thinking, access equity, ethical issues, teacher development, reliability, and the consequences of AI-generated content. They also highlight correlations between various AI-associated concerns, suggesting intertwined consequences rather than isolated issues. For instance, enhancements in AI transparency could simultaneously support teacher professional development and foster better student outcomes. Furthermore, the study acknowledges the transformative potential of AI but cautions against its unexamined adoption in education. It advocates for comprehensive strategies to maintain human connections, ensure data privacy and security, mitigate biases, enhance system transparency, foster creativity, reduce access disparities, emphasize ethics, prepare teachers, ensure system reliability, and regulate AI-generated content. Such strategies underscore the need for holistic policymaking to leverage AI's benefits while safeguarding against its disadvantages.
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Affiliation(s)
- Abdulrahman M. Al-Zahrani
- Department of Learning Design and Technology, Faculty of Education, University of Jeddah, P.O. box 15758, 21454, Jeddah, Saudi Arabia
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7
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Gu X. Enhancing social media engagement using AI-modified background music: examining the roles of event relevance, lyric resonance, AI-singer origins, audience interpretation, emotional resonance, and social media engagement. Front Psychol 2024; 15:1267516. [PMID: 38686081 PMCID: PMC11057495 DOI: 10.3389/fpsyg.2024.1267516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 03/25/2024] [Indexed: 05/02/2024] Open
Abstract
Introduction Drawing on the S-O-R model, this study aims to investigate the influence of three stimuli from AI-modified music (i.e., event relevance, lyric resonance, and AI-singer origins), two responses from social media content consumers (i.e., audience interpretation and emotional resonance) on the social media engagement of personalized background music modified by artificial intelligence (AI). Methods The structural equation modeling analyses of 467 social media content consumers' responses confirmed the role of those three stimuli and the mediating effect of audience interpretation and emotional resonance in shaping social media engagement. Results The findings shed light on the underlying mechanisms that drive social media engagement in the context of AI-modified background music created for non-professional content creators. Discussion The theoretical and practical implications of this study advance our understanding of social media engagement with AI-singer-originated background music and provide a basis for future investigations into this rapidly evolving phenomenon in the gig economy.
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Affiliation(s)
- Xiaohui Gu
- Conservatory of Music, Communication University of Zhejiang, Hangzhou, China
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8
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Hubert KF, Awa KN, Zabelina DL. The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks. Sci Rep 2024; 14:3440. [PMID: 38341459 PMCID: PMC10858891 DOI: 10.1038/s41598-024-53303-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
The emergence of publicly accessible artificial intelligence (AI) large language models such as ChatGPT has given rise to global conversations on the implications of AI capabilities. Emergent research on AI has challenged the assumption that creative potential is a uniquely human trait thus, there seems to be a disconnect between human perception versus what AI is objectively capable of creating. Here, we aimed to assess the creative potential of humans in comparison to AI. In the present study, human participants (N = 151) and GPT-4 provided responses for the Alternative Uses Task, Consequences Task, and Divergent Associations Task. We found that AI was robustly more creative along each divergent thinking measurement in comparison to the human counterparts. Specifically, when controlling for fluency of responses, AI was more original and elaborate. The present findings suggest that the current state of AI language models demonstrate higher creative potential than human respondents.
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Affiliation(s)
- Kent F Hubert
- Department of Psychological Sciences, University of Arkansas, Fayetteville, AR, 72701, USA.
| | - Kim N Awa
- Department of Psychological Sciences, University of Arkansas, Fayetteville, AR, 72701, USA
| | - Darya L Zabelina
- Department of Psychological Sciences, University of Arkansas, Fayetteville, AR, 72701, USA
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9
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Wu Y, Yi A, Ma C, Chen L. Artificial intelligence for video game visualization, advancements, benefits and challenges. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:15345-15373. [PMID: 37679183 DOI: 10.3934/mbe.2023686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
In recent years, the field of artificial intelligence (AI) has witnessed remarkable progress and its applications have extended to the realm of video games. The incorporation of AI in video games enhances visual experiences, optimizes gameplay and fosters more realistic and immersive environments. In this review paper, we systematically explore the diverse applications of AI in video game visualization, encompassing machine learning algorithms for character animation, terrain generation and lighting effects following the PRISMA guidelines as our review methodology. Furthermore, we discuss the benefits, challenges and ethical implications associated with AI in video game visualization as well as the potential future trends. We anticipate that the future of AI in video gaming will feature increasingly sophisticated and realistic AI models, heightened utilization of machine learning and greater integration with other emerging technologies leading to more engaging and personalized gaming experiences.
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Affiliation(s)
- Yueliang Wu
- School of Architecture and Art Design, Hunan University of Science and Technology, Xiangtan 411100, China
| | - Aolong Yi
- School of Architecture and Art Design, Hunan University of Science and Technology, Xiangtan 411100, China
| | - Chengcheng Ma
- School of Architecture and Art Design, Hunan University of Science and Technology, Xiangtan 411100, China
| | - Ling Chen
- College of Engineering and Design, Hunan Normal University, Changsha 410081, China
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10
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Hallsworth JE, Udaondo Z, Pedrós‐Alió C, Höfer J, Benison KC, Lloyd KG, Cordero RJB, de Campos CBL, Yakimov MM, Amils R. Scientific novelty beyond the experiment. Microb Biotechnol 2023; 16:1131-1173. [PMID: 36786388 PMCID: PMC10221578 DOI: 10.1111/1751-7915.14222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 02/15/2023] Open
Abstract
Practical experiments drive important scientific discoveries in biology, but theory-based research studies also contribute novel-sometimes paradigm-changing-findings. Here, we appraise the roles of theory-based approaches focusing on the experiment-dominated wet-biology research areas of microbial growth and survival, cell physiology, host-pathogen interactions, and competitive or symbiotic interactions. Additional examples relate to analyses of genome-sequence data, climate change and planetary health, habitability, and astrobiology. We assess the importance of thought at each step of the research process; the roles of natural philosophy, and inconsistencies in logic and language, as drivers of scientific progress; the value of thought experiments; the use and limitations of artificial intelligence technologies, including their potential for interdisciplinary and transdisciplinary research; and other instances when theory is the most-direct and most-scientifically robust route to scientific novelty including the development of techniques for practical experimentation or fieldwork. We highlight the intrinsic need for human engagement in scientific innovation, an issue pertinent to the ongoing controversy over papers authored using/authored by artificial intelligence (such as the large language model/chatbot ChatGPT). Other issues discussed are the way in which aspects of language can bias thinking towards the spatial rather than the temporal (and how this biased thinking can lead to skewed scientific terminology); receptivity to research that is non-mainstream; and the importance of theory-based science in education and epistemology. Whereas we briefly highlight classic works (those by Oakes Ames, Francis H.C. Crick and James D. Watson, Charles R. Darwin, Albert Einstein, James E. Lovelock, Lynn Margulis, Gilbert Ryle, Erwin R.J.A. Schrödinger, Alan M. Turing, and others), the focus is on microbiology studies that are more-recent, discussing these in the context of the scientific process and the types of scientific novelty that they represent. These include several studies carried out during the 2020 to 2022 lockdowns of the COVID-19 pandemic when access to research laboratories was disallowed (or limited). We interviewed the authors of some of the featured microbiology-related papers and-although we ourselves are involved in laboratory experiments and practical fieldwork-also drew from our own research experiences showing that such studies can not only produce new scientific findings but can also transcend barriers between disciplines, act counter to scientific reductionism, integrate biological data across different timescales and levels of complexity, and circumvent constraints imposed by practical techniques. In relation to urgent research needs, we believe that climate change and other global challenges may require approaches beyond the experiment.
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Affiliation(s)
- John E. Hallsworth
- Institute for Global Food Security, School of Biological SciencesQueen's University BelfastBelfastUK
| | - Zulema Udaondo
- Department of Biomedical InformaticsUniversity of Arkansas for Medical SciencesLittle RockArkansasUSA
| | - Carlos Pedrós‐Alió
- Department of Systems BiologyCentro Nacional de Biotecnología (CSIC)MadridSpain
| | - Juan Höfer
- Escuela de Ciencias del MarPontificia Universidad Católica de ValparaísoValparaísoChile
| | - Kathleen C. Benison
- Department of Geology and GeographyWest Virginia UniversityMorgantownWest VirginiaUSA
| | - Karen G. Lloyd
- Microbiology DepartmentUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Radamés J. B. Cordero
- Department of Molecular Microbiology and ImmunologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Claudia B. L. de Campos
- Institute of Science and TechnologyUniversidade Federal de Sao Paulo (UNIFESP)São José dos CamposSPBrazil
| | | | - Ricardo Amils
- Department of Molecular Biology, Centro de Biología Molecular Severo Ochoa (CSIC‐UAM)Nicolás Cabrera n° 1, Universidad Autónoma de MadridMadridSpain
- Department of Planetology and HabitabilityCentro de Astrobiología (INTA‐CSIC)Torrejón de ArdozSpain
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Tigre Moura F. Artificial Intelligence, Creativity, and Intentionality: The Need for a Paradigm Shift. JOURNAL OF CREATIVE BEHAVIOR 2023. [DOI: 10.1002/jocb.585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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12
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Abstract
Artificial intelligence (AI) is deemed to increase workers’ productivity by enhancing their creative abilities and acting as a general-purpose tool for innovation. While much is known about AI’s ability to create value through innovation, less is known about how AI’s limitations drive innovative work behaviour (IWB). With AI’s limits in perspective, innovative work behaviour might serve as workarounds to compensate for AI limitations. Therefore, the guiding research question is: How will AI limitations, rather than its apparent transformational strengths, drive workers’ innovative work behaviour in a workplace? A search protocol was employed to identify 65 articles based on relevant keywords and article selection criteria using the Scopus database. The thematic analysis suggests several themes: (i) Robots make mistakes, and such mistakes stimulate workers’ IWB, (ii) AI triggers ‘fear’ in workers, and this ‘fear’ stimulates workers’ IWB, (iii) Workers are reskilled and upskilled to compensate for AI limitations, (iv) AI interface stimulates worker engagement, (v) Algorithmic bias requires IWB, and (vi) AI works as a general-purpose tool for IWB. In contrast to prior reviews, which generally focus on the apparent transformational strengths of AI in the workplace, this review primarily identifies AI limitations before suggesting that the limitations could also drive innovative work behaviour. Propositions are included after each theme to encourage future research.
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Affiliation(s)
- Araz Zirar
- grid.15751.370000 0001 0719 6059Huddersfield Business School, University of Huddersfield, Huddersfield, UK
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13
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Zirar A. Can artificial intelligence’s limitations drive innovative work behaviour? REVIEW OF MANAGERIAL SCIENCE 2023. [PMCID: PMC9910241 DOI: 10.1007/s11846-023-00621-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Artificial intelligence (AI) is deemed to increase workers’ productivity by enhancing their creative abilities and acting as a general-purpose tool for innovation. While much is known about AI’s ability to create value through innovation, less is known about how AI’s limitations drive innovative work behaviour (IWB). With AI’s limits in perspective, innovative work behaviour might serve as workarounds to compensate for AI limitations. Therefore, the guiding research question is: How will AI limitations, rather than its apparent transformational strengths, drive workers’ innovative work behaviour in a workplace? A search protocol was employed to identify 65 articles based on relevant keywords and article selection criteria using the Scopus database. The thematic analysis suggests several themes: (i) Robots make mistakes, and such mistakes stimulate workers’ IWB, (ii) AI triggers ‘fear’ in workers, and this ‘fear’ stimulates workers’ IWB, (iii) Workers are reskilled and upskilled to compensate for AI limitations, (iv) AI interface stimulates worker engagement, (v) Algorithmic bias requires IWB, and (vi) AI works as a general-purpose tool for IWB. In contrast to prior reviews, which generally focus on the apparent transformational strengths of AI in the workplace, this review primarily identifies AI limitations before suggesting that the limitations could also drive innovative work behaviour. Propositions are included after each theme to encourage future research.
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Affiliation(s)
- Araz Zirar
- Huddersfield Business School, University of Huddersfield, Huddersfield, UK
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14
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Wingström R, Hautala J, Lundman R. Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists. CREATIVITY RESEARCH JOURNAL 2022. [DOI: 10.1080/10400419.2022.2107850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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15
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Samuel J, Kashyap R, Samuel Y, Pelaez A. Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2022. [DOI: 10.1016/j.ijinfomgt.2022.102505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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16
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Xiong P, Lee SMY, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022; 9:860032. [PMID: 35402563 PMCID: PMC8990170 DOI: 10.3389/fcvm.2022.860032] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
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Affiliation(s)
- Ping Xiong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
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17
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Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data. MATHEMATICS 2022. [DOI: 10.3390/math10040554] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Mistrust, amplified by numerous artificial intelligence (AI) related incidents, is an issue that has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and is fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle such an issue. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced into XAI. However, many deficiencies, particularly the lack of explanation assessment methods and uncertainty quantification, plague this young domain. In the present paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model and Shapley additive explanations (SHAP) to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as a marker for anomalies and expands the prognostic explanation scope to include the model’s confidence. In addition, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of studies on PHM-XAI. The quality of the explanation is examined employing local accuracy and consistency properties. The elaborated framework is tested on real-world gas turbine anomalies and synthetic turbofan failure prediction data. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed a 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.
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