[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124205-en":3,"doc-seo-124205-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124205,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Augmented intelligence with voice assistance and automated machine learning in Industry 5.0","Augmented intelligence integrates human and artificial agents into socio-technical systems that co-evolve through learning and decision optimization via intuitive interfaces, including conversational voice-enabled technologies. Existing voice-assistant research often emphasizes knowledge management and simulation rather than data-driven algorithms, and real-world practical evaluation remains limited. The study proposes combining voice assistance with Automated Machine Learning (AutoML) to realize augmented intelligence in Industry 5.0.","TYPE Original Research PUBLISHED 04 March 2025 DOI 10.3389/frai.2025.1538840  \nOPEN ACCESS  \nEDITED BY  \nAmir Zadeh,  \nWright State University, United States  \nREVIEWED BY  \nEvren Şadi Şeker,  \nIstanbul University, Türkiye Dimitris Apostolou, University of Piraeus, Greece  \n*CORRESPONDENCE  \nAlexandros Bousdekis  \n [albous@mail.ntua.gr](albous@mail.ntua.gr)  \nRECEIVED 03 December 2024  \nACCEPTED 07 February 2025  \nPUBLISHED 04 March 2025  \nCITATION  \nBousdekis A, Foosherian M, Fikardos M, Wellsandt S, Lepenioti K, Bosani E, Mentzas G and Thoben K-D (2025)  \nAugmented intelligence with voice assistance and automated machine learning in Industry 5.0.  \nFront. Artif. Intell. 8:1538840 .  \ndoi: 10.3389/frai.2025.1538840  \nCOPYRIGHT  \n© 2025 Bousdekis, Foosherian, Fikardos, Wellsandt, Lepenioti, Bosani, Mentzas and Thoben. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAugmented intelligence with voice assistance and automated machine learning in Industry 5.0  \nAlexandros Bousdekis 1*, Mina Foosherian 2, Mattheos Fikardos 1, Stefan Wellsandt 2, Katerina Lepenioti 1, Enrica Bosani3, Gregoris Mentzas 1 and Klaus-Dieter Thoben 2  \n1 Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Athens, Greece, 2 BIBA-Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Germany, 3 Beko Europe, Varese, Italy  \nAugmented intelligence puts together human and artificial agents to create a socio-technological system, so that they co-evolve by learning and optimizing decisions through intuitive interfaces, such as conversational, voice-enabled interfaces. However, existing research works on voice assistants relies on knowledge management and simulation methods instead of data-driven algorithms. In addition, practical application and evaluation in real-life scenarios are scarce and limited in scope. In this paper, we propose the integration of voice assistance technology with Automated Machine Learning (AutoML) in order to enable the realization of the augmented intelligence paradigm in the context of Industry 5.0. In this way, the user is able to interact with the assistant through Speech-To-Text (STT) and Text-To-Speech (TTS) technologies, and consequently with the Machine Learning (ML) pipelines that are automatically created with AutoML, through voice in order to receive immediate insights while performing their task. The proposed approach was evaluated in a real manufacturing environment. We followed a structured evaluation methodology, and we analyzed the results, which demonstrates the effectiveness of our proposed approach.  \nKEYWORDS  \ndigital intelligent assistant, automated machine learning, voice assistance, human-AI collaboration, artificial intelligence, smart manufacturing  \n1 Introduction  \nIndustry 5.0 relies on placing human well-being at the center of manufacturing systems (Leng et al., 2022) and has recently been attracting the attention of researchers and practitioners in terms of both social and technological aspects (Leng et al., 2022) . Human-centric manufacturing is a prerequisite for factories aiming at achieving flexibility, agility, and robustness against disruptions (Nguyen Ngoc et al., 2022; Wang et al., 2022; Bousdekis et al., 2020). From the technological perspective, enabling technologies, such as human-machine interaction, that combine the strengths of humans and machines as well as big data analytics for providing data-driven insights for advanced manufacturing systems, leading to actionable intelligence","cbCainjPlQJ6wMQp","https://ap.wps.com/l/cbCainjPlQJ6wMQp","pdf",3170204,1,22,"English","en",105,"# Introduction\n## Industry 5.0 and human-centric manufacturing\n## Voice-enabled assistants and human-machine interaction\n## AutoML for data-driven decision-making","[{\"question\":\"What problem does the paper address regarding current voice assistant research?\",\"answer\":\"It highlights that existing work relies more on knowledge management and simulation than on data-driven algorithms, and that practical evaluation in real scenarios is scarce.\"},{\"question\":\"How does the proposed approach integrate voice assistance with AutoML?\",\"answer\":\"It connects speech input and output (Speech-To-Text and Text-To-Speech) with Machine Learning pipelines automatically created by AutoML, enabling voice-driven interaction and immediate insights.\"},{\"question\":\"Where was the proposed approach evaluated and what were the results?\",\"answer\":\"The approach was evaluated in a real manufacturing environment using a structured evaluation methodology, and the analyzed results demonstrate its effectiveness.\"}]","Augmented intelligence with voice assistance and automated machine learning in Industry 5.0 | 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