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Zeitschriftenartikel zum Thema „Intelligence augmentée“

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Veröffentlicht am 25. Mai 2024

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1

Morillon, Laurent, und Arlette Bouzon. „Entre intelligence collective et porosité augmentée, étude d’un médiateur entre chercheurs et praticiens“. Communication et organisation, Nr. 47 (01.06.2015): 75–84. http://dx.doi.org/10.4000/communicationorganisation.4902.

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Ahmad, Zubair. „Artificial Intelligence or Augmented Intelligence?“ International Journal of Science and Research (IJSR) 12, Nr. 12 (05.12.2023): 1782–88. http://dx.doi.org/10.21275/sr231212220052.

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Khaddad, A., A. Bartoli, K. Chandelon, G. Margue, N. Bourdel und J. Bernhard. „Reconnaissance automatisée de la surface rénale par intelligence artificielle au cours de néphrectomies partielles robot-assistées (NPRA) : un pas vers la réalité augmentée“. Progrès en Urologie - FMC 33, Nr. 3 (November 2023): S46—S47. http://dx.doi.org/10.1016/j.fpurol.2023.07.069.

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Bonnaure, Pierre. „MALLARD Stéphane / ALEXANDRE Laurent (préf.), Disruption, Intelligence artificielle, fin du salariat, humanité augmentée. Préparez-vous à changer de monde, Paris : Dunod, mai 2018, 256 p.“ Futuribles N° 428, Nr. 1 (02.01.2019): IX. http://dx.doi.org/10.3917/futur.428.0131i.

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Jarrahi, Mohammad Hossein, Christoph Lutz und Gemma Newlands. „Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation“. Big Data & Society 9, Nr. 2 (Juli 2022): 205395172211428. http://dx.doi.org/10.1177/20539517221142824.

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There is little consensus on what artificial intelligence (AI) systems may or may not embrace. Although this may point to multiplicity of interpretations and backgrounds, a lack of conceptual clarity could thwart the development of common ground around the concept among researchers, practitioners and users of AI and pave the way for misinterpretation and abuse of the concept. This article argues that one of the effective ways to delineate the concept of AI is to compare and contrast it with human intelligence. In doing so, the article broaches the unique capabilities of humans and AI in relation to one another (human and machine tacit knowledge), as well as two types of AI systems: one that goes beyond human intelligence and one that is necessarily and inherently tied to it. It finally highlights how humans and AI can augment their capabilities and intelligence through synergistic human–AI interactions (i.e., human-augmented AI and augmented human intelligence), resulting in hybrid intelligence, and concludes with a future-looking research agenda.

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Cerf, Vinton G. „Augmented Intelligence“. IEEE Internet Computing 17, Nr. 5 (September 2013): 96. http://dx.doi.org/10.1109/mic.2013.90.

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7

Suman, Sristi. „Augmented Intelligence for National Security and Development“. Unity Journal 3, Nr. 01 (06.03.2022): 245–52. http://dx.doi.org/10.3126/unityj.v3i01.43329.

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Augmented Intelligence is a system model which centre on the partnership between human general intelligence & common sense with Artificial Intelligence’s exceptional processing speed and computation power to get better cognitive performance. This study can shed light on Augmented Intelligence and how it can be implemented so that we have heroes working at the peak human level possible, suffer fewer casualties, and also have equal footing in terms of technology and military strength. Nepal being open bordered country has a great threat from the border issues and with ever-rising crime and challenges in various cities creating intra threat. The study analyses to overcome this geographical challenge, operational problems, the rise of conflicts, and establish better policies, faced by security personnel with technology that actually empowers all the brave soldiers and Police officers to maintain security from both external and internal risk. In the ever-evolving technology limitations of human beings can be catastrophic, but replacing it all with Artificial Intelligence is not a practical solution as well because it would be giving the third entity all the ability that might revolt against humans themselves. The article can be summed up as, research on the practicality of Artificial Intelligence and Augmented Intelligence in national security and figure out a way for its legal adaptation. The availability of various case studies, surveys, research on various existing technology, and research papers published in renowned journals and publications has made this study possible.

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Rui, Yong. „From Artificial Intelligence to Augmented Intelligence“. IEEE MultiMedia 24, Nr. 1 (Januar 2017): 4–5. http://dx.doi.org/10.1109/mmul.2017.8.

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Lee, JoongHak, Jinwoo Jung und Taekyung Song. „Research on Personnel Selection in the Age of Artificial General Intelligence Focusing on Augmented Intelligence“. Korean Academy of Organization and Management 47, Nr. 2 (31.05.2023): 113–37. http://dx.doi.org/10.36459/jom.2023.47.2.113.

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This study is designed to actively consider how the use of artificial general intelligence, sparked by the rise of generative artificial intelligence (AI), can help organizations decide who to hire. For a long time, cognitive abilities have been used by organizations as an important selection factor and an important tool for predicting performance. However, ChatGPT, AutoGPT, BabyAGI, and others are heralding the rise of artificial general intelligence, which means that the cognitive skills required of employees will become less and less important. Therefore, through a literature review and expert discussions, this study proposes augmented intelligence as a new intelligence to consider when hiring. To do so, we first examine how organizations are applying cognitive abilities to hiring and outline the history of AI in three milestones. Then, we summarized experiments on the impact of the recently studied GPT-4 algorithm on work to show its impact on organizations, and summarized previous research to present the concept and possibilities of general AI and explain its limitations. Based on this, we conceptualized augmented intelligence as an intelligence that can understand, utilize, and make decisions about artificial general intelligence, and presented sub-factors and behavioral indicators to measure it. We also provided detailed skills, cognitive abilities, and knowledge that can be matched based on the U.S. occupational classification to measure and utilize augmented intelligence so that it can be quickly applied in academia and practice.

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von Ahn, Luis. „Augmented intelligence: the Web and human intelligence“. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, Nr. 1987 (28.03.2013): 20120383. http://dx.doi.org/10.1098/rsta.2012.0383.

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Adlassnig, Klaus-Peter. „Artificial-intelligence-augmented systems“. Artificial Intelligence in Medicine 24, Nr. 1 (Januar 2002): 1–4. http://dx.doi.org/10.1016/s0933-3657(01)00102-6.

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Skiba, Diane J. „Augmented Intelligence and Nursing“. Nursing Education Perspectives 38, Nr. 2 (2017): 108–9. http://dx.doi.org/10.1097/01.nep.0000000000000124.

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Cappel, Christian, Ranna Iraqi und und Maik Lachmann. „Augmented Intelligence im Controlling“. Controlling 35, Nr. 2 (2023): 50–56. http://dx.doi.org/10.15358/0935-0381-2023-2-50.

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Mittels einer qualitativen Fallstudie in einem Industrieunternehmen beleuchtet dieser Beitrag Faktoren, welche die Mitarbeiterakzeptanz von KI-Systemen im Controlling fördern bzw. hemmen. Zudem werden Maßnahmen im Zuge der Implementierung von KI-Systemen aufgezeigt, die diese Akzeptanz steigern können, sodass Potenziale durch eine Automatisierung der Controlling-Prozesse ausgeschöpft werden.

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Jain, Hemant, Balaji Padmanabhan, Paul A. Pavlou und T. S. Raghu. „Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society“. Information Systems Research 32, Nr. 3 (September 2021): 675–87. http://dx.doi.org/10.1287/isre.2021.1046.

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Recent developments in artificial intelligence (AI) have increased interest in combining AI with human intelligence to develop superior systems that augment human and artificial intelligence. In this paper, augmented intelligence informally means computers and humans working together, by design, to enhance one another, such that the intelligence of the resulting system improves. Intelligence augmentation (IA) can pool the joint intelligence of humans and computers to transform individual work, organizations, and society. Notably, applications of IA are beginning to emerge in several domains, such as cybersecurity, privacy, counterterrorism, and healthcare, among others. We provide a brief summary of papers in this special section that represent early attempts to address some of the rapidly emerging research issues. We also present a framework to guide research on IA and advocate for the important implications of IA for the future of work, organizations, and society. We conclude by outlining promising research directions based on this framework for the information systems and related disciplines.

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Amit Dubey , Sanjeev Kumar Sharma , Saurabh Karsoliya, Prashant Chandra. „A novel Conceptualization of AI Literacy and Empowering Employee Experience at Digital Workplace Using Generative AI and Augmented Analytics: A Survey“. Journal of Electrical Systems 20, Nr. 2 (04.04.2024): 2582–603. http://dx.doi.org/10.52783/jes.2031.

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With the fast, rapid, and expeditious integration of Artificial Intelligence (AI) technologies, particularly Generative AI and Augmented Analytics, organizations are presented with new opportunities to transform their operations and empower their workforce. This paper explores the intersection of AI literacy, Generative AI, and Augmented Analytics to propose strategies for fostering a culture of AI fluency among employees. This paper reviews the literature on AI literacy and its potential implications for employee experience (Ex) in digital workplaces. AI literacy and competency is the ability to understand, interact with, and thoughtfully assess the applications and ramifications of artificial intelligence (AI) across diverse domains. The paper argues that AI literacy is a key competence for employees in the digital era, as it enables them to leverage the potential of generative AI and augmented analytics, two of the most promising technologies for enhancing Ex. Generative AI refers to the use of AI to create novel and diverse outputs, such as text, images, music, or designs, while augmented analytics drives potential capability to make use of Artificial Intelligence technologies to automate and augment data analysis and decision-making. The paper discusses how these technologies can empower employees to be more creative, productive, collaborative, and engaged in their work and the challenges and risks they pose. This paper additionally highlights the obstacles, deficiencies, and proposed pathways for further research advancement concerning AI technological competency and literacy, aiming to enhance the employee experience significantly in digital workplaces.

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Khan, Tanveer, Masoom Alam, Adnan Akhunzada, Ali Hur, Muhammad Asif und Muhammad Khurram Khan. „Towards augmented proactive cyberthreat intelligence“. Journal of Parallel and Distributed Computing 124 (Februar 2019): 47–59. http://dx.doi.org/10.1016/j.jpdc.2018.10.006.

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Ilyas, Mohammad. „Augmented Intelligence for Advancing Healthcare“. Journal of Systemics, Cybernetics and Informatics 20, Nr. 6 (Dezember 2022): 13–19. http://dx.doi.org/10.54808/jsci.20.06.13.

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Augmented Intelligence (AuI) is being integrated in a variety of our daily activities and applications. Healthcare is one of those applications and, in fact, it will greatly benefit from AuI leading to impressive improvements. Although, as with the use of any new innovations, the use of AuI in healthcare is not without concerns, the benefits are expected to outweigh the concerns. It is also expected and adequate education about AuI related innovations in healthcare and their potential benefits, will build trust among consumers and alleviate perceived concerns. For the process to be relatively smooth, an interdisciplinary dialogue must take place between medical professionals, AuI experts, engineering professionals, and consumers. This paper discusses a brief history of AI and AuI, its potential applications in healthcare, potential benefits that all stakeholders will reap, and potential challenges that we will need to overcome, to fully benefit from using AuI in healthcare.

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Duong, Michael Tran, Andreas M. Rauschecker, Jeffrey D. Rudie, Po-Hao Chen, Tessa S. Cook, R. Nick Bryan und Suyash Mohan. „Artificial intelligence for precision education in radiology“. British Journal of Radiology 92, Nr. 1103 (November 2019): 20190389. http://dx.doi.org/10.1259/bjr.20190389.

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In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution’s practice. The coming age of “AI-augmented radiology” may enable not only “precision medicine” but also what we describe as “precision medical education,” where instruction is tailored to individual trainees based on their learning styles and needs.

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Ehrenfeld, Jesse M. „Artificial Intelligence versus Augmented Intelligence: What's in a Word?“ Biomedical Instrumentation & Technology 56, Nr. 4 (Januar 2022): 130–31. http://dx.doi.org/10.2345/0899-8205-56.4.130.

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Pal Singh Shobhit, Jatin. „Automating Threat Intelligence Analysis with Retrieval Augmented Generation (RAG) for Enhanced Cybersecurity Posture“. International Journal of Science and Research (IJSR) 13, Nr. 5 (05.05.2024): 251–55. http://dx.doi.org/10.21275/sr24502103758.

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Marwaha, Shruti, A. K. Sinha und Ramesh Sahani. „Cognitive Development and Academic Achievement Extent of Association - A Review Report“. International Journal of Research in Informative Science Application & Techniques (IJRISAT) 3, Nr. 4 (10.04.2019): 68–107. http://dx.doi.org/10.46828/ijrisat.v3i4.83.

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Education, cognition and learning have become integral part of every child’s personality. These inseparable aspects are considerably vital in shaping the individual and helping him to achieve success. In the schools and educational institutes where education has been taken on priority, the cognitive abilities and excellence in mastering them mitigate the burdens and stress in educational system. In this context, it is assumed that when the children are taught as per their affinity and dominant multiple intelligence, they can learn better and turn into efficient learners. This review paper has been written in order to compile the augmented knowledge into systematic and scientific information to ensure that the educational processes can become lucid for masses. The valid studies related to cognitive development including intelligence quotient, focus factor, decision making ability, creative quotient and academic achievement in light of multiple intelligences were selected. The review studies have been taken on international basis for universal application. It was inferred after the review that the concept of teaching students through multiple intelligences approach should be adopted to make create a meaning and impactful teaching learning process.

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Bagnato, Vincenzo Paolo. „Artificial Intelligence for Design The Artificial Intelligence of Objects“. Venturing into the Age of AI: Insights and Perspectives, Nr. 27 (01.10.2023): 30–35. http://dx.doi.org/10.37199/f40002705.

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Artificial Intelligence (AI) is increasingly permeating various domains, revolutionizing traditional processes and opening new avenues for innovation. In the realm of design, AI holds immense promise, offering capabilities to augment human creativity, streamline workflows, and deliver personalized user experiences. This paper explores the intersection of AI and design, highlighting its transformative potential and key implications. Firstly, for instance, AI-powered design tools automate repetitive tasks, freeing up designers' time for more creative endeavors, while generative design algorithms can generate myriad design options based on specified constraints, enabling designers to explore more efficiently brand-new solutions. Additionally, AI-driven predictive analytics provide insights into user preferences and market trends, facilitating informed design decisions and enhancing product relevance; moreover, AI enables the creation of personalized user experiences by analyzing vast datasets to understand individual preferences and behaviors: this personalization extends across various design domains, from user interfaces and digital products to physical spaces and services. Ethical considerations are paramount in the integration of AI into design processes: designers must address issues of transparency, fairness, and bias to ensure that AI-driven solutions uphold ethical standards and respect user privacy; furthermore, human-AI collaboration emerges as a fundamental paradigm, where AI augments human creativity and decision-making rather than replacing it entirely. Challenges such as technical complexity, data privacy concerns, and the need for upskilling within the design workforce accompany the integration of AI in design; thus, addressing these challenges requires interdisciplinary collaboration and a holistic approach encompassing technological, ethical, and socio-economic dimensions. In this framework, this paper sets the stage for further exploration of AI's transformative impact on design and the ethical considerations guiding its integration into practice.

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Xie, Guotong, Tiange Chen, Yingxue Li, Tingyu Chen, Xiang Li und Zhihong Liu. „Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists’ Intelligence?“ Kidney Diseases 6, Nr. 1 (03.12.2019): 1–6. http://dx.doi.org/10.1159/000504600.

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Cuillandre, Hervé. „Intelligence artificielle : homme augmenté ou homme substitué ?“ Humanisme N° 316, Nr. 3 (03.07.2017): 25–30. http://dx.doi.org/10.3917/huma.316.0025.

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Nishida, Toyoaki. „Augmenting Conversational Environment“. International Journal of Cognitive Informatics and Natural Intelligence 6, Nr. 4 (Oktober 2012): 103–24. http://dx.doi.org/10.4018/jcini.2012100105.

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People are proficient in collaboratively forming and maintaining gatherings thereby shaping and cultivating collective thoughts through fluent conversational interactions. A big challenge is to develop a technology for augmenting the conversational environment so that people can conduct even better conversational interactions for collective intelligence and creation. Conversational informatics is a field of research that focuses on investigating conversational interactions and designing intelligent artifacts that can augment conversational interactions. The field draws on a foundation provided by artificial intelligence, natural language processing, speech and image processing, cognitive science, and conversation analysis. In this article, the author overviews a methodology for developing augmented conversational environment and major achievements. The author also discusses issues for making agents empathic so that they can induce sustained and constructive engagement with people.

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Imran, Sheik, Rekha C und Reshma C R. „Advanced Virtual Fitting Rooms: A Review of Underlying Artificial Intelligence Technologies and Augmented Reality“. International Journal of Science and Research (IJSR) 12, Nr. 10 (05.10.2023): 412–16. http://dx.doi.org/10.21275/sr231003234825.

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Honarmand, Melika. „Augmented Machine Intelligence with Human Intelligence for Cryptocurrency Price Prediction“. International Journal of Machine Learning 14, Nr. 1 (2024): 7–11. http://dx.doi.org/10.18178/ijml.2024.14.1.1150.

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This research explores the integration of human and machine intelligence in the financial industry. While AI systems excel at analyzing data, humans possess unique traits that contribute to accurate predictions. Inspired by the concept of Augmented Financial Intelligence, the study aims to integrate human intelligence with existing models, considering the limitations of current human input methods. Natural Language Processing and Sentiment Analysis are used to enhance prediction tasks, but limited training data and suboptimal processing can introduce noise. The study introduces a framework that integrates human and machine intelligence to enhance cryptocurrency market forecasts, analyzing six cryptocurrencies and utilizing an Elo-based rating system to identify exceptional predictors. The framework aims to minimize noise and optimize human input in financial forecasting.

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Nelson, Scott D., Colin G. Walsh, Casey A. Olsen, Andrew J. McLaughlin, Joseph R. LeGrand, Nick Schutz und Thomas A. Lasko. „Demystifying artificial intelligence in pharmacy“. American Journal of Health-System Pharmacy 77, Nr. 19 (04.07.2020): 1556–70. http://dx.doi.org/10.1093/ajhp/zxaa218.

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Abstract Purpose To provide pharmacists and other clinicians with a basic understanding of the underlying principles and practical applications of artificial intelligence (AI) in the medication-use process. Summary “Artificial intelligence” is a general term used to describe the theory and development of computer systems to perform tasks that normally would require human cognition, such as perception, language understanding, reasoning, learning, planning, and problem solving. Following the fundamental theorem of informatics, a better term for AI would be “augmented intelligence,” or leveraging the strengths of computers and the strengths of clinicians together to obtain improved outcomes for patients. Understanding the vocabulary of and methods used in AI will help clinicians productively communicate with data scientists to collaborate on developing models that augment patient care. This primer includes discussion of approaches to identifying problems in practice that could benefit from application of AI and those that would not, as well as methods of training, validating, implementing, evaluating, and maintaining AI models. Some key limitations of AI related to the medication-use process are also discussed. Conclusion As medication-use domain experts, pharmacists play a key role in developing and evaluating AI in healthcare. An understanding of the core concepts of AI is necessary to engage in collaboration with data scientists and critically evaluating its place in patient care, especially as clinical practice continues to evolve and develop.

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Zheng, Nan-ning, Zi-yi Liu, Peng-ju Ren, Yong-qiang Ma, Shi-tao Chen, Si-yu Yu, Jian-ru Xue, Ba-dong Chen und Fei-yue Wang. „Hybrid-augmented intelligence: collaboration and cognition“. Frontiers of Information Technology & Electronic Engineering 18, Nr. 2 (Februar 2017): 153–79. http://dx.doi.org/10.1631/fitee.1700053.

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Hellebrandt, Thomas, Louis Huebser, Tobias Adam, Ina Heine und Robert H. Schmitt. „Augmented Intelligence – Mensch trifft Künstliche Intelligenz“. Zeitschrift für wirtschaftlichen Fabrikbetrieb 116, Nr. 6 (01.06.2021): 433–37. http://dx.doi.org/10.1515/zwf-2021-0104.

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Abstract Künstliche Intelligenz (KI) gilt in vielen Bereichen als bahnbrechend, da die kognitive Leistung des Menschen reproduziert oder sogar übertroffen werden kann. Zunehmend findet KI nun auch Anwendung in der industriellen Produktion. Bisherige Applikationen haben jedoch gezeigt, dass ein vollständig automatisierter Einsatz von KI für die Entscheidungsfindung nicht ausreichend ist. Stattdessen geht der Ansatz der Augmented Intelligence davon aus, dass sich menschliche und maschinelle Intelligenz positiv ergänzen, um effizienter und zuverlässiger Entscheidungen zu treffen und daraus Handlungen abzuleiten. In diesem Beitrag werden sowohl das Konzept hinter Augmented Intelligence vorgestellt als auch vielversprechende Anwendungsfälle in der industriellen Produktion exemplarisch aufgezeigt. Ebenso werden die organisationalen bzw. betrieblichen Rahmenbedingungen diskutiert und Empfehlungen für eine erfolgreiche Umsetzung gegeben.

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Fournier, Martine. „Intelligence . Peut-on augmenter nos capacités ?“ Sciences Humaines N° 241, Nr. 10 (01.10.2012): 5. http://dx.doi.org/10.3917/sh.241.0005.

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Petrotta, Mark, und Troy Peterson. „Implementing Augmented Intelligence In Systems Engineering“. INCOSE International Symposium 29, Nr. 1 (Juli 2019): 543. http://dx.doi.org/10.1002/j.2334-5837.2019.00619.x.

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Barber, Kirk. „Augmented Intelligence—JCMS 2022“. Journal of Cutaneous Medicine and Surgery 27, Nr. 3 (Mai 2023): 212–13. http://dx.doi.org/10.1177/12034754231176616.

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M, Devi, Renukasrinidhi N und Swarnamaalika N. Mrs P. Rekha. „An intelligent device control system using augmented reality and zigbee technology“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30.04.2018): 1725–28. http://dx.doi.org/10.31142/ijtsrd11435.

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Nikonov, Oleh. „Concept of convergence of augmented reality technologies and artificial intelligence for spe-cial purpose vehicles“. Bulletin of Kharkov National Automobile and Highway University, Nr. 95 (16.12.2021): 235. http://dx.doi.org/10.30977/bul.2219-5548.2021.95.0.235.

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The use of artificial intelligence is a modern important trend in the creation of promising information and control systems of vehicles, including special purpose. The high demands on augmented reality software simply cannot rely solely on human programming to display virtual objects against the real world. Neural networks and machine learning can perform these tasks with much greater efficiency and can greatly improve the augmented reality experience. Goal. The purpose of the article is to develop the concept of convergence of augmented reality and artificial intelligence technologies for special purpose vehicles. Methodology. Artificial intelligence technologies contribute to the transformation of the economy, labor market, government institutions and society as a whole. The use of artificial intelligence technologies helps to reduce costs, increase production efficiency, quality of goods and services. Augmented reality technology offers more innovative methods of visualization by expanding the boundaries of reality, controlling the perspective of the object and visualization in a real context. Results. The concept of convergence of augmented reality and artificial intelligence technologies for special purpose vehicles based on a synergetic approach has been developed. An integrated intelligent information and control system for special purpose vehicles with augmented reality technology has been developed. Originality. Using the convergence of augmented reality and artificial intelligence technologies for special purpose vehicles. Practical value. Convergence is the foundation for achieving numerous positive social and economic effects. The use of augmented reality technology and artificial intelligence can significantly increase the efficiency of special purpose vehicles.

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Alhamuddin, Alhamuddin, Dinar Nur Inten, Dewi Mulyani, Asep Dadan Suganda, Juhji Juhji, Veena Prachagool und Prasart Nuangchalerm. „Multiple intelligence-based differential learning on critical thinking skills of higher education students“. International Journal of ADVANCED AND APPLIED SCIENCES 10, Nr. 8 (August 2023): 132–39. http://dx.doi.org/10.21833/ijaas.2023.08.015.

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This research aims to scrutinize the impact of multiple intelligences-based differential learning on the enhancement of critical thinking skills among university students. A cohort of sixty-six teacher students actively participated in this empirical investigation. Critical thinking proficiency was meticulously assessed employing a battery of tests and a comprehensive questionnaire. The findings of this study unequivocally demonstrate that multiple intelligences-based differential learning exerts a discernible influence on the development of critical thinking abilities among university students. Intriguingly, the study found no significant difference in critical thinking performance among the various types of intelligence. Additionally, this research posits that differential learning grounded in multiple intelligences represents a viable alternative pedagogical approach that should be earnestly embraced by educators when devising and implementing instructional strategies and methodologies. Such an approach, the study contends, holds the potential to augment students' cognitive capacities in a manner that aligns with the exigencies of 21st-century education.

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Stephen, Andrew T. „Here Comes the Hyper-Connected Augmented Consumer“. GfK Marketing Intelligence Review 9, Nr. 2 (27.11.2017): 10–17. http://dx.doi.org/10.1515/gfkmir-2017-0012.

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Abstract Consumers have become always on and constantly connected. Search costs have plummeted, individuals’ abilities to digitally express themselves and their opinions increased, and the opportunities for superior business and market intelligence for companies have skyrocketed. This has given rise to more, richer, and new sources of consumer data that marketers can leverage, and has fueled the data-driven insights revolution in marketing. But there is more to come very soon. In marketing, we are quickly moving from the age of the connected consumer to the age of the augmented consumer. New technologies like wearable devices, smart sensors, consumer IoT devices, smart homes, and, critically, artificial intelligence ecosystems will not only connect, but will substantially and meaningfully augment the consumer in terms of their thoughts and behaviors. The biggest challenge for marketers will lie in how they approach marketing to this new type of consumer, particularly personal artificial intelligence ecosystems. This means marketing to algorithms, instead of people, and that is very different to how most marketing work is currently done.

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Warner, Mike. „Satori: Toward a Global Augmented Intelligence Architecture“. Journal of Investing 28, Nr. 3 (31.03.2019): 88–99. http://dx.doi.org/10.3905/joi.2019.28.3.088.

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Xue, Jianru, Bin Hu, Lingxi Li und Junping Zhang. „Human—machine augmented intelligence: research and applications“. Frontiers of Information Technology & Electronic Engineering 23, Nr. 8 (August 2022): 1139–41. http://dx.doi.org/10.1631/fitee.2250000.

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Currie, Geoffrey M. „Intelligent Imaging: Artificial Intelligence Augmented Nuclear Medicine“. Journal of Nuclear Medicine Technology 47, Nr. 3 (10.08.2019): 217–22. http://dx.doi.org/10.2967/jnmt.119.232462.

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Pruinelli, Lisiane, und Martin Michalowski. „Toward an Augmented Nursing-Artificial Intelligence Future“. CIN: Computers, Informatics, Nursing 39, Nr. 6 (Juni 2021): 296–97. http://dx.doi.org/10.1097/cin.0000000000000784.

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Ting, Daniel S. W., und Lama A. Al-Aswad. „Augmented Intelligence in Ophthalmology: The Six Rights“. Asia-Pacific Journal of Ophthalmology 10, Nr. 3 (Mai 2021): 231–33. http://dx.doi.org/10.1097/apo.0000000000000410.

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Patel, Rohini, Arielle Lee, John Hallsten, John Lane, Andrew Barleben und Mahmoud Malas. „Augmented Intelligence Maps Can Improve Intraoperative Outcomes“. Journal of Vascular Surgery 76, Nr. 4 (Oktober 2022): e86-e87. http://dx.doi.org/10.1016/j.jvs.2022.07.104.

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Kromenacker, Bryan, Melody Maarouf und Vivian Yan Shi. „Augmented Intelligence in Dermatology: Fantasy or Future?“ Dermatology 235, Nr. 3 (2019): 250–52. http://dx.doi.org/10.1159/000497275.

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Moawad, Gaby, Paul Tyan und Michelle Louie. „Artificial intelligence and augmented reality in gynecology“. Current Opinion in Obstetrics and Gynecology 31, Nr. 5 (Oktober 2019): 345–48. http://dx.doi.org/10.1097/gco.0000000000000559.

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Kovarik, Carrie, Ivy Lee, Justin Ko, Adewole Adamson, Clark Otley, Joseph Kvedar, Priyank Vedak, Susan Huang, Matthew Fitzgerald und Rachna Chaudhari. „Commentary: Position statement on augmented intelligence (AuI)“. Journal of the American Academy of Dermatology 81, Nr. 4 (Oktober 2019): 998–1000. http://dx.doi.org/10.1016/j.jaad.2019.06.032.

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Yu, Yipeng, Gang Pan, Yongyue Gong, Kedi Xu, Nenggan Zheng, Weidong Hua, Xiaoxiang Zheng und Zhaohui Wu. „Intelligence-Augmented Rat Cyborgs in Maze Solving“. PLOS ONE 11, Nr. 2 (09.02.2016): e0147754. http://dx.doi.org/10.1371/journal.pone.0147754.

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Sabry Abdel-Hamid Helwa, Hasnaa. „Artificial Intelligence Versus Augmented Reality in Instruction“. Sustainability Education Globe 1, Nr. 1 (01.12.2023): 1–10. http://dx.doi.org/10.21608/seg.2023.340083.

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Heer, Jeffrey. „Agency plus automation: Designing artificial intelligence into interactive systems“. Proceedings of the National Academy of Sciences 116, Nr. 6 (04.02.2019): 1844–50. http://dx.doi.org/10.1073/pnas.1807184115.

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Much contemporary rhetoric regards the prospects and pitfalls of using artificial intelligence techniques to automate an increasing range of tasks, especially those once considered the purview of people alone. These accounts are often wildly optimistic, understating outstanding challenges while turning a blind eye to the human labor that undergirds and sustains ostensibly “automated” services. This long-standing focus on purely automated methods unnecessarily cedes a promising design space: one in which computational assistance augments and enriches, rather than replaces, people’s intellectual work. This tension between human agency and machine automation poses vital challenges for design and engineering. In this work, we consider the design of systems that enable rich, adaptive interaction between people and algorithms. We seek to balance the often-complementary strengths and weaknesses of each, while promoting human control and skillful action. We share case studies of interactive systems we have developed in three arenas—data wrangling, exploratory analysis, and natural language translation—that integrate proactive computational support into interactive systems. To improve outcomes and support learning by both people and machines, we describe the use of shared representations of tasks augmented with predictive models of human capabilities and actions. We conclude with a discussion of future prospects and scientific frontiers for intelligence augmentation research.

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Nelson, Caroline A., Swapna Pachauri, Rosie Balk, Jeffrey Miller, Rushan Theunis, Justin M. Ko und Carrie L. Kovarik. „Dermatologists’ Perspectives on Artificial Intelligence and Augmented Intelligence — A Cross-sectional Survey“. JAMA Dermatology 157, Nr. 7 (01.07.2021): 871. http://dx.doi.org/10.1001/jamadermatol.2021.1685.

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