Готові списки джерел за темами / AI readiness

Добірка наукової літератури з теми "AI readiness"

Автор: Grafiati
Опубліковано: 19 червня 2021
Оновлено: 5 лютого 2022

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "AI readiness".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Статті в журналах з теми "AI readiness":

1

Dai, Yun, Ching-Sing Chai, Pei-Yi Lin, Morris Siu-Yung Jong, Yanmei Guo, and Jianjun Qin. "Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age." Sustainability 12, no. 16 (August 14, 2020): 6597. http://dx.doi.org/10.3390/su12166597.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
This study developed and validated an instrument to measure students’ readiness to learn about artificial intelligence (AI). The designed survey questionnaire was administrated in a school district in Beijing after an AI course was developed and implemented. The collected data and analytical results provided insights regarding the self-reported perceptions of primary students’ AI readiness and enabled the identification of factors that may influence this parameter. The results indicated that AI literacy was not predictive of AI readiness. The influences of AI literacy were mediated by the students’ confidence and perception of AI relevance. The students’ AI readiness was not influenced by a reduction in their anxiety regarding AI and an enhancement in their AI literacy. Male students reported a higher confidence, relevance, and readiness for AI than female students did. The sentiments reflected by the open-ended responses of the students indicated that the students were generally excited to learn about AI and viewed AI as a powerful and useful technology. The student sentiments confirmed the quantitative findings. The validated survey can help teachers better understand and monitor students’ learning, as well as reflect on the design of the AI curriculum and the associated teaching effectiveness.
2

Lemay, David J., Ram B. Basnet, and Tenzin Doleck. "Fearing the Robot Apocalypse: Correlates of AI Anxiety." International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI) 2, no. 2 (August 27, 2020): 24. http://dx.doi.org/10.3991/ijai.v2i2.16759.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
This study examines the relationship between individuals’ beliefs about AI (Artificial Intelligence) and levels of anxiety with respect to their technology readiness level. In this cross-sectional study, we surveyed 65 students at a southwestern US college. Using partial least squares analysis, we found that technology readiness contributors were significantly and positively related to only one AI anxiety factor: socio-technical illiteracy. In contrast, all four links between technology readiness inhibitors and AI anxiety factors were significant with medium effect sizes. Technology readiness inhibitors are positively related to learning, fears of job replacement, socio-technical illiteracy, and particular AI configurations. Thus, we conclude that AI anxiety runs through a spectrum. It is influenced by real, practical consequences of immediate effects of increased automatization but also by popular representations and discussions of the negative consequences of artificial general intelligence and killer robots and addressing technology readiness is unlikely to mitigate effects of AI anxiety.
3

Dudnik, Olesya, Marina Vasiljeva, Nikolay Kuznetsov, Marina Podzorova, Irina Nikolaeva, Larisa Vatutina, Ekaterina Khomenko, and Marina Ivleva. "Trends, Impacts, and Prospects for Implementing Artificial Intelligence Technologies in the Energy Industry: The Implication of Open Innovation." Journal of Open Innovation: Technology, Market, and Complexity 7, no. 2 (June 12, 2021): 155. http://dx.doi.org/10.3390/joitmc7020155.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
This research aims to substantiate the impact of using open innovation (OI) in the energy sector in readiness to implement artificial intelligence (AI) technologies and their effectiveness. The empirical method was proposed to determine the readiness level of OI for the implementation of AI technologies by comparing Russian and French energy companies. Readiness level indicators of companies for AI implementation using the Fibonacci sequence, Student’s t-test, and the method of fuzzy sets were empirically determined. The integrated readiness indicator for AI implementation by companies was calculated using the method of fuzzy sets and expressed through variance, allowing for these significant factors. Russian companies are at a low level of developmental readiness to implement AI, which is in contrast to companies operating in a developed market where the determining factor is the AI technology cost. The example of the innovative business model “Energy-as-a-Service” shows the synergistic effects of OI use and AI technology introduction. This paper is novel because it seeks to contribute to the current debate in the literature, justifying the position that energy companies that have in the past actively applied the concept of open innovation in business, are the most competitive and most efficient in implementing AI technologies.
4

Ovcharenko, Natalia, Olha Matveieva, and Olha Chebotarenko. "Metodological readiness formation of future music art teachers for their professional activity." Revista Amazonia Investiga 9, no. 27 (March 21, 2020): 157–64. http://dx.doi.org/10.34069/ai/2020.27.03.16.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
The article highlights the ways of solving the current problem of artistic education in Ukraine - methodological readiness formation of future music art teachers for their professional activity. The purpose of the study is experimental verification of the organizational and methodical principles of the methodological readiness formation of future music art teachers for their further professional activity, which is considered as: professional and personal formation of a teacher based on the motivation to such a kind of activity, a complex of methodological knowledge, skills and psychological capacities of the music art teacher to apply them. The research covers the structure of the methodological readiness of the music art teacher for their professional activity, which includes the following components: motivation-demanding, informational-cognitive, scientific activity-based, reflexive-evaluational, self-improving; also certain criteria, indicators and levels of methodological readiness formation of the music art teacher are determined in particular and the organizational and methodical principles of forming the methodological readiness of future music art teachers, based on the modernization of the content, forms and methods of methodological training of future music art teachers. The effectiveness of certain organizational and methodical principles has been confirmed by experimental research.
5

Bekirova, Elmira Sh, Meliya N. Harabadjah, Juliya V. Makarenko, Olga I. Vaganova, and Ludmila A. Sundeeva. "Model formation of future teachers readiness to innovative activity." Revista Amazonia Investiga 9, no. 29 (May 18, 2020): 22–28. http://dx.doi.org/10.34069/ai/2020.29.05.3.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
The relevance of the study is justified by the new requirements for the personality of a specialist in the field of education. Thus, an obligatory component of professional and pedagogical competence is the innovative competence of the teacher. The formation of this competence in future teachers in the system of their professional training in higher education requires special methodological system of innovative competence formation. The purpose of this article is to reveal the essence and to describe the model of readiness to innovative activity of future teachers. The purpose of the article is realized by applying theoretical (spectral analysis, generalization) and empirical (modeling, methods of pedagogical diagnostics) methods of scientific knowledge in the research. On the basis of the application of these methods in the study, the essence, features and structure of pedagogical innovative activity are determined. The necessity of innovative activity readiness formation of future teachers is substantiated. The components of the structure of teacher's readiness for innovative activity are described. The essence of pedagogical formation model of future teachers readiness to innovative activity is described. Its efficiency on the basis of quantitative and qualitative results is proved. The elements of scientific novelty in the work are represented by the creation of an experimental pedagogical model of the formation of the future teachers’ readiness to innovative activity, the substantiation of methodological and methodical aspects of its implementation, the development of the criteria base for determining the level of formation of the future teachers’ readiness to innovative activity.
6

Tint, Barbara S., Viv McWaters, and Raymond van Driel. "Applied improvisation training for disaster readiness and response." Journal of Humanitarian Logistics and Supply Chain Management 5, no. 1 (April 7, 2015): 73–94. http://dx.doi.org/10.1108/jhlscm-12-2013-0043.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Purpose – The purpose of this paper is to introduce applied improvisation (AI) as a tool for training humanitarian aid workers. AI incorporates principles and practices from improvisational theatre into facilitation and training. It is an excellent modality for training aid workers to deal with crisis and disaster scenarios where decision-making and collaboration under pressure are critical. Design/methodology/approach – This paper provides a theoretical base for understanding skills needed in disaster response and provides a case for innovative training that goes beyond the current standard. AI principles, activities and case examples are provided. Interviews with development experts who have participated in AI training are excerpted to reveal the impact and promise of this methodology. Findings – Different from typical training and games, which simulate potential crisis scenarios, AI works with participants in developing the skills necessary for success in disaster situations. The benefit is that workers are better prepared for the unexpected and unknown when they encounter it. Research limitations/implications – The current paper is based on author observation, experience and participant interviews. While AI is consistently transformative and successful, it would benefit from more rigorous and structured research to ground the findings more deeply in larger evidence based processes. Practical implications – The authors offer specific activities, resources for many others and practical application of this modality for training purposes. Social implications – Its application has tremendous benefits in training for specific skills, in creating greater cohesion and satisfaction in work units and breaking down culture and language barriers. Originality/value – This work is original in introducing these training methods to humanitarian aid contexts in general, and disaster preparedness and response in particular.
7

Nikolaesku, Inna, Olena Budnyk, Viktoria Bondar, Oksana Tepla, and Liudmyla Berezovska. "Pedagogical Management in Inclusive Process of the Educational Institution." Revista Amazonia Investiga 10, no. 39 (May 5, 2021): 76–85. http://dx.doi.org/10.34069/ai/2021.39.03.7.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
The aim of this article is to substantiate the requirements to the head of an inclusive education institution in the context of modern realities, to present the results of an experimental study of the development of education manager’s inclusive competence, the cognitive-activity and analytical-performance components of readiness to solve professional practical tasks. The professional profile of the head of an inclusive education institution (manager-leader-expert) is singled out, which combines pedagogical, psychological, inclusive competences, professional skills, as well as personal, business and professional qualities, value orientations, etc. The results of an empirical study of the level of inclusive readiness of modern leaders to form an inclusive environment in an educational institution are presented. Education managers from Ivano-Frankivsk and Cherkasy regions (Ukraine) (having 5-15 years of management experience) were involved in the experiment. Positive dynamics of increasing the level of readiness in the experimental group, for whom a series of thematic exercises (lectures, workshops, webinars) using innovative educational technologies was carried out.
8

Hmoud, Bilal Ibrahim, and László Várallyai. "Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants." International Journal of Engineering and Management Sciences 5, no. 1 (April 14, 2020): 749–65. http://dx.doi.org/10.21791/ijems.2020.1.65.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
With the rapidly emerging trend of employing Artificial Intelligence technologies within modern economics. This study is an attempt to fill the research gap associated with the factors that have influence with the adoption of artificial intelligence in human resources information systems on HR-leaders intention to use it. It empirically investigates the influences that trust, technological readiness, facilitating condition and performance expectancy on HR-professional’s behavioral intention to use AI in HRM. Besides, examine the moderating effect of age and experience on the proposed associations. Data were collected from by online questionnaire from 185 HR managers. A structural framework was introduced to test the relationship between study latent variables. Result exhibited that trust and performance expectancy has a significant influence on HR-professionals behavioral intention to use AI-HRIS. Trust and technological readiness showed a significant influence on HR-professionals performance expectancy of using AI-HRIS. While facilitating condition, organizational size and technological readiness did not show a significant influence on HR-professionals behavioral intention toward using AI-HRIS. Lastly, Age and Experience did not have a moderating effect on trust and performance expectancy association with the behavioral intention toward using AI-HRIS. The findings of this study contribute to the theory development of information technology diffusion in HRM.
9

Vuong, Quan-Hoang, Manh-Tung Ho, Thu-Trang Vuong, Viet-Phuong La, Manh-Toan Ho, Kien-Cuong P. Nghiem, Bach Xuan Tran, et al. "Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System." Journal of Clinical Medicine 8, no. 2 (February 1, 2019): 168. http://dx.doi.org/10.3390/jcm8020168.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
This review paper presents a framework to evaluate the artificial intelligence (AI) readiness for the healthcare sector in developing countries: a combination of adequate technical or technological expertise, financial sustainability, and socio-political commitment embedded in a healthy psycho-cultural context could bring about the smooth transitioning toward an AI-powered healthcare sector. Taking the Vietnamese healthcare sector as a case study, this paper attempts to clarify the negative and positive influencers. With only about 1500 publications about AI from 1998 to 2017 according to the latest Elsevier AI report, Vietnamese physicians are still capable of applying the state-of-the-art AI techniques in their research. However, a deeper look at the funding sources suggests a lack of socio-political commitment, hence the financial sustainability, to advance the field. The AI readiness in Vietnam’s healthcare also suffers from the unprepared information infrastructure—using text mining for the official annual reports from 2012 to 2016 of the Ministry of Health, the paper found that the frequency of the word “database” actually decreases from 2012 to 2016, and the word has a high probability to accompany words such as “lacking”, “standardizing”, “inefficient”, and “inaccurate.” Finally, manifestations of psycho-cultural elements such as the public’s mistaken views on AI or the non-transparent, inflexible and redundant of Vietnamese organizational structures can impede the transition to an AI-powered healthcare sector.
10

Brusova, Olga, Margarita Corzo, and Michael J. Pyrcz. "Introduction to this special section: Machine learning and AI." Leading Edge 39, no. 10 (October 2020): 700. http://dx.doi.org/10.1190/tle39100700.1.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
There is an ongoing digital revolution motivated by the data-driven scientific discovery paradigm. Geophysics has a mixed level of readiness, with some of these technologies already applied, research still emerging, and new opportunities to be discovered.
Більше джерел
Також вас можуть зацікавити розширені добірки на тему "AI readiness" для конкретних типів джерел:
Статті в журналах

Дисертації з теми "AI readiness":

1

Göransson, Johanna, and Sofi Glas. "AI-READINESS : En kvalitativ fallstudie i skogsindustrin." Thesis, Umeå universitet, Institutionen för informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-183674.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Organizations in all industries have reached a transition point because of the rapid development of digital technology. Digitalization and AI has therefore become the driving force for transformation within today's organizations to remain competitive in the digital era. The forest industry is no exception. However, digital transformation through AI within organizations is synonymous with high complexity and the forestry industry faces unique challenges to overcome because of the industry's traditional approach and the corporate culture that comes with it. This approach creates challenges for technology to be able to take an active and leading role. Problems that can emerge with digital transformation are one of the most important topics that have been researched for a long time. But few studies have examined digital transformation through AI in the forestry industry. Against this background, the purpose of this study is to analyze the barriers that can inhibit AI-Readiness in the forest industry. To answer our research question, we have used a qualitative case study with semi-structured interviews. The semi-structured interviews are based on the framework Technological Frames which intends to examine interpretation about information technologies. The results shows that barriers that can inhibit AI-Readiness exist which can be linked to organizational culture, user experiences and IT-strategies.
2

Ek, Lina, and Sanna Ström. "Organizational AI Readiness : Evaluating Employee Attitudes and Management Responses." Thesis, Jönköping University, Internationella Handelshögskolan, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53412.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Background - As a result of the latest advances in artificial intelligence (AI), the world ofbusiness is facing a major transformation where basic organizational principlesare redefined initiating a new era. It is predicted that AI in the coming decadeswill make a significant imprint and organizations aiming to stay at the forefrontcannot afford not to change. AI adoption can bring great benefits to organizationswhere a crucial factor is to establish AI readiness. However, as in any change,different perceptions are raised among employees which can either hinder orfoster organizational AI readiness, placing leaders in a crucial position. Purpose - The purpose of this study is to investigate how managers can foster organizationalAI readiness by understanding distinctive features of employee AI attitudes. Byidentifying how employees develop change attitudes towards AI, the opportunityto explore how managers should respond to these attitudes in order to achieve AIreadiness opens. Method - To gain a greater understanding of the phenomenon managing AI attitudes and tofulfil the purpose of the study, a mix of a qualitative and quantitative researchmethodology was used. The empirical data were abductively collected through asingle case study via a survey containing 80 respondents and through a focusgroup including six participants holding different roles affected by an AIimplementation. The empirical data were processed using thematic analysis andfurther analysed through systematic combining. Conclusions - The conclusions in this study confirm already existing theory. It also expands itas the phenomenon managing attitudes towards AI change was placed in a newcontext. The research results indicate that employees’ change attitudes towardsAI are affected by the organizational AI maturity, personal interest, and personaland organizational AI knowledge. They also indicate that employees develop theirchange attitudes towards AI depending on how managers handle or not handletheir attitudes. Finally, four dimensions along which leaders should manageemployee change attitudes to promote AI readiness were elaborated.
3

Krantz, Oscar. "Beredskap för AI implementering : En fallstudie om beredskapsfaktorer för AI implementeringar på svenska IT-företag." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19939.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Titel: Beredskap för AI implementering: En fallstudie om beredskapsfaktorer för AI implementeringar på svenska IT-företag Forskningsfråga: Vilka beredskapsfaktorer krävs för implementering av AI på svenska IT-företag? Syfte: Undersöka vilka beredskapsfaktorer svenska IT-företag behöver för att skapa en lyckad AI implementering. Genomförande: Studien genomfördes som en fallstudie. En litteraturstudie utfördes för att sammanfatta tillgänglig och relevant vetenskaplig forskning inom området och fungerade som forskningsöversikt till studien. Datainsamlingen bestod av åtta semistrukturerade intervjuer med respondenter från sju olika företag. Material från litteraturstudie jämförs med insamlade och bearbetade data från respondenterna för att kunna hitta likheter och skillnader för att på så sätt kunna besvara studiens frågeställning. Metod: Studien har en induktiv ansats med en kvalitativ inriktning som karaktäriseras av analytiska tolkningar utifrån litteratur och intervjuer. Resultat: Utifrån analysen, en jämförelse av tidigare litteratur och insamlade data, nåddes resultat i form av identifierade beredskapsfaktorer som företag borde prioritera för att lyckas med sin AI implementering. Beredskapsfaktorer som identifierats är data, kunskap, syfte, involvering av anställda, resurser samt etik.Slutsats: Studiens slutsats visar på att beredskapsfaktorerna som tagits fram kan hjälpa företag att bättre förbereda sig inför en AI implementering och därigenom också öka möjligheterna för att åstadkomma en lyckad AI implementering på svenska IT-företag.
Title: Readiness for AI Implementation: A case study on readiness factors for AI implementations in Swedish IT companies search Question: Which readiness factors are required for an implementation of AI in Swedish IT companies? Purpose: Investigate which readiness factors Swedish IT companies need to create a successful AI implementation. Implementation: The study was performed as a case study. A literature study was conducted to summarize available and relevant research in the research area and served as a research overview for the study. The data collection consisted of eight semistructured interviews with respondents from seven different IT companies. Data from the literature study were compared with data collected and processed from the respondents, in order to find similarities and differences to answer the study's research question. Method: The study had an inductive approach with a qualitative focus, characterized by analytical interpretations based on literature and interviews. Findings: Based on the analysis, a comparison of previous literature and collected data, readiness factors required for AI implementations were identified. Readiness factors identified were data, knowledge, purpose, employee involvement, resources and ethics of which companies should prioritize to succeed with their AI implementation. Conclusion: The conclusion of the study indicates that the readiness can help companies to better prepare for an AI implementation and thereby also increase the opportunities for achieving a successful AI implementation in Swedish IT companies.
4

Stenberg, Louise, and Svante Nilsson. "Factors influencing readiness of adopting AI : A qualitative study of how the TOE framework applies to AI adoption in governmental authorities." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279583.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Artificial intelligence is increasing in interest and it is creating value to many organizations world-wide. Due to the potential, governmental authorities in Sweden who work with large volumes of text documents are interested in natural language processing models, which is a sub field of AI and have started to incorporate it to their organizations. This study explores and discusses factors that are influential for governmental authorities when adopting AI and highlights ethical aspects which are of importance for the adoption process. This is explored through a literature review which lead to a frame of reference built on the Technology Organization Environment framework (TOE), which then was tested through interviews with project leaders and AI architects at governmental authorities who are working with language models. The results show that the TOE framework is suitable for analysing AI adoption for governmental authorities. The factors that are found influential are Relative Advantage, Compatibility and Complexity, Management support, Staff capacity, Regulatory environment and Cooperation. Furthermore, the findings suggest that AI Ethics and Data access are influential in all three contexts of technology, organization and environment. The findings of this study confirm results from previous research regarding adoption of new technology, and also provides the literature with exploring the adoption process of AI in governmental authorities, which was not widely explored in literature on beforehand.
Allt fler intresserar sig för artificiell intelligens då det skapar värde för många organisationer. Svenska myndigheter som arbetar med stora mängder textdokument ser potentialen i AI och har börjat implementera språkmodeller, ett sorts AI, i sina organisationer. Den här studien utforskar och diskuterar faktorer som är inflytelserika inför implementering av AI och belyser etiska aspekter som är viktiga för implementationsprocessen. Detta har utforskats först genom en litteraturstudie, ur vilken ett ramverk som bygger på Teknologi Organisation Miljö-ramverket (TOE) har tagits fram. Detta har sedan testats genom intervjuer med projektledare och AI arkitekter på svenska myndigheter som arbetar med språkmodeller. Resultaten visar att TOE-ramverket lämpar sig väl för att analysera adoptering av AI i myndigheter. Faktorerna som har identifierats som inflytelserika är relativ fördel, kompatibilitet, komplexitet, ledningsstöd, anställdas kapacitet, regleringskontext och samarbete. Dessutom föreslås det att etik för AI och datatillgång ska spänna över alla tre kontexter inom TOE. Resultaten av studien bekräftar tidigare forskning gällande adoptering av nya teknologier, och den bidrar även till litteraturen genom att utforska adopteringsprocessen av AI i myndigheter, vilket inte har utforskats i större utsträckning tidigare.
5

Pajany, Peroumal. "AI Transformative Influence: Extending the TRAM to Management Student's AI’s Machine Learning Adoption." Franklin University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=frank1623093426530669.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Umurerwa, Janviere, and Maja Lesjak. "AI IMPLEMENTATION AND USAGE : A qualitative study of managerial challenges in implementation and use of AI solutions from the researchers’ perspective." Thesis, Umeå universitet, Institutionen för informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-187810.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Artificial intelligence (AI) technologies are developing rapidly and cause radical changes in organizations, companies, society, and individual levels. Managers are facing new challenges that they might not be prepared for. In this work, we seek to explore managerial challenges experienced while implementing and using AI technologies from the researchers’ perspective. Moreover, we explore how appropriate ethical deliberations should be applied when using big data concerning AI and the meaning of understanding or defining it. We describe qualitative research, the triangulation that includes related literature, in-depth interviews with researchers working on related topics from various fields, and a focus group discussion. Our findings show that AI algorithms are not universal, objective, or neutral and therefore researchers believe, it requires managers to have a solid understanding of the complexity of AI technologies and the nature of big data. Those are necessary to develop sufficient purchase capabilities and apply appropriate ethical considerations. Based on our results, we believe researchers are aware that those issues should be handled, but so far have too little attention. Therefore, we suggest further discussion and encourage research in this field.

Книги з теми "AI readiness":

1

AI Tools for Military Readiness. RAND Corporation, 2021. http://dx.doi.org/10.7249/rra449-1.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Readings from AI magazine: Volumes 1-5. Menlo Park, Calif: American Association for Artificial Intelligence, 1988.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

1946-, Yang Jwing-Ming, ed. Tai chi secrets of the ancient masters: Selected readings with commentary. Boston, Mass: YMAA Publication Center, 1999.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

(AAAI), American Association for Artificial Intelligence. Readings from AI Magazine, Vols. 1-5: 1980-1985. AAAI Press, 1990.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Stewart, Sarah. Ai shu ren Huang Moli. 8th ed. 2001.

Знайти повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Частини книг з теми "AI readiness":

1

Bandyopadhyay, Bortik, Sambaran Bandyopadhyay, Srikanta Bedathur, Nitin Gupta, Sameep Mehta, Shashank Mujumdar, Srinivasan Parthasarathy, and Hima Patel. "1st International Workshop on Data Assessment and Readiness for AI." In Lecture Notes in Computer Science, 117–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75015-2_12.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Ozturk, Koray, Yu Zhong, Zi-Kui Liu, and Alan A. Luo. "Computational Thermodynamics and Experimental Investigation of Mg-AI-Ca Alloys." In Essential Readings in Magnesium Technology, 415–19. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118859803.ch66.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

Galin, Rinat, and Roman Meshcheryakov. "Collaborative Robots: Development of Robotic Perception System, Safety Issues, and Integration of AI to Imitate Human Behavior." In Proceedings of 15th International Conference on Electromechanics and Robotics "Zavalishin's Readings", 175–85. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5580-0_14.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

"Operational Readiness Testing Manual (ORT." In ITIL Release Management, 225–54. CRC Press, 2010. http://dx.doi.org/10.1201/9781439815595-ai.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

McAuliffe, Brian. "Challenges Facing Technology Standardization in the Age of Digital Transformation." In Advances in Human Resources Management and Organizational Development, 34–45. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9008-8.ch003.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
It is widely recognized that we are in rapid transition to the so-called fourth industrial revolution, a world of digitalization and mass interconnectedness enabled by a plethora of emergent powerful technologies including artificial intelligence (AI), internet of things (IoT), and distributed ledgers (DLT). A key element of this “revolution” is the move to digital manufacturing. While undoubtedly exciting, this transition presents challenges to policymakers, industry, and societal stakeholders alike. One such challenge is defining an optimum level for any market intervention measure(s), such that a balance is struck between ensuring a pro-industrial and economic innovation-friendly approach and guaranteeing adequate levels of consumer-focused protection. Standardization can be leveraged as one element of interventionary policy designed to help strike the required balance, both in its well-proven bottom-up and industry-led voluntary application and as a tool to support implementation of regulations. With a focus on digital transformation, this chapter will analyze the readiness of the current standardization system to support this significant transition focusing on strengths and challenges to be addressed from the perspective of industry, policymakers, and standards-setting organizations.
6

McAuliffe, Brian. "Challenges Facing Technology Standardization in the Age of Digital Transformation." In Research Anthology on Artificial Intelligence Applications in Security, 1839–50. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7705-9.ch081.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
It is widely recognized that we are in rapid transition to the so-called fourth industrial revolution, a world of digitalization and mass interconnectedness enabled by a plethora of emergent powerful technologies including artificial intelligence (AI), internet of things (IoT), and distributed ledgers (DLT). A key element of this “revolution” is the move to digital manufacturing. While undoubtedly exciting, this transition presents challenges to policymakers, industry, and societal stakeholders alike. One such challenge is defining an optimum level for any market intervention measure(s), such that a balance is struck between ensuring a pro-industrial and economic innovation-friendly approach and guaranteeing adequate levels of consumer-focused protection. Standardization can be leveraged as one element of interventionary policy designed to help strike the required balance, both in its well-proven bottom-up and industry-led voluntary application and as a tool to support implementation of regulations. With a focus on digital transformation, this chapter will analyze the readiness of the current standardization system to support this significant transition focusing on strengths and challenges to be addressed from the perspective of industry, policymakers, and standards-setting organizations.
7

McAuliffe, Brian. "Challenges Facing Technology Standardization in the Age of Digital Transformation." In Research Anthology on Artificial Intelligence Applications in Security, 1839–50. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7705-9.ch081.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
It is widely recognized that we are in rapid transition to the so-called fourth industrial revolution, a world of digitalization and mass interconnectedness enabled by a plethora of emergent powerful technologies including artificial intelligence (AI), internet of things (IoT), and distributed ledgers (DLT). A key element of this “revolution” is the move to digital manufacturing. While undoubtedly exciting, this transition presents challenges to policymakers, industry, and societal stakeholders alike. One such challenge is defining an optimum level for any market intervention measure(s), such that a balance is struck between ensuring a pro-industrial and economic innovation-friendly approach and guaranteeing adequate levels of consumer-focused protection. Standardization can be leveraged as one element of interventionary policy designed to help strike the required balance, both in its well-proven bottom-up and industry-led voluntary application and as a tool to support implementation of regulations. With a focus on digital transformation, this chapter will analyze the readiness of the current standardization system to support this significant transition focusing on strengths and challenges to be addressed from the perspective of industry, policymakers, and standards-setting organizations.
8

"Introduction to AI and Databases." In Readings in Artificial Intelligence and Databases, 9. Elsevier, 1989. http://dx.doi.org/10.1016/b978-0-934613-53-8.50005-4.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
9

Gasser, Les, Carl Braganza, and Nava Herman. "Implementing Distributed AI Systems Using MACE." In Readings in Distributed Artificial Intelligence, 445–50. Elsevier, 1988. http://dx.doi.org/10.1016/b978-0-934613-63-7.50047-4.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
10

"D.C. Dennett, “Cognitive Wheels: The Frame Problem of AI”." In Philosophy of Psychology: Contemporary Readings, 445–66. Routledge, 2007. http://dx.doi.org/10.4324/9780203028957-38.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "AI readiness":

1

Najdawi, Anas. "Assessing AI Readiness Across Organizations: The Case of UAE." In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020. http://dx.doi.org/10.1109/icccnt49239.2020.9225386.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
2

Montoya, Laura, and Pablo Rivas. "Government AI Readiness Meta-Analysis for Latin America And The Caribbean." In 2019 IEEE International Symposium on Technology and Society (ISTAS). IEEE, 2019. http://dx.doi.org/10.1109/istas48451.2019.8937869.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
3

McNatt, Cameron, and Tom Gaudette. "Vectorized Test Program Sets using MATLAB and the Teradyne AI-710 Analog Test Instrument." In 2006 IEEE AUTOTESTCON. IEEE Systems Readiness Technology Conference. IEEE, 2006. http://dx.doi.org/10.1109/autest.2006.283701.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
4

Lin, Chen, Hongtan Sun, Jinho Hwang, Maja Vukovic, and John Rofrano. "Cloud Readiness Planning Tool (CRPT): An AI-Based Framework to Automate Migration Planning." In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, 2019. http://dx.doi.org/10.1109/cloud.2019.00021.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
5

Gelashvili, Teona, and Ingrid Pappel. "Challenges of Transition to Paperless Management: Readiness of Incorporating AI in Decision-making Processes." In 2021 Eighth International Conference on eDemocracy & eGovernment (ICEDEG). IEEE, 2021. http://dx.doi.org/10.1109/icedeg52154.2021.9530905.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
6

Brown, Nassima, Adrian Brown, Abhijeet Degupta, Barry Quinn, Dustin Stringer, and Bozhidar Yankov. "Industry First AI-Powered Fully Automated Safety Observation System Deployed to Global Offshore Fleet." In SPE Offshore Europe Conference & Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205465-ms.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Abstract As the oil and gas industry is facing tumultuous challenges, adoption of cutting-edge digital technologies has been accelerated to deliver safer, more efficient operations with less impact on the environment. While advanced AI and other digital technologies have been rapidly evolving in many fields in the industry, the HSE sector is playing catch-up. With the increasing complexity of risks and safety management processes, the effective application of data-driven technologies has become significantly harder, particularly for international organizations with varying levels of digital readiness across diverse global operations. Leaders are more cautious to implement solutions that are not fit-for purpose, due to concerns over inconsistencies in rolling out the program across international markets and the impact this may have on ongoing operations. This paper describes how the effective application of Artificial intelligence (AI) and Machine Learning (ML) technologies have been used to engineer a solution that fully digitizes and automates the end-to-end offshore behavior-based safety program across a global offshore fleet; optimizing a critical safety process used by many leading oil & gas organization to drive positive workplace safety culture. The complex safety program has been transformed into clear, efficient and automated workflow, with real-time analytics and live transparent dashboards which detail critical safety indicators in real time, aiding decision-making and improving operational performance. The novel behavior-based safety digital solution, referred to as 3C observation tool within Noble drilling, has been built to be fully aligned with the organization's safety management system requirements and procedures, using modern and agile tools and applications for fully scalability and easy deployment. It has been critical in sharpening the offshore safety observation program across global operations, resulting in a boost of the workforce engagement by 30%, and subsequently increasing safety awareness skill set attainment; improving overall offshore safety culture, all while reducing operating costs by up to 70% and cutting carbon footprint through the elimination of 15,000 manhours and half a million paper cards each year, when compared to previously used methods and workflows
7

Hanna, Botros N., Tran C. Son, and Nam T. Dinh. "Benchmarking an AI-Guided Reasoning-Based Operator Support System on the Three Mile Island Accident Scenario." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16400.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Abstract In the Nuclear Power Plant (NPP) control room, the operators’ performance in emergencies is impacted by the need to monitor many indicators on the control room boards, the limited time to interact with dynamic events, and the incompleteness of the operator’s knowledge. Recent research has been directed toward increasing the level of automation in the NPP system by employing modern AI techniques that support the operator’s decisions. In previous work, the authors have employed a novel AI-guided declarative approach (namely, Answer Set Programming (ASP)) to represent and reason with human qualitative knowledge. This represented knowledge is structured to form a reasoning-based operator support system that assists the operator and compensates for any knowledge incompleteness by performing reasoning to diagnose failures and recommend executing actions in real time. A general ASP code structure has been proposed and tested against simple scenarios, e.g., diagnosis of pump failures that result in loss of flow transients and generating the needed plans for resolving the issue of stuck valves in the secondary loop. In this work, we investigate the potential of the previously proposed ASP structure by applying ASP to a realistic case study of the Three Mile Island, Unit 2 (TMI-2) accident event sequence (in particular, the first 142 minutes). The TMI scenario presents many challenges for a reasoning system, including a large number of variables, the complexity of the scenario, and the misleading readings. The capability of the ASP-based reasoning system is tested for diagnosis and recommending actions throughout the scenario. This paper is the first work to test and demonstrate the capability of an automated reasoning system by applying it to a realistic nuclear accident scenario, such as the TMI-2 accident.
8

Ooi, Guang An, Mehmet Burak Özakin, Tarek Mahmoud Mostafa, Hakan Bagci, Shehab Ahmed, and Mohamed Larbi Zeghlache. "EM-Based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning PIML." In SPE Offshore Europe Conference & Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205404-ms.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
Abstract In the wake of today's industrial revolution, many advanced technologies and techniques have been developed to address the complex challenges in well integrity evaluation. One of the most prominent innovations is the integration of physics-based data science for robust downhole measurements. This paper introduces a promising breakthrough in electromagnetism-based corrosion imaging using physics informed machine learning (PIML), tested and validated on the cross-sections of real metal casings/tubing with defects of various sizes, locations, and spacing. Unlike existing electromagnetism-based inspection tools, where only circumferential average metal thickness is measured, this research investigates the artificial intelligence (AI)-assisted interpretation of a unique arrangement of electromagnetic (EM) sensors. This facilitates the development of a novel solution for through-tubing corrosion imaging that enhances defect detection with pixel-level accuracy. The developed framework incorporates a finite-difference time-domain (FDTD)-based EM forward solver and an artificial neural network (ANN), namely the long short-term memory recurrent neural network (LSTM-RNN). The ANN is trained using the results generated from the FDTD solver, which simulates sensor readings for different scenarios of defects. The integration of the array EM-sensor responses and an ANN enabled generalizable and accurate measurements of metal loss percentage across various experimental defects. It also enabled the precise predictions of the defects’ aperture sizes, numbers, and locations in 360-degree coverage. Results were plotted in customized 2D heat-maps for any desired cross-section of the test casings. Further analysis of different techniques demonstrated that the LSTM-RNN could show higher precision and robustness compared to regular dense NNs, especially in the case of multiple defects. The LSTM-RNN is validated using additional data from simulated and experimental data. The results show reliable predictions even with limited training data. The model accurately predicted defects of larger and smaller sizes that were intentionally excluded from the training data to demonstrate generalizability. This highlights a major advance toward corrosion imaging behind tubing. This novel technique paves the way for the use of similar concepts on other sensors in multiple barriers imaging. Further work includes improvement to the sensor package and ANNs by adding a third dimension to the imaging capabilities to produce 3D images of defects on casings.

Звіти організацій з теми "AI readiness":

1

Konaev, Margarita, Husanjot Chahal, Ryan Fedsiuk, Tina Huang, and Ilya Rahkovsky. U.S. Military Investments in Autonomy and AI: A Strategic Assessment. Center for Security and Emerging Technology, October 2020. http://dx.doi.org/10.51593/20190044.

Повний текст джерела
Стилі APA, Harvard, Vancouver, ISO та ін.
Анотація:
This brief examines how the Pentagon’s investments in autonomy and AI may affect its military capabilities and strategic interests. It proposes that DOD invest in improving its understanding of trust in human-machine teams and leverage existing AI technologies to enhance military readiness and endurance. In the long term, investments in reliable, trustworthy, and resilient AI systems are critical for ensuring sustained military, technological, and strategic advantages.

До бібліографії