Relevant bibliographies by topics / Artificial intelligence (AI)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial intelligence (AI)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 March 2025

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Journal articles on the topic "Artificial intelligence (AI)"

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Layne, Armand. "Artificial intelligence (ai)." Intelligent Systems in Accounting, Finance and Management 13, no. 3 (July 2005): 179–81. http://dx.doi.org/10.1002/isaf.267.

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Peter Smith, Peter Smith, and Laura Smith Peter Smith. "Artificial Intelligence (AI) and Ethics." International Journal of Computer Auditing 6, no. 1 (December 2024): 069–76. https://doi.org/10.53106/256299802024120601006.

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<p>AI is everywhere around us and affecting our lives in many ways, some of which we may not even be aware of. This paper discusses the advantages and disadvantages of AI and its impact upon society. The authors, Peter, and Laura are both disabled which means that, inevitably, they view AI and the ethical issues which it brings, through the lens of disabled people. However, they try as much as they can to discuss the ethical issues around AI in an unbiased manner. This commentary starts by introducing the concept of AI, the authors and their disabilities, the meanings of ethics and goes on to discuss the ethical issues which Peter and Laura see in their day-to-day lives. They then drawfromthe literature on AI and ethics to broaden the discussion to take account of current published work by others on the topic. Finally, they return to their own perspectives and conclude that AI offers many advantages to society and our future. However, they also warn of the dangers and ethical challenges which AI raises. It is hoped that this commentary frames a contribution to the field of AI and ethics which readers will find interesting, useful and, perhaps, challenging.</p> <p>&nbsp;</p>
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Kishorekumar, Mr A., Mr E. Ezhilarasan, and Mr R. Parthiban. "Intelligent Drone based Personal Assistant using Artificial Intelligence AI." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1618–21. http://dx.doi.org/10.31142/ijtsrd11482.

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Huang, Ming-Hui, and Roland T. Rust. "Artificial Intelligence in Service." Journal of Service Research 21, no. 2 (February 5, 2018): 155–72. http://dx.doi.org/10.1177/1094670517752459.

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Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for “lower” (easier for AI) intelligence tasks first. AI first replaces some of a service job’s tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job’s tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.
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Tarekar, Sagar. "Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 2533–35. http://dx.doi.org/10.22214/ijraset.2024.59386.

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Abstract: We have chosen this topic to spotlight on one of the most technological trend these days known as AI(Artificial Intelligent). Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. AI brings up images of high- functioning, human-like robots taking over the world, AI isn’t intended to replace humans. Therefore we will discuss some of the most important aspects related to AI in which it will help in a better understanding of Artificial Intelligent and both its advantages and disadvantages to be able to protect ourselves from the upcoming technological trend.
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Singhal, Ankur, and Komal Gupta. "Artificial Intelligence: Transforming Ayurveda." Indian Journal of Ancient Medicine and Yoga 15, no. 4 (December 27, 2022): 151–58. http://dx.doi.org/10.21088/ijamy.0974.6986.15422.3.

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As we know technology is at its boom in 21st century and it has touched our every aspect of life including The Ayurveda: ‘The science of life’. So for upgrading this ancient and 5000 yrs old system of medicine there must be full fledged inclusion of technology that allows computers and machines to function in an intelligent manner. This change with the help of technology is called as Artificial intelligence. It is the simulation of human intelligence processes by machines, especially computer systems. By adopting and adapting this digital world in the field of Ayurveda we can create a revolutionary change. The development of artificial intelligence (AI) in the medical field has been growing rapidly. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians. The objective of this article is to categorize and seek the current and further usage of AI in Ayurveda. A systematic scoping review has been conducted. The entire database, articles, and news have been used to find studies regarding AI and Ayurveda. A broad-range search strategy was applied to locate all relevant studies. Although the results from this review have shown the potential use of AI models in Ayurveda, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in Ayurveda in the era of digital health.
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Daisy, Anjali. "Artificial Intelligence." International Journal of Social Ecology and Sustainable Development 13, no. 1 (January 2022): 1–9. http://dx.doi.org/10.4018/ijsesd.292075.

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Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation, and robotic intervention. In this article, the recent successful and influential applications of AI in surgery are reviewed from pre-operative planning and intra-operative guidance to the integration of surgical robots. It ends with summarizing the current state, emerging trends, and major challenges in the future development of AI in surgery. Robotic surgery is the use of computer technologies working in conjunction with robot systems to perform medical procedures. The technology is also known as computer-aided surgery and robot-assisted surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments in the field of Neuro Surgery.
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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, no. 27 (October 1, 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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Kaur, Ms Parminder. "Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 597–99. http://dx.doi.org/10.22214/ijraset.2023.56025.

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Abstract: A multidisciplinary discipline called artificial intelligence (A.I.) aims to automate jobs that currently need human intelligence. Artificial intelligence (AI), while not being well known, is a technology that is revolutionising all facets of existence. In order to rethink how we mix data, analyse it, and make decisions, this article attempts to inform laypeople about AI and urge them to use it as a tool in various fields. In this post, we briefly discussed artificial intelligence (AI), its principles, and potential applications.
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Al Dajeh, Bakhit Moh’d. "Artificial Intelligence Governance." Journal of Ecohumanism 3, no. 4 (July 18, 2024): 300–313. http://dx.doi.org/10.62754/joe.v3i4.3515.

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Research on AI governance is important towards potentially useful and constraining affordable misuse, reduce new risks and economic trends that threaten to disrupt public political and economic trends, and drive off target as interest in advanced AI systems and the norms, focal points, and use of new AI research are potentially transformative and governance institutions aim to prevent. Potential public benefits from policy community re-using AI research are enormous, including reduced economic instability. A fundamental challenge in AI governance is a cognitive framing challenge: governing AI research requires understanding new kinds of safety risks, performance goals, and intended applications that advanced AI systems will make possible. Specifically, the letter focuses on how AI research could mitigate issues such as the possibility of AI capabilities getting concentrated within a small and hard-to-regulate group of actors, and ultimately recommends the prioritization of open research and collaboration, with concern for long-term social and economic looming to the forefront of coalitions if AI becomes an increasingly important aspect of the future economy and society.
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Dissertations / Theses on the topic "Artificial intelligence (AI)"

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Karlsson, Marcus. "Developing services based on Artificial Intelligence." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-73090.

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This thesis explores the development process of services based on artificial intelligence (AI) technology within an industrial setting. There has been a renewed interest in the technology and leading technology companies as well as many start-ups has integrated it into their market offerings. The technology´s general application potential for enhancing products and services along with the task automation possibility for improved operational excellence makes it a valuable asset for companies. However, the implementation rate of AI services is still low for many industrial actors. The research in the area has been technically dominated with little contribution from other disciplines. Therefore, the purpose of this thesis is to identify development challenges of AI services and drawing on service development- and value-theory to propose a process framework promoting implementation. The work will have two main contributions. Firstly, to compare differences in theoretical and practical development challenges and secondly to combine AI with service development and value theory. The empirical research is done through a single case study based on a systematic combining research approach. It moves iteratively between the theory and empirical findings to direct and support the thesis throughout the work process. The data was collected through semi-structured interviews with a purposive sample. It consisted of two groups of interview participants, one AI expert group and one case internal group. This was supported by participant observation of the case environment. The data analysis was done through flexible pattern matching. The results were divided into two sections, practical challenges and development aspect of AI service development. These were combined with the selected theories and a process framework was generated. The study showed a current understudied area of business and organisational aspect regarding AI service development. Several such challenges were identified with limited theoretical research as support. For a wider industrial adoption of AI technology, more research is needed to understand the integration into the organisation. Further, sustainability and ethical aspect were found not to be a primary concern, only mention in one of the interviews. This, despite the plethora of theory and identified risks found in the literature. Lastly, the interdisciplinary research approach was found to be beneficial to the AI field to integrate the technology into an industrial setting. The developed framework could draw from existing service development models to help manage the identified challenges.
Denna uppsats utforskar utvecklingsprocessen av tjänster baserade på artificiell intelligens (AI) i en industriell miljö. Tekniken har fått ett förnyat intresse vilket har lett till att allt fler ledande teknik företag och start-up:s har integrerat AI i deras marknads erbjudande. Teknikens generella applikations möjlighet för att kunna förbättra produkter och tjänster tillsammans med dess automatiserings möjlighet för ökad operationell effektivitet gör den till en värdefull tillgång för företag. Dock så är implementations graden fortfarande låg för majoriteten av industrins aktörer. Forskningen inom AI området har varit mycket teknik dominerat med lite bidrag från andra forskningsdiscipliner. Därför syftar denna uppsats att identifiera utvecklingsutmaningar med AI tjänster och genom att hämta delar från tjänsteutveckling- och värde teori generera ett processramverk som premierar implementation. Uppsatsen har två huvudsakliga forskningsbidrag. Först genom att jämföra skillnader mellan teoretiska och praktiska utvecklingsutmaningar, sedan bidra genom att kombinera AI med tjänsteutveckling- och värdeteori. Den empiriska forskningen utfördes genom en fallstudie baserad på ett systematic combining tillvägagångsätt. På så sätt rör sig forskning iterativt mellan teori och empiri för att forma och stödja uppsatsen genom arbetet. Datat var insamlad genom semi strukturerade intervjuer med två separata, medvetet valda intervjugrupper där ena utgjorde en AI expert grupp och andra en intern grupp för fallstudien. Detta stöttades av deltagande observationer inom fallstudiens miljö. Dataanalysen utfördes med metoden flexible pattern matching. Resultatet var uppdelat i två olika sektioner, den första med praktiska utmaningar och den andra med utvecklingsaspekter av AI tjänsteutveckling. Dessa kombinerades med de utvalda teorierna för att skapa ett processramverk. Uppsatsen visar ett under studerat område angående affär och organisation i relation till AI tjänsteutveckling. Ett flertal av sådana utmaningar identifierades med begränsat stöd i existerande forskningslitteratur. För en mer utbredd adoption av AI tekniken behövs mer forskning för att förstå hur AI ska integreras med organisationer. Vidare, hållbarhet och etiska aspekter var inte en primär aspekt i resultatet, endast bemött i en av intervjuerna trots samlingen av artiklar och identifierade risker i litteraturen. Till sist, det tvärvetenskapliga angreppsättet var givande för AI området för att bättre integrera tekniken till en industriell miljö. Det utvecklade processramverket kunde bygga på existerande tjänsteutvecklings modeller för att hantera de identifierade utmaningarna.
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Kim, Jee Won. "How speciesism affects artificial intelligence (AI) adoption intent." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/228673/1/Jee%20Won_Kim_Thesis.pdf.

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As there have been concerns about the excessive advancement of artificial intelligence (AI) surpassing humans, exploring reactions to AI as challenging human superiority is meaningful. By examining how the hierarchical and discriminative views on animals (speciesism) affects the views on non-living AI, this thesis has significant and novel contributions to AI adoption literature and AI product marketing.
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Fatima, Samar. "Mapping artificial intelligence affordances for the public sector." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235926/1/Samar%2BFatime%2BThesis.pdf.

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This thesis explores the affordances of artificial intelligence (AI) for the public sector. The thesis consists of three studies that answer what, why and how questions of AI affordance actualization in public sector using a combination of primary and secondary data sources. In this thesis, the affordance theory lens is used to explore AI affordance perception and actualization for the public sector through three related studies. The perception of AI affordance is investigated in the first two studies. The third study designed and evaluated artefact for public agencies to actualize AI affordance.
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Malmborn, Albin, and Linus Sjöberg. "Implementing Artificial intelligence." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20942.

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Den här uppsatsen har som syfte att undersöka huruvida det är möjligt att ta fram riktlinjer för vad privata verksamheter behöver ta i beaktande inför en planerad implementering av artificiell intelligens. Studien kommer belysa faktorer som hjälper företag att förstå vad som krävs inför en sådan omställning, men även de hinder som måste övervinnas för att lyckas. Studiens datainsamling har genomförts med två metoder, först en litteraturstudie sedan kvalitativa, semistrukturerade forskningsintervjuer. Dessa har sedan analyserats med vars en analysmetod som kompletterar varandra och därefter tolkats för att se mönster som kan besvara studiens frågeställning: Vad måste svenska organisationer inom den privata sektorn beakta för att lyckas implementera Artificiell intelligens i sin verksamhet? Resultatet har tagits fram genom att jämföra vetenskapliga texter och intervjuer, för att undersöka om den akademiska och praktiska synen skiljer sig åt. Studien resulterade i åtta faktorer som företag borde ta i beaktning inför en implementering av artificiell intelligens. Författarna hoppas att med den här studien kunna främja svensk utveckling inom artificiell intelligens och på så vis generera ett större nationellt mervärde och en starkare konkurrenskraft internationell.
The purpose of this paper is to investigate the possibilities to develop guidelines for businesses to take into account before an implementation of artificial intelligence. The study will highlight different factors that will help companies to understand what is required to make this kind of digital transition, it will also highlight the obstacles companies have to overcome in order to succeed. The data collection was conducted in two parts, first a literature study and then qualitative, semi-structured interviews. These were analyzed with their own analysis which supplement each other, and interpreted to identify patterns that could answer the study's main question: What must Swedish organizations in the private sector consider in order to successfully implement Artificial Intelligence in their operations?The result of the study has been produced by comparing scientific texts and interviews, to investigate whether the academic and practical views differ. The study resulted in eight factors that companies should consider before implementing artificial intelligence. The authors hope that the study will promote Swedish development in artificial intelligence and thus generate a greater national value and international competitiveness.
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Edlund, Mattias. "Artificial Intelligence in Games : Faking Human Behavior." Thesis, Uppsala universitet, Institutionen för speldesign, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-258222.

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This paper examines the possibilities of faking human behavior with artificial intelligence in computer games, by using efficient methods that save valuable development time and also creates a more rich experience for the players of a game. The specific implementation of artificial intelligence created and discussed is a neural network controlling a finite-state machine. The objective was to mimic human behavior rather than simulating true intelligence. A 2D shooter game is developed and used for experiments performed with human and artificial intelligence controlled players. The game sessions played were recorded in order for other humans to replay. Both players and spectators of the game sessions left feedbacks and reports that could later be analyzed. The data collected from these experiments was then analyzed, and reflections were made on the entire project. Tips and ideas are proposed to developers of shooter games who are interested in making human-like artificial intelligence. Conclusions are made and extra information is provided in order to further iterate on this research.
Denna rapport undersöker möjligheterna att förfalska mänskligt beteende genom artificiell intelligens i datorspel, med hjälp av effektiva metoder som sparar värdefull utvecklingstid och som även skapar en rikare upplevelse för spelare. Den specifika implementationen av artificiell intelligens som utvecklas och diskuteras är ett neuralt nätverk som kontrollerar en finite-state machine. Målet var att efterlikna mänskligt beteende snarare än att simulera verklig intelligens. Ett 2D shooter-spel utvecklas och används för utförda experiment med mänskliga och artificiell intelligens-kontrollerade spelare. De sessioner som spelades under experimenten spelades in, för att sedan låta andra människor titta på inspelningarna. Både spelare och åskådare av spelsessionerna lämnade återkoppling och rapporter för senare analysering. Datan som samlats in från experimenten analyserades, och reflektioner utfördes på hela projektet. Tips och idéer presenteras till utvecklare av shooter-spel som är intresserade av en mer människolik artificiell intelligens. Slutsatser läggs fram och extra information presenteras för att kunna fortsätta iterera vidare på denna undersökning.
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Rafiq, M. Y. "Artificial intelligence techniques for the structural design of buildings." Thesis, University of Strathclyde, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382446.

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Casten, Carlberg Carl Johan, and Elsa Jerhamre. "Artificial Intelligence in Agriculture : Opportunities and Challenges." Thesis, Uppsala universitet, Avdelningen för datorteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-443576.

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Artificial Intelligence (AI) is increasingly used in different parts of society for providing decision support in various activities. The agricultural sector is anticipated to benefit from an increased usage of AI and smart devices, a concept called smart farming technologies. Since the agricultural sector faces several simultaneous challenges, such as shrinking marginals, complicated pan-European regulations, and demands to mitigate the environmental footprint, there are great expectations that smart farming will benefit both individual farmers and industry stakeholders. However, most previous research focuses only on a small set of characteristics for implementing and optimising specific smart farming technologies, without considering all possible aspects and effects. This thesis investigates both technical and non-technical opportunities and hurdles when implementing AI in Swedish agricultural businesses. Three sectors in agriculture are scrutinized: arable farming, milk production and beef production. As a foundation for the thesis, a literature review revises former research on smart farming. Thereafter, an interview study with 27 respondents both explores the susceptibility and maturity of smart farming technologies and provides examples of technical requirements of three chosen applications of AI in agriculture. Findings of the study include a diverse set of aspects that both enable and obstruct the transition. Main identified opportunities are the importance smart farming has on the strategic agendas of several industry stakeholders, the general trend towards software technology as a service through shared machinery, the vast amount of existing data, and the large interest from farmers towards new technology. Contrasting, the thesis identifies main hurdles as technical and legislative challenges to data ownership, potential cybersecurity threats, the need for a well-articulated business case, and the sometimes lacking technical knowledge within the sector. The thesis concludes that the macro trend points towards a smart farming transition but that the speed of the transformation will depend on the resolutions for the identified obstacles.
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Khan, Laiq. "Hybrid AI paradigms applied to power system damping controls." Thesis, University of Strathclyde, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273412.

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Vincenzi, Leonardo. "eXplainable Artificial Intelligence User Experience: contesto e stato dell’arte." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23338/.

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Il grande sviluppo del mondo dell’Intelligenza Artificiale unito alla sua vastissima applicazione in molteplici ambiti degli ultimi anni, ha portato a una sempre maggior richiesta di spiegabilità dei sistemi di Machine Learning. A seguito di questa necessità il campo dell’eXplainable Artificial Intelligence ha compiuto passi importanti verso la creazione di sistemi e metodi per rendere i sistemi intelligenti sempre più trasparenti e in un futuro prossimo, per garantire sempre più equità e sicurezza nelle decisioni prese dall’AI, si prevede una sempre più rigida regolamentazione verso la sua spiegabilità. Per compiere un ulteriore salto di qualità, il recente campo di studio XAI UX si pone come obiettivo principale l’inserimento degli utenti al centro dei processi di progettazione di sistemi di AI, attraverso la combinazione di tecniche di spiegabilità offerte dall'eXplainable AI insieme allo studio di soluzioni UX. Il nuovo focus sull’utente e la necessità di creare team multidisciplinari, e quindi con maggiori barriere comunicative tra le persone, impongono ancora un largo studio sia da parte degli esperti XAI sia da parte della comunità HCI, e sono attualmente le principali difficoltà da risolvere. All’interno dell’elaborato si fornisce una visione attuale sullo stato dell'arte della XAI UX introducendo le motivazioni, il contesto e i vari ambiti di ricerca che comprende.
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Svedberg, Peter O. S. "Steps towards an empirically responsible AI : a methodological and theoretical framework." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-246.

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Initially we pursue a minimal model of a cognitive system. This in turn form the basis for the development of amethodological and theoretical framework. Two methodological requirements of the model are that explanation be from the perspective of the phenomena, and that we have structural determination. The minimal model is derived from the explanatory side of a biologically based cognitive science. Fransisco Varela is our principal source for this part. The model defines the relationship between a formally defined autonomous system and an environment, in such a way as to generate the world of the system, its actual environment. The minimal model is a modular explanation in that we find it on different levels in bio-cognitive systems, from the cell to small social groups. For the latter and for the role played by artefactual systems we bring in Edwin Hutchins' observational study of a cognitive system in action. This necessitates the introduction of a complementary form of explanation. A key aspect of Hutchins' findings is the social domain as environment for humans. Aspects of human cognitive abilities usually attributed to the person are more properly attributed to the social system, including artefactual systems.

Developing the methodological and theoretical framework means making a transition from the bio-cognitive to the computational. The two complementary forms of explanation are important for the ability to develop a methodology that supports the construction of actual systems. This has to be able to handle the transition from external determination of a system in design to internal determination (autonomy) in operation.

Once developed, the combined framework is evaluated in an application area. This is done by comparing the standard conception of the Semantic Web with how this notion looks from the perspective of the framework. This includes the development of the methodological framework as a metalevel external knowledge representation. A key difference between the two approaches is the directness by which the semantic is approached. Our perspective puts the focus on interaction and the structural regularities this engenders in the external representation. Regularities which in turn form the basis for machine processing. In this regard we see the relationship between representation and inference as analogous to the relationship between environment and system. Accordingly we have the social domain as environment for artefactual agents. For human level cognitive abilities the social domain as environment is important. We argue that a reasonable shortcut to systems we can relate to, about that very domain, is for artefactual agents to have an external representation of the social domain as environment.

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Books on the topic "Artificial intelligence (AI)"

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Winston, Patrick Henry. Artificial intelligence. 3rd ed. Reading, MA: Addison-Wesley, 1993.

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Alareeni, Bahaaeddin A. M., and Islam Elgedawy, eds. Artificial Intelligence (AI) and Finance. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39158-3.

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Long, Guodong, Xinghuo Yu, and Sen Wang, eds. AI 2021: Advances in Artificial Intelligence. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3.

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Nicholson, Ann, and Xiaodong Li, eds. AI 2009: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10439-8.

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Peng, Wei, Damminda Alahakoon, and Xiaodong Li, eds. AI 2017: Advances in Artificial Intelligence. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63004-5.

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Orgun, Mehmet A., and John Thornton, eds. AI 2007: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76928-6.

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Wobcke, Wayne, and Mengjie Zhang, eds. AI 2008: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3.

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Thielscher, Michael, and Dongmo Zhang, eds. AI 2012: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35101-3.

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McKay, Bob, and John Slaney, eds. AI 2002: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36187-1.

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Pfahringer, Bernhard, and Jochen Renz, eds. AI 2015: Advances in Artificial Intelligence. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26350-2.

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Book chapters on the topic "Artificial intelligence (AI)"

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Moring, Andreas. "Artificial Intelligence." In AI on The Job, 21–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-64005-0_3.

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Lele, Ajey. "Artificial Intelligence (AI)." In Disruptive Technologies for the Militaries and Security, 139–54. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3384-2_8.

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Charalabidis, Yannis. "Artificial Intelligence (AI)." In The Palgrave Encyclopedia of Interest Groups, Lobbying and Public Affairs, 1–8. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-13895-0_129-1.

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Gilboa, Daniella. "Artificial Intelligence (AI)." In Textbook of Assisted Reproductive Techniques, 202–7. 6th ed. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003268598-18.

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Marsden, Paul. "Artificial Intelligence (AI)." In Digital Quality Management in Construction, 180–90. Title: Digital quality management in construction/Paul Marsden. Description: Abingdon, Oxon; New York, NY: Routledge is an imprint of the Taylor & Francis Group, an Informa Business, 2019.: Routledge, 2019. http://dx.doi.org/10.1201/9780429423062-18.

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Charalabidis, Yannis. "Artificial Intelligence (AI)." In The Palgrave Encyclopedia of Interest Groups, Lobbying and Public Affairs, 43–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-44556-0_129.

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Nair, K. Vinaykumar. "Artificial Intelligence (AI)." In Artificial Intelligence Theory, Models, and Applications, 75–92. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003175865-5.

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Kaushik, Manju. "Artificial Intelligence (Ai)." In Intelligent System Algorithms and Applications in Science and Technology, 119–33. Boca Raton: Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003187059-11.

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Zeldman, Maurice I. "Artificial Intelligence (AI)." In What Every Engineer Should Know about Robots, 53–61. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003573593-6.

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Myklebust, Thor, Tor Stålhane, and Dorthea Mathilde Kristin Vatn. "Artificial Intelligence (AI)." In SpringerBriefs in Computer Science, 39–46. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80504-2_6.

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Abstract This chapter first provides a section on artificial intelligence (AI) in high risk systems, giving an overview over the current progress in standards relating to this topic. Next, a section addresses explainable AI (XAI) both as a technical concept and as a concept that has evident human and organizational sides to it. Lastly, a section on the concept of safety of intended functionality (SOTIF) is provided as it addresses safety in AI-driven systems, especially autonomous vehicles. This approach helps mitigate risks from functional insufficiencies in AI algorithms, making it vital for deploying AI safely in high risk areas.
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Conference papers on the topic "Artificial intelligence (AI)"

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R, Vinu, and Jisy N. K. "Artificial Intelligence (AI) based Fast Billing System." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), 1421–25. IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823152.

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Cao, Hui. "AI photonics." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan, 501. SPIE, 2024. http://dx.doi.org/10.1117/12.3032303.

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Chan, Lok, Kenzie Doyle, Duncan McElfresh, Vincent Conitzer, John P. Dickerson, Jana Schaich Borg, and Walter Sinnott-Armstrong. "Artificial Artificial Intelligence." In AIES '20: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375627.3375870.

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"Session AI: Artificial intelligence." In 2013 8th International Conference on Computer Engineering & Systems (ICCES). IEEE, 2013. http://dx.doi.org/10.1109/icces.2013.6707160.

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"Session AI: Artificial Intelligence." In 2020 15th International Conference on Computer Engineering and Systems (ICCES). IEEE, 2020. http://dx.doi.org/10.1109/icces51560.2020.9334674.

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Monteiro, Juarez, Roger Granada, Rafael C. Pinto, and Rodrigo C. Barros. "Beating Bomberman with Artificial Intelligence." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4430.

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Artificial Intelligence (AI) seeks to bring intelligent behavior for machines by using specific techniques. These techniques can be employed in order to solve tasks, such as planning paths or controlling intelligent agents. Some tasks that use AI techniques are not trivially testable, since it can handle a high number of variables depending on their complexity. As digital games can provide a wide range of variables, they become an efficient and economical means for testing artificial intelligence techniques. In this paper, we propose a combination of a behavior tree and a Pathfinding algorithm to solve a maze-based problem using the digital game Bomberman of the Nintendo Entertainment System (NES) platform. We perform an analysis of the AI techniques in order to verify the feasibility of future experiments in similar complex environments. Our experiments show that our intelligent agent can be successfully implemented using the proposed approach.
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Chen, Rain, Yu-Chin Hsiao, and Mei-Wei Yang. "When AI Meets AI - Artificial Intelligence Competition." In 2023 11th International Conference on Information and Education Technology (ICIET). IEEE, 2023. http://dx.doi.org/10.1109/iciet56899.2023.10111413.

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Winecoff, Amy A., and Elizabeth Anne Watkins. "Artificial Concepts of Artificial Intelligence." In AIES '22: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514094.3534138.

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Wall, David S. "AI (Artificial Intelligence) and Cybercrime." In WebSci '20: 12th ACM Conference on Web Science. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394332.3402837.

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Panetta, Karen A. "Artificial Intelligence (AI) for Humanity." In 2020 International Symposium on Community-centric Systems (CcS). IEEE, 2020. http://dx.doi.org/10.1109/ccs49175.2020.9231494.

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Reports on the topic "Artificial intelligence (AI)"

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Bhatnagar, Ansh, and Devyani Gajjar. Policy implications of artificial intelligence (AI). Parliamentary Office of Science and Technology, January 2024. http://dx.doi.org/10.58248/pn708.

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Latorre, Lucia, Valentín Muro, Eduardo Rego, Mariana Gutierrez, Ignacio Cerrato, and Jose Daniel Zarate. Tech Report Artificial Intelligence. Inter-American Development Bank, June 2024. http://dx.doi.org/10.18235/0013015.

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This report provides a comprehensive overview of AI, from its fundamentals to its practical applications, covering topics such as its definition, evolution, and implementation. It also delves into various applications, such as machine learning, natural language processing, computer vision, and generative AI, providing specific examples and use cases across sectors like healthcare, logistics, environment, and security.
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Blanchard, Alexander, and Laura Bruun. Bias in Military Artificial Intelligence. Stockholm International Peace Research Institute, December 2024. https://doi.org/10.55163/cjft9557.

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To support states involved in the policy debate on military artificial intelligence (AI), this background paper provides a deeper examination of the issue of bias in military AI. Three insights arise. First, policymakers could usefully develop an account of bias in military AI that captures shared concern around unfairness. If so, ‘bias in military AI’ might be taken to refer to the systemically skewed performance of a military AI system that leads to unjustifiably different behaviours—which may perpetuate or exacerbate harmful or discriminatory outcomes—depending on such social characteristics as race, gender and class. Second, among the many sources of bias in military AI, three broad categories are prominent: bias in society; bias in data processing and algorithm development; and bias in use. Third, bias in military AI can have various humanitarian consequences depending on context and use. These range from misidentifying people and objects in targeting decisions to generating flawed assessments of humanitarian needs.
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Arnold, Zachary, and Ngor Luong. China’s Artificial Intelligence Industry Alliance. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/20200094.

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As part of its strategy to achieve global leadership in AI, the Chinese government brings together local governments, academic institutions, and companies to establish collaboration platforms. This data brief examines the role of China’s Artificial Intelligence Industry Alliance in advancing its AI strategy, and the key players in the Chinese AI industry.
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Rinuado, Christina, William Leonard, Christopher Morey, Theresa Coumbe, Jaylen Hopson, and Robert Hilborn. Artificial intelligence (AI)–enabled wargaming agent training. Engineer Research and Development Center (U.S.), April 2024. http://dx.doi.org/10.21079/11681/48419.

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Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.
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Brione, Patrick, and Devyani Gajjar. Artificial intelligence: ethics, governance and regulation. Parliamentary Office of Science and Technology, October 2024. http://dx.doi.org/10.58248/hs51.

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Diaz-Herrera, Jorge L. Artificial Intelligence (AI) and Ada: Integrating AI with Mainstream Software Engineering. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada286093.

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Haddad, Ibrahim. Artificial Intelligence and Data in Open Source. The Linux Foundation, March 2022. https://doi.org/10.70828/zaow8899.

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Artificial intelligence (AI) is no different from any other technology domain where OSS dominates. As with other industries, OSS adoption in the AI field has increased the use of open source in products and services, contributions to existing projects, the creation of projects fostering collaboration, and the development of new technologies. Artificial Intelligence and Data in Open Source reviews critical challenges in the open source AI ecosystem, discusses common characteristics across AI and data projects, and presents the role of the LF AI & Data Foundation in empowering innovators and accelerating open source development.
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Vilalta, Ricardo. A Gentle Introduction to Artificial Intelligence. Instats Inc., 2023. http://dx.doi.org/10.61700/ws1767apt7i86469.

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This workshop offers a comprehensive overview of AI, hands-on applications, and its relevance in academic research, with discussions on ethical considerations and future trends in AI. The workshop is designed for PhD students, professors, and professional researchers. An official Instats certificate of completion and 2 ECTS equivalent points are provided at the conclusion.
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Bhatnagar, Ansh, and Devyani Gajjar. How is artificial intelligence affecting society? Parliamentary Office of Science and Technology, May 2024. http://dx.doi.org/10.58248/rr15.

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