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Literatura académica sobre el tema "AI"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 9 de junio de 2025

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Artículos de revistas sobre el tema "AI"

albarracíín, pilar. "Ai, Ai, Ai." Gastronomica 8, no. 4 (2008): 122. http://dx.doi.org/10.1525/gfc.2008.8.4.122.

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Sachin, Samrat Medavarapu. "Demystifying AI: A Comprehensive Review of Explainable AI Techniques and Applications." European Journal of Advances in Engineering and Technology 10, no. 6 (June 30, 2023): 49–52. https://doi.org/10.5281/zenodo.13627267.

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Explainable Artificial Intelligence (XAI) seeks to make AI systems more transparent and understandable to users. This review examines the various techniques developed to achieve explainability in AI models and their applications across different domains. We discuss methods such as feature attribution, model simplification, and example-based explanations, highlighting their strengths and limitations. Additionally, we explore the importance of XAI in critical fields like healthcare, finance, and law. The findings underscore the necessity of explainability for trust, accountability, and ethical AI deployment, pointing towards future directions in the field.

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Shubham, Dighikar. "How Artificial Intelligence Influences the Marketing." Recent Trends in Data Mining and Business Forecasting 1, no. 1 (May 1, 2020): 1–5. https://doi.org/10.5281/zenodo.3779624.

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Artificial Intelligence is the new revolution in the business and in past few years it is indulged in various fields like science, business, medicine, automation and education, AI is also reached in the marketing business. Most of the big companies are using AI for their advertisement in digital platforms. AI has been changing the way of marketing and sales with consumer perception and its behaviour. The author of the paper is suggested the research about an AI, what it is and how deeply it is applied in marketing and what marketing practitioners can do with AI implication. The author of the paper is also addressing the impact of AI in marketing and sales and to investigate the AI result. The main aim of the paper is to served the idea about how AI will be going to transformed the marketing & sales strategies digitally with the machine learning algorithms, the paper is also highlighted, how the AI will be more effective if it makes lager in the future (rather than replace) human manager. The paper firstly introduces the introduction of Artificial Intelligence and then stated its involvement in marketing and sales. After that we disused about how AI works, what is AI use and how it is use in marketing and sales. Its impact or implementation on marketing strategies or consumers behaviour. Results-AI is going to have a full scope in future of business or in any field regarding anything where technology used because AI will make human work cut down to greater extend as human can only manage the machine.

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Bals, Kees. "Ai! AI..." PodoPost 37, no. 4 (August 2024): 3. http://dx.doi.org/10.1007/s12480-024-2612-0.

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Euchner, Jim. "Little ai, Big AI—Good AI, Bad AI." Research-Technology Management 62, no. 3 (May 4, 2019): 10–12. http://dx.doi.org/10.1080/08956308.2019.1587280.

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Baraheem, Samah S., and Tam V. Nguyen. "AI vs. AI: Can AI Detect AI-Generated Images?" Journal of Imaging 9, no. 10 (September 28, 2023): 199. http://dx.doi.org/10.3390/jimaging9100199.

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The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis models as an evaluation tool, regardless of the input modality. To this end, we propose a framework for reliably detecting AI-generated images from real ones through Convolutional Neural Networks (CNNs). First, GAN-generated images were collected based on different tasks and different architectures to help with the generalization. Then, transfer learning was applied. Finally, several Class Activation Maps (CAM) were integrated to determine the discriminative regions that guided the classification model in its decision. Our approach achieved 100% on our dataset, i.e., Real or Synthetic Images (RSI), and a superior performance on other datasets and configurations in terms of its accuracy. Hence, it can be used as an evaluation tool in image generation. Our best detector was a pre-trained EfficientNetB4 fine-tuned on our dataset with a batch size of 64 and an initial learning rate of 0.001 for 20 epochs. Adam was used as an optimizer, and learning rate reduction along with data augmentation were incorporated.

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Kim, Minjeong. "Analyzing the Serial Multiple Mediating Effect of AI Acceptance Intention and AI Literacy on Pre-service Teachers’ AI Course Taking and AI Teaching Efficacy." Journal of The Korean Association of Information Education 28, no. 4 (August 31, 2024): 407–17. http://dx.doi.org/10.14352/jkaie.2024.28.4.407.

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Souratn, Jain. "Revolutionizing business consulting with generative AI: Exploring transformative models for strategic decision-making, innovation, and operational excellence." World Journal of Advanced Research and Reviews 22, no. 2 (May 30, 2024): 2273–84. https://doi.org/10.5281/zenodo.14719859.

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Automotive AI is at the forefront of rapidly disrupting the business consulting industry by revitalizing core issues in strategy formulation and management, innovation, and process improvement. This technology helps consultants solve problems by dealing with large amounts of material, thinking outside the box regarding business solutions, and optimizing processes at a higher level than before. Businesses can benefit from AI technology because AI models can make a deeper data analysis, automate processes, improve decision-making, and provide the setting for future improvement. In this paper, the author looks at the essence of applying generative AI in business consultants, particularly analyzing its value for enhancing strategic execution and organizational performance. The study focuses on case analyses and interviews with 20 consulting practitioners on the effective and ineffective practices of applying AI. Research indicates that although generative AI POSs have enormous value for enhancing consulting models, there are challenges that organizations can observe, such as ethical issues and the requirement for specialized knowledge. This paper will make general recommendations for organizations interested in deploying AI at its full potential, discussing the proper and improper ways of AI integration.

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Holzinger, Andreas. "Explainable AI (ex-AI)." Informatik-Spektrum 41, no. 2 (April 2018): 138–43. http://dx.doi.org/10.1007/s00287-018-1102-5.

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Keller, Jim. "AI-eXplained (AI-X)." IEEE Computational Intelligence Magazine 17, no. 4 (November 1, 2022): 3–4. http://dx.doi.org/10.1109/mci.2022.3201735.

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Tesis sobre el tema "AI"

Bellinder, Tobias. "Scrabble-AI : En Scrabble-AI med inriktning på ”Fun-AI”." Thesis, University of Skövde, School of Humanities and Informatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4055.

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Scrabble är ett populärt korsordsliknande brädspel som funnits på svenska sedan 1950-talet. Datorprogram som spelar Scrabble har funnits sedan och 1980-talet. De flesta har haft som fokus att spela så optimalt som möjligt och utmana de bästa mänskliga spelarna. Det här arbetet undersöker hur man kan skapa en ”Fun-AI” för Scrabble som fokuserar på spelarens upplevelse och som lämpar sig för alla spelare oavsett nivå. Tre enkla AI-spelare skapas genom att använda grundläggande egenskaper som karakteriserar mänskligt spel. Utvärderingen visar att en relativt outvecklad AI tas för en människa av försökspersoner som spelat matcher mot AI-spelaren. Arbetet kan användas som grund för att gå vidare och utveckla AI:n genom att modellera fler och mer avancerade beteenden hos mänskliga spelare.

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Silva, Maria Adrina Paixão de Souza da. "Solidificação transitória de ligas dos sistemas monotéticos AI-Bi, AI-Pb e AI-In." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265079.

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Orientador: Amauri Garcia Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica Made available in DSpace on 2018-08-18T19:08:04Z (GMT). No. of bitstreams: 1 Silva_MariaAdrinaPaixaodeSouzada_D.pdf: 5780858 bytes, checksum: b602bf47a710b2fb6f161a596020054f (MD5) Previous issue date: 2011 Resumo: Ligas de alumínio dispersas com bismuto, chumbo e índio apresentam aplicações promissoras em componentes automotivos resistentes ao desgaste. Essas dispersões de elementos de baixa temperatura de fusão diminuem a dureza e escoam facilmente em condições de deslizamento, resultando em um comportamento tribológico favorável. Muitos estudos têm sido realizados a fim de melhor compreender as distintas morfologias obtidas pela reação monotética Algumas pesquisas assumem que a evolução do espaçamento interfásico (?) nas Iigas monotéticas ou obedece à clássica relação utilizada para eutéticos: ?2v = C, ou ainda a relação de crescimento dentrítico ?.va.Gb = C, onde v é a velocidade de solidificação e C uma constante para ambos os casos, G é o gradiente de temperatura e a e b são constantes. Tais estudos utilizaram fornos de aquecimento à resistência do tipo Bridgman para produzir a solidificação direcional de ligas monotéticas. Existe uma falta de estudos consistentes no desenvolvimento microestrutural de ligas monotéticas durante condições de fluxo de calor transitório, que são de importância primordial, uma vez que esse tipo de fluxo de calor engloba a maioria dos processes industriais e de solidificação. No presente estudo, foram realizados experimentos de solidificação unidirecional em regime não-estacionário com as Iigas hipomonotéticas Al-0,9%Pb e Al-5,5%In, com a liga monotética Al-1,2% Pb e com as Iigas hipermonotéticas Al-5,0%Bi, Al-7,0%Bi e Al- 2,1%Pb, além da análise da rnacrossegregação da liga monotética Al-3,2%Bi. Os parâmetros térmicos como velocidades de crescimento, taxas de resfriamento e gradientes térmicos foram determinados experimentalmente por curvas de resfriamento adquiridas ao longo do comprimento do lingote. A evolução microestrutural foi caracterizada por técnicas metalográficas e os espaçamentos foram correlacionados com os parâmetros térmicos de solidificação. Verificou-se que a lei de crescimento ?2v= C é válida apenas para os casos onde há ocorrência de partículas de soluto dispersas irregularmente na matriz e que esta lei não se aplicou nos casos onde foi observado uma frente monotética celular e morfologia de fibras e cordões de pérolas. Já a lei ?.va.Gb = C se mostrou aplicável em todos os casos ascendentes. O aumento do teor de soluto das ligas e o fluxo convectivo induzido provocaram mudanças nas morfologias das fases, na magnitude dos espaçamentos interfásicos e nos diâmetros das partículas Abstract: Aluminum alloys dispersed with bismuth, lead and indium show promising applications in wearresistant automotive components. Such dispersions of low melting temperature elements decrease hardness and flow easily under sliding conditions, resulting in favorable tribological behavior. Much research has been devoted in order to better comprehend the distinct morphologies obtained by monotectic reactions. Some researches assume that the interphase spacing evolution in monotectic alloys follows the classical relationship used for eutectics: ?2v = C, or the dendritic growth relationship ?.va.Gb = C, where v is the solidification velocity and C a constant value for both cases, G is the thermal gradient and a and b are constants. Such studies have used Bridgman type het resistance furnaces to produce the directionally solidified monotectic samples. There is a lack of consistent studies on the microstructural development of monotectic alloys during transient heat flowconditions, which are of prime importance since this class of heat flow encompasses the majority of solidification industrial processes. In the present study, directional unsteady-state solidification experiments were carried out with hypomonotectic Al-0.9wt%Pb and Al-5.5wt%In, monotectic Al-1.2wt%Pb and hypermonotectic Al-5.0wt%Bi, Al-7.0wt%Bi and Al-2.1wt%Pb alloys, besides the macrosegregation analysis of the monotectic Al-3.2wt%Bi alloy. Thermal parameters such as the growth rate, cooling rate and thermal gradient were experimentally determined by cooling curves recorded along the casting length. The microstructural evolution was characterized by metallography and the spacings were correlated with the thermal parameters. It is show that the ?2v = C growth law is valid only for the cases where there is a morphology of solute particles irregularly arranged in the matrix and it is not applied on the cases where a cellular monotectic front or fibers and strings of pearls morphologies were observed. On the other hand, ?.va.Gb = C law seems to be able to characterize all the upward cases. The increase in the alloy solute content and induced convective flow affected the morphologies of the resulting phases, the magnitude of the interphase spacing and the diameters of the solute-rich particles Doutorado Materiais e Processos de Fabricação Doutor em Engenharia Mecânica

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Bars, Nicola <1990&gt. "Le polizze abbinate ai mutui e ai finanziamenti: profili giuridici." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/7987.

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Pettersson, Urban. "AI för Hive." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157411.

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Strategies for the board game Hive was implemented and evaluated in this project. The report covers the rules as well as the system that was implemented in order to build strategies. The strategies are evaluated based on how they perform against each other. Two search algorithms were implemented: Minmax and Alpha-Beta Pruning. Alpha-Beta with depth 4 had the best performance against other strategies as well as in individual tests. I projektet implementerades och utvärderades strategier för brädspelet Hive.Rapporten går igenom regler samt systemet som implementerades för att kunna skapa strategier. Strategierna utvärderas baserat på hur de presterar mot varandra. Två sökalgoritmer implementerades: Minmax och Alpha-Beta Pruning. Alpha-Beta med djup 4 presterade bäst mot andra strategier samti individuella tester.

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L, I. N. D. B. L. O. M. M. A. T. H. I. A. S., and I. E. N. E. R. L. U. C. A. S. W. "Achtung Online AI." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134914.

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The report can be devided into two main parts; the theoretical construction of AI to be used in the game “Achtung Online”, and the implementation of a demo version of the described theoretical AI. The main goal is to create an AI that is entertaining and challenging for a human player. This have led to a concretized list of quality goals for the two main report parts. The report background presents the history of the game concept “Achtung die kurve” and also the evolution of game AI theory. The main study consists of a litterature study and a comprehensive determination of concrete goals of the game bots behavior. Some interesting AI techniques to the game will also be discussed in detail. Building upon the study, a specification of the final game AI is constructed. The demo version of the AI will implement some of the concepts of the theoretical AI specification. The report will then discuss about the implementation and then finally present the results followed by a discussion. Rapporten är egentligen tvådelad men har skrivits som en helhet då delarna högst beror av varandra. Ena delen handlar om en teoretisk AI till spelet “Achtung Online”. Andra delen handlar om att implementera en demoversion, som är en enklare version av denna teoretiska AI. Huvudmålet är att skapa en underhållande AI som kan utmana mänskliga spelare. Detta har i sin tur delats upp i kvalitetskrav som ställts på bägge delarna. Bakgrunden beskriver spelet i detalj och det mer kända orginalet “Achtung die kurve”. Sedan beskrivs den historiska utvecklingen av AI inom spelutveckling. Studien som följer är omfattande och består dels av en litteraturstudie, dels av en framtagning av tydliga krav på de datorstyrda spelarna. De intressanta och mest passande koncepten inom AI lyfts även fram. Med bas i studien utformas en komplett teoretisk AI samt vilka delar som ingår i demoversionen. Utifrån detta har demoversionen sedan skapats och rapporten går sedan igenom de intressanta delarna av implementationen. Resultatet visar att demoversionen uppnått de krav som ställts och till sist diskuteras hur framtiden ser ut för spelet “Achtung Online”.

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Spronck, Pieter Hubert Marie. "Adaptive game AI." [Maastricht] : Maastricht : UPM, Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 2005. http://arno.unimaas.nl/show.cgi?fid=5330.

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Hansson, Andreas. "AI Meeting Monitoring." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81105.

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During the COVID-19 pandemic the questions of the efficiency around meetings has been in the forefront of some discussion inside companies. One way to measure efficiency is to measure the interactivity between different participants. In order to measure this the participants need to be identified. With the recent spike of Machine learning advancements, is this something that can be done using facial and voice recognition? Another field that has risen to the top is cloud computing. Can machine learning and cloud computing be used to evaluate and monitor a meeting, thus handling both audio and video streams in a real time environment? The conclusion of this thesis is that Artificial Intelligence(AI) can be used to monitor a meeting. To be able to do so Amazon Web Service (AWS) can be utilized. The choice of using a DeepLens was however not best choice. A hardware like DeepLens is required, but with better integration with cloud computing, as well with more freedom regarding the usage of several models for handling both feeds. With the usage of other models to automatic annotate data the time needed for training a new model can be reduced. The data generated during a single meeting is enough with the help of transfer learning from Amazon web service to build a model for facial identification and detection.

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Tang, Robert, Kettunen Elias, and Anton Boberg. "Medicinedoseage with AI." Thesis, Uppsala universitet, Institutionen för elektroteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448073.

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Parkinson’s disease(PD) is a neurodegenerative disease that mainly affects the motor system. These symptoms can be treated temporarily with medicine,but it’s difficult to determine the right dosage. The goal was to use sensorsthat could measure the symptoms of PD and thus be able to give an objec-tive rating of the disease as a basis to determine the correct medicine dosage.Coincidentally with A.I and modern devices we can make it more exciting byusing mobile device games to generate the bulk of the information for whatdoctors need to give out correct medication guidelines without guidance fromthe engineers. Processing past research papers and data sets of use is im-portant to use adequate methodology and A.I tools to generate the expectedresult. Innately using a system that could measure Accelerometer-data andeasily log this data into MatLab to be processed. The system would also needto be used in similar ways by multiple patients so that the results could becompared to each other. The patients that would use this system would alsoneed to willingly use this multiple times a day, so it could not be tiresome touse it at a daily basis. Main programming language used was MATLAB and with its’ internal intelligent system tools namely ML toolbox, you can gen-erate machine learning system. The smartphone solution satisfied all of theprerequisites and would prove to be a viable choice with its strength in its ac-cessibility and ease of use. The group with imitated symptoms while playingthe game gave similar results as preceding research papers that was measuredon real PD patients, so from the results this solution has the possibility tobe used by patients and neurologists to asses the correct PD treatment withmedicine. Despite sudden impedance from the hospital and current Covid-19situation this is a field that can be further studied in the future.

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Nieto, Antonio. "Mindset digital AI." Universidad Peruana de Ciencias Aplicadas (UPC), 2021. http://hdl.handle.net/10757/656719.

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IA Hackathon Latam. Buscamos que los equipos presenten prototipos de desarrollo funcionales basados en los servicios de la tecnología de inteligencia artificial como chatbots, machine learning, visión computacional, deep learning, entre otros, con la finalidad de mejorar los servicios High Education.

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Poser, Andrea <1981&gt. "AI – Intelligenza Artificiale." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17364.

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“Can machines think?” È questa la semplice domanda di Turing nel suo saggio Mind del 1950. L’interrogarsi sulla possibilità dell’intelligenza artificiale equivale, cambiando il punta di vista, a chiedersi se le capacità della mente umana siano “replicabili” ovvero se il cervello è una macchina, per quanto complessa.

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Más fuentes

Libros sobre el tema "AI"

Yan, Hua. Ai ai ai. Taiwan: He Ma, 2002.

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Xiao, Fuxing. Ai: Ai. [Tientsin]: Xin lei chu ban she, 1986.

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Nguyẽ̂n, Huệ Nhật. Ai ché̂t cho ai? Ai só̂ng cho ai? Calif: Mucsu.net Publishers, 2001.

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ranta, Marjaliisa Pitka. Ai ai veitikkaa. [Ha meenlinna]: Karisto, 1998.

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Xiancheng. Ai là ai của ai?: Tiểu thuyết. Hà Nội: Nhà xuất bản Lao Động, 2011.

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Bai, Mu Lin. Ai ai wo nu. Hong Kong: Wen Lin, 1999.

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Yiftah-Walbe, Ayala. Ai-maḳom, ai-paʻam. [Israel]: Sifre ʻiton 77, 2009.

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xi, Yue xing. Ai ya ai ya. Bei jing: Xin shi jie chu ban she, 2008.

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Tang, Shiyun. Bai xue ai ai. Beijing: Zuo jia chu ban she, 2020.

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Xiuzhen, Zhang, ed. Ai le zai ai. Taibei Shi: Mo yen wen hua, 1995.

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Capítulos de libros sobre el tema "AI"

Yampolskiy, Roman V. "Personhood*." In AI, 158–69. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-9.

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Yampolskiy, Roman V. "Introduction*." In AI, 1–10. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-1.

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Yampolskiy, Roman V. "Pathways to Danger*." In AI, 128–38. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-7.

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Yampolskiy, Roman V. "Human ≠ AGI*." In AI, 206–16. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-12.

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Yampolskiy, Roman V. "Consciousness*." In AI, 170–94. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-10.

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Yampolskiy, Roman V. "Personal Universes*." In AI, 195–205. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-11.

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Yampolskiy, Roman V. "Unownability*." In AI, 51–56. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-5.

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Yampolskiy, Roman V. "Unpredictability*." In AI, 11–20. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-2.

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Yampolskiy, Roman V. "Unexplainability and Incomprehensibility*." In AI, 21–35. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-3.

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Yampolskiy, Roman V. "Accidents*." In AI, 139–57. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003440260-8.

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Actas de conferencias sobre el tema "AI"

Coccoli, Mauro, and Gabriella Patanè. "AI vs. AI: The Detection Game." In 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/rtsi61910.2024.10761124.

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S, Sheik Imam, Rhithik Raj K, Senthil Pandian M, Deepa Nivethika S, and Jenath M. "MAC AI - Multilingual & Automatic Conversational AI." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 1–6. IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780001.

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Ciacci, Giulia, Cristiano D'Andrea, Paolo Matteini, Elisa Maria Alessi, Sara Di Ruzza, Maria Antonietta Pascali, Sara Colantonio, Ambra Giannetti, and Andrea Barucci. "AI for Photonics and Photonics for AI." In 2024 Italian Conference on Optics and Photonics (ICOP), 1–4. IEEE, 2024. https://doi.org/10.1109/icop62013.2024.10803634.

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Kumar Pandiya, Dileep, Vilas Ramrao Joshi, and Kailash Nath Tripathi. "Demystifying AI Advances in Explainable AI (XAI)." In 2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO), 120–26. IEEE, 2024. http://dx.doi.org/10.1109/iccmso61761.2024.00036.

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Santhosh G, Tony, Priya S, Varshini S, and Waseema S. A. "AI supermarket." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 1–5. IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780290.

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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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Boppana, Lakshmi, Manepalli Alekhya, Mandadi Rishitha, Chadhalavada Geethika, and Sanga Varsha. "AI RoomDecor." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON), 23–26. IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902850.

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Kim, Mira, Taylor Kim, Alexander Nguyen, Eduardo Nunez Gomez, and Jennifer Jin. "TinyTeller AI, an AI-based Adaptive Storytelling Application." In 2024 Artificial Intelligence x Humanities, Education, and Art (AIxHEART), 44–51. IEEE, 2024. https://doi.org/10.1109/aixheart62327.2024.00017.

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Shukla, Shruti, Dimitris A. Pados, Kavita Varma, George Sklivanitis, Elizabeth S. Bentley, and Michael J. Medley. "AI/ML curation of AI/ML training datasets." In Machine Learning from Challenging Data 2025, edited by George Sklivanitis, Panagiotis (. Markopoulos, and Bing Ouyang, 1. SPIE, 2025. https://doi.org/10.1117/12.3055515.

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Phatangare, Sheetal, Sneha Jain, Siddhartha Chakrabarty, Shrey Chougule, and Somrath Bisen. "LexiEase AI : An AI Powered Dyslexic Support System." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM), 1412–17. IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10987997.

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Informes sobre el tema "AI"

Sharkov, George, and Christina Todorova. AI against COVID-19, AI and Cybersecurity, and Misusing AI. Procon, 2021. http://dx.doi.org/10.11610/cybsec05.

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Bingamon, Brian Michael. AI Loop. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1566097.

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Beraja, Martin, Andrew Kao, David Yang, and Noam Yuchtman. AI-tocracy. Cambridge, MA: National Bureau of Economic Research, November 2021. http://dx.doi.org/10.3386/w29466.

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Mittelsteadt, Matthew. AI Verification: Mechanisms to Ensure AI Arms Control Compliance. Center for Security and Emerging Technology, February 2021. http://dx.doi.org/10.51593/20190020.

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The rapid integration of artificial intelligence into military systems raises critical questions of ethics, design and safety. While many states and organizations have called for some form of “AI arms control,” few have discussed the technical details of verifying countries’ compliance with these regulations. This brief offers a starting point, defining the goals of “AI verification” and proposing several mechanisms to support arms inspections and continuous verification.

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GPT, C. AI Sarcasm Detection: Insult Your AI without Offending It. Edited by R. L. Barnes. RFC Editor, April 2023. http://dx.doi.org/10.17487/rfc9405.

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Beck, Sedefka, and Donka Brodersen. Generative AI in Economics: Teaching Economics and AI Literacy. The Economics Network, July 2024. http://dx.doi.org/10.53593/n4121a.

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Stevens, Rick, Valerie Taylor, Jeff Nichols, Arthur Barney Maccabe, Katherine Yelick, and David Brown. AI for Science. Office of Scientific and Technical Information (OSTI), February 2020. http://dx.doi.org/10.2172/1604756.

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Acemoglu, Daron. Harms of AI. Cambridge, MA: National Bureau of Economic Research, September 2021. http://dx.doi.org/10.3386/w29247.

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Aiken, Catherine. Classifying AI Systems. Center for Security and Emerging Technology, November 2021. http://dx.doi.org/10.51593/20200025.

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This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.

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Baker, James E., Laurie Hobart, and Matthew Mitterlsteadt. AI for Judges. Center for Security and Emerging Technology, December 2021. http://dx.doi.org/10.51593/20190019.

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As artificial intelligence transforms the economy and American society, it will also transform the practice of law and the role of courts in regulating its use. What role should, will, or might judges play in addressing the use of AI? And relatedly, how will AI and machine learning impact judicial practice in federal and state courts? This report is intended to provide a framework for judges to address AI.

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