Artículos de revistas: "AI"

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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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3

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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4

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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5

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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7

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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9

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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10

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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Baungarten-Leon, Emilio Isaac, Susana Ortega-Cisneros, Mohamed Abdelmoneum, Ruth Yadira Vidana Morales, and German Pinedo-Diaz. "The Genesis of AI by AI Integrated Circuit: Where AI Creates AI." Electronics 13, no. 9 (April 28, 2024): 1704. http://dx.doi.org/10.3390/electronics13091704.

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The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AI by AI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AI by AI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs.

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12

Aatmaj, Amol Salunke. "Cracking the Code: Self-Explaining AI Models for Transparent Decision Making in Complex Algorithms." International Journal for Multidisciplinary Research (IJFMR) 5, no. 4 (August 16, 2023): 10. https://doi.org/10.5281/zenodo.8268922.

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This research paper explores self-explaining AI models that bridge the gap between complex black-box algorithms and human interpretability. The study focuses on techniques like LIME, SHAP, attention mechanisms, and rule-based systems to create locally interpretable models. By providing transparent and understandable explanations for AI predictions, these models enhance user trust and comprehension. Realworld applications in healthcare, finance, and autonomous systems are evaluated to demonstrate the effectiveness of self-explaining AI models. Ethical considerations regarding fairness, bias, and accountability in AI decision-making are also addressed. The findings underscore the potential of such models to unlock the mysteries of complex algorithms, making AI more accessible and interpretable for diverse applications.

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13

Ravindra, Kumar. "Biases in Artificial Intelligence Applications Affecting Human Life: A Review." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 1 (May 30, 2021): 54–55. https://doi.org/10.35940/ijrte.A5719.0510121.

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The introduction of Artificial Intelligence has improved operations in almost every sector, industry, and part of human life. The use of AI has been vital in the department of justice, recruitment by organizations, facial recognition by police, and school admissions. The aim of introducing AI algorithms in various fields was to reduce human bias in decision-making. Despite the progress, there are ethical concerns that the AI algorithms also exhibit biases. The main reason behind the claim is because human developers are in charge of training data used by the algorithms. There are areas where the issue of biases affects human life directly and can do damages to a person, physically or emotionally. Some examples are college admissions, recruitment, administration of justice at the courts, public benefits systems, police, public safety, and healthcare. There are high chances that the development process introduced biases in artificial intelligence algorithms, knowingly or unknowingly, during any area mentioned above. The paper provides background knowledge on AI bias and possible solutions to solve the problem.

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Siva, Karthik Devineni. "AI Data Quality Copilots: Enhancing Intelligent Systems with Real-Time Data Integrity, Scalability, and Ethical AI Practices." Journal of Scientific and Engineering Research 11, no. 10 (October 31, 2024): 8–26. https://doi.org/10.5281/zenodo.13991320.

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AI has proven to be a quickly growing field; hence, data quality is essential when attaining proper, equitable, and lasting AI solutions. Data quality is critically important as organizations rely more on AI for their decisions, training, and decisions. This paper aims to establish AI Data Quality Copilots; sophisticated systems focused on addressing data-related issues through automatic real-time data quality evaluation and enhancement. Future issues – data drift, privacy, and inclusion – as verified by AI Data Quality Copilots will remain non-triggering for the AI model's reliability and nonpartisan nature. This paper examines how DQ Copilot enables enterprises to answer those challenges arising from new trends affecting data, with examples from healthcare, financial, and retail industries, among many more. It also talks about how the applications of copilots foster more decision-making content at base levels in real time and reduce false fraud signals, as well as how ethical AI is achievable through identifying biases and tweaking them. By connecting these copilots, enterprises can extend and enrich AI with suitable data management and keep valuable AI models with constant good data inputs. The paper also looks at the various ethical aspects that are associated with the management of data quality. This illustrates how AI Data Quality Copilots bring fairness to AI thinking. The paper concludes by arguing that data quality copilots are crucial in both the sustainability and variability of AI within the relevant domains.

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15

Kortz, Mason, Jessica Fjeld, Hannah Hilligoss, and Adam Nagy. "Is Lawful AI Ethical AI?" Morals & Machines 2, no. 1 (2022): 60–65. http://dx.doi.org/10.5771/2747-5174-2022-1-60.

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Attempts to impose moral constraints on autonomous, artificial decision-making systems range from “human in the loop” requirements to specialized languages for machine-readable moral rules. Regardless of the approach, though, such proposals all face the challenge that moral standards are not universal. It is tempting to use lawfulness as a proxy for morality; unlike moral rules, laws are usually explicitly defined and recorded – and they are usually at least roughly compatible with local moral norms. However, lawfulness is a highly abstracted and, thus, imperfect substitute for morality, and it should be relied on only with appropriate caution. In this paper, we argue that law-abiding AI systems are a more achievable goal than moral ones. At the same time, we argue that it’s important to understand the multiple layers of abstraction, legal and algorithmic, that underlie even the simplest AI-enabled decisions. The ultimate output of such a system may be far removed from the original intention and may not comport with the moral principles to which it was meant to adhere. Therefore, caution is required lest we develop AI systems that are technically law-abiding but still enable amoral or immoral conduct.

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La Rosa, Michele, and Enrica Morlicchio. "Ai collaboratori e ai lettori." SOCIOLOGIA DEL LAVORO, no. 153 (March 2019): 7. http://dx.doi.org/10.3280/sl2019-153001.

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BIRMINGHAM, WILLIAM P. "The AI in AI EDAM." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 5–6. http://dx.doi.org/10.1017/s0890060407070035.

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A natural question is why AI in design? Although the design applications written about in the journal vary widely, the common thread is that researchers use AI techniques to implement their ideas. The use of AI techniques for design applications, at least when AI EDAM was started, was partially a reaction against the predominant design methods based on some form of optimization. Knowledge-based techniques, particularly rule-based systems of various sorts, were very popular. One of the draws of these methods, I believe, was their ability to represent knowledge that is hard or awkward to represent in traditional optimization frameworks. This mirrors my experience: at the time, I was working in configuration with components that had a large number compatibility and resource constraints. Although many constraints could be represented in mixed integer linear programming systems, it was not easy to conceptualize, write, and most importantly, maintain the constraints in those systems.

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笹嶋, 宗彦. "AI戦略（AI Strategy）". Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 35, № 2 (15 травня 2023): 25. http://dx.doi.org/10.3156/jsoft.35.2_25_1.

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Manning, Ryan Vincent. "Tech Limited: AI is AI." Architectural Design 94, no. 3 (May 2024): 94–101. http://dx.doi.org/10.1002/ad.3060.

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AbstractWhat might a conversation between a highly trained generator that calculates the probability of the next word in a sentence, and the jumbled mess of organic neurons inside the head of a human architect look like? Through such a fictitious dialogue, architectural designer and educator Ryan Vincent Manning explores issues of human inquisitiveness, uniqueness and agency, human‐ machine interfaces, machine intelligence, AI latent spaces and the assimilation of design originality into free‐access, ubiquitous machine code that is adding to huge potential datasets.

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ZAVATIN (CHILEA), Iuliana, Ioana Maria POPESCU (IACOBESCU), Rodica LUPU (ȘTEFĂNESCU), and Andra-Elena ORLOSCHI. "AI REGULATION VS. AI STANDARDIZATION." Revista Economica 76, no. 3 (September 2, 2024): 113–26. https://doi.org/10.56043/reveco-2024-0030.

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The key specificity of Artificial Intelligence today is that it is a fast-evolving field with yet unknown capabilities and potential. In technical research as well as in media there are new emerging AI-related topics almost every day. We hear a lot about risks of AI but also about so many useful AI applications that help people and companies benefit from better lives or enhanced results of their activity. Businesses developing AI solutions worldwide face significant challenges not only in designing processes but also in putting on the market safe, efficient and reliable technologies. Thus, there is huge time pressure to set the necessary regulatory frame that will enable proper market functioning and encourage innovation in this area. On the other hand, there is considerable pressure from society to have a safe and trustworthy environment to use AI technology. Such expectations are only reasonable as AI development affects all the society and economy at all levels. Therefore, it’s important to identify the best regulatory option. Is there a need for binding rules and coercive state measures to regulate markets and social behavior? Or could the consensual technical requirements and ethical principles provide the necessary direction for a reasonable development, function and use of AI? This paper aims at formulating the most suitable regulatory framework to guide further development of AI. The two-step research methodology focuses on analyzing the incipient regulatory initiatives on AI, exploring the published articles in the area as well as the recently approved standards at international level. Based on this analysis authors will assess the pros and cons for the two regulatory options and will further elaborate the arguments for the best setting to regulate AI.

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Chilea, Iuliana, Maria Popescu (Iacobescu) Ioana, Rodica Lupu, and Andra ORLOSCHI. "AI REGULATION VS. AI STANDARDIZATION." Revista Economica 76, no. 3 (September 1, 2024): 113–26. https://doi.org/10.56043/reveco-2024-0030.

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The key specificity of Artificial Intelligence today is that it is a fast-evolving field with yet unknown capabilities and potential. In technical research as well as in media there are new emerging AI-related topics almost every day. We hear a lot about risks of AI but also about so many useful AI applications that help people and companies benefit from better lives or enhanced results of their activity. Businesses developing AI solutions worldwide face significant challenges not only in designing processes but also in putting on the market safe, efficient and reliable technologies. Thus, there is huge time pressure to set the necessary regulatory frame that will enable proper market functioning and encourage innovation in this area. On the other hand, there is considerable pressure from society to have a safe and trustworthy environment to use AI technology. Such expectations are only reasonable as AI development affects all the society and economy at all levels.Therefore, it’s important to identify the best regulatory option. Is there a need for binding rules and coercive state measures to regulate markets and social behavior? Or could the consensual technical requirements and ethical principles provide the necessary direction for a reasonable development, function and use of AI? This paper aims at formulating the most suitable regulatory framework to guide further development of AI. The two-step research methodology focuses on analyzing the incipient regulatory initiatives on AI, exploring the published articles in the area as well as the recently approved standards at international level. Based on this analysis authors will assess the pros and cons for the two regulatory options and will further elaborate the arguments for the best setting to regulate AI.

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Park, SunJu. "AI education perception of pre-service teachers according to AI learning experience, Interest in AI education, and Major." Journal of The Korean Association of Information Education 25, no. 1 (February 28, 2021): 103–11. http://dx.doi.org/10.14352/jkaie.2021.25.1.103.

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Yoo, Sunghee. "Ethical Issues Posed by ‘Generative-AI’ (G-AI) - Response strategies for ‘Good AI Society’." Journal of the Korean Bioethics Association 24, no. 1 (June 30, 2023): 1–29. http://dx.doi.org/10.37305/jkba.2023.06.24.1.1.

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van Wynsberghe, Aimee. "Sustainable AI: AI for sustainability and the sustainability of AI." AI and Ethics 1, no. 3 (February 26, 2021): 213–18. http://dx.doi.org/10.1007/s43681-021-00043-6.

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AbstractWhile there is a growing effort towards AI for Sustainability (e.g. towards the sustainable development goals) it is time to move beyond that and to address the sustainability of developing and using AI systems. In this paper I propose a definition of Sustainable AI; Sustainable AI is a movement to foster change in the entire lifecycle of AI products (i.e. idea generation, training, re-tuning, implementation, governance) towards greater ecological integrity and social justice. As such, Sustainable AI is focused on more than AI applications; rather, it addresses the whole sociotechnical system of AI. I have suggested here that Sustainable AI is not about how to sustain the development of AI per say but it is about how to develop AI that is compatible with sustaining environmental resources for current and future generations; economic models for societies; and societal values that are fundamental to a given society. I have articulated that the phrase Sustainable AI be understood as having two branches; AI for sustainability and sustainability of AI (e.g. reduction of carbon emissions and computing power). I propose that Sustainable AI take sustainable development at the core of its definition with three accompanying tensions between AI innovation and equitable resource distribution; inter and intra-generational justice; and, between environment, society, and economy. This paper is not meant to engage with each of the three pillars of sustainability (i.e. social, economic, environment), and as such the pillars of sustainable AI. Rather, this paper is meant to inspire the reader, the policy maker, the AI ethicist, the AI developer to connect with the environment—to remember that there are environmental costs to AI. Further, to direct funding towards sustainable methods of AI.

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Janssen, T. "SP-0541 AI^2: When the AI checks the AI." Radiotherapy and Oncology 182 (May 2023): S427. http://dx.doi.org/10.1016/s0167-8140(23)67400-6.

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Bokdash, Saed. "AI-Imam AI- Shaikh M. Aabid AI- Sendi AI-Ansari and his Scientific and.Feqhia Efforts." Journal of King Abdulaziz University-Educational Sciences 16, no. 1 (2003): 95–191. http://dx.doi.org/10.4197/edu.16-1.6.

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Lee, Keun-woo. "AI as a tool, AI era and Response of the Criminal Law." Gachon Law Review 18, no. 1 (March 31, 2025): 41–68. https://doi.org/10.15335/glr.2025.18.1.002.

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Dong, Hwa Kim. "AI Curriculum Design for Korea K-12 AI Education Through Analyzing AI Education Curriculum." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (September 30, 2023): 72–81. https://doi.org/10.35940/ijrte.C7860.0912323.

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This paper deals with the curriculum design of Korean K-12 AI education. AI is the core technology of 4th wave and it is impact is wide and strong. Therefore, every country has s strong nurture system for AI manpower. However, there are few materials about curriculum design and operation experience documentation because AI was an interest since 2016, and its education is very new. In Korea, they started a program for teacher manpower education in 2020. Therefore, there is no manpower that had an experience in the design and operation of the K-12 AI curriculum. The AI curriculum is a core parameter for AI manpower. So, AI-advanced countries try to develop AI education methods. This paper analyzes the K-12 AI curriculum of the advanced countries, and then designs and suggests Korea’s K-12 AI curriculum.

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Bordalejo, Barbara, Davide Pafumi, Frank Onuh, AKM Iftekhar Khalid, Morgan S. Pearce, and Daniel O'Donnell. ""Scarlet Cloak and the Forest Adventure": a preliminary study of the impact of AI on commonly used writing tools." International Journal of Educational Technology in Higher Education volume 22, no. 6 (March 11, 2025): 1–25. https://doi.org/10.5281/zenodo.15014534.

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This paper explores the growing complexity of detecting and differentiating generative AI from other AI interventions. Initially prompted by noticing how tools like Grammarly were being flagged by AI detection software, it examines how these popular tools such as Grammarly, EditPad, Writefull, and AI models such as ChatGPT and Microsoft Bing Copilot affect human-generated texts and how accurately current AI-detection systems, including Turnitin and GPTZero, can assess texts for use of these tools. The results highlight that widely used writing aids, even those not primarily generative, can trigger false positives in AI detection tools. In order to provide a dataset, the authors applied different AI-enhanced tools to a number of texts of different styles that were written prior to the development of consumer AI tools, and evaluated their impact through key metrics such as readability, perplexity, and burstiness. The findings reveal that tools like Grammarly that subtly enhance readability also trigger detection and increase false positives, especially for non-native speakers. In general, paraphrasing tools score low values in AI detection software, allowing the changes to go mostly unnoticed by the software. However, the use of Microsoft Bing Copilot and Writefull on our selected texts were able to eschew AI detection fairly consistently. To exacerbate this problem, traditional AI detectors like Turnitin and GPTZero struggle to reliably differentiate between legitimate paraphrasing and AI generation, undermining their utility for enforcing academic integrity. The study concludes by urging educators to focus on managing interactions with AI in academic settings rather than outright banning its use. It calls for the creation of policies and guidelines that acknowledge the evolving role of AI in writing, emphasizing the need to interpret detection scores cautiously to avoid penalizing students unfairly. In addition, encouraging openness on how AI is used in writing could alleviate concerns in the research and writing process for both students and academics. The paper recommends a shift toward teaching responsible AI usage rather than pursuing rigid bans or relying on detection metrics that may not accurately capture misconduct.

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Miller, David P., R. James Firby, Paul A. Fishwick, and Jeff Rothenberg. "AI." ACM Transactions on Modeling and Computer Simulation 2, no. 4 (October 1992): 269–84. http://dx.doi.org/10.1145/149516.149519.

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Clancey, William J. "AI." ACM Computing Surveys 27, no. 3 (September 1995): 320–22. http://dx.doi.org/10.1145/212094.212110.

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Wu, Xianchao. "When Creative AI Meets Conversational AI." Journal of Natural Language Processing 28, no. 3 (2021): 881–87. http://dx.doi.org/10.5715/jnlp.28.881.

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Sūna, Laura, and Dagmar Hoffmann. "From AI imaginaries to AI literacy." MedieKultur: Journal of media and communication research 40, no. 76 (August 30, 2024): 53–76. http://dx.doi.org/10.7146/mk.v40i76.137144.

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Based on the results of a qualitative study on how migrants experience technologies of automation in everyday life, the article describes users’ imaginaries of artificial intelligence as the overall technology behind different digital media applications. This encompasses the subjective idea of users about what AI technology is, what it can do, and what it should do. All respondents share a general understanding of AI as a feeling and awareness that the technology has its own logic of some kind, as articulated in recommendation algorithms on TikTok, YouTube or Netflix, language correction on WhatsApp or email programs, translation apps, but also in voice assistants like Amazon Alexa or Siri. By analytically linking the two concepts of AI imaginaries and AI literacy, a perspective is developed that focuses on culturally-shaped ideas about technology and the subjectively perceived agency is thus analyzed in the context of the technologies of automation.

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Moehle, Matthew R., Roopa Nandi, and Hardik Shah. "AI Practitioner November 2013 - AI Resources." AI Practitioner 15, no. 3 (November 1, 2013): 71–75. http://dx.doi.org/10.12781/978-1-907549-17-5-10.

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35

Vaux, Janet. "AI is dead; long live AI." Expert Systems 11, no. 1 (February 1994): 49. http://dx.doi.org/10.1111/j.1468-0394.1994.tb00317.x.

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Johnson, Deborah G., and Mario Verdicchio. "Ethical AI is Not about AI." Communications of the ACM 66, no. 2 (January 20, 2023): 32–34. http://dx.doi.org/10.1145/3576932.

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Hollands, Fiona, and Cynthia Breazeal. "Establishing AI Literacy before Adopting AI." Science Teacher 91, no. 2 (March 3, 2024): 35–42. http://dx.doi.org/10.1080/00368555.2024.2308316.

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Sreejani, K. "Melody AI – An AI Music Generator." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (March 10, 2025): 1–9. https://doi.org/10.55041/ijsrem42228.

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MELODY AI is a music generation system based on AI that converts textual descriptions into novel musical pieces. Built with the Hugging Face model, the system uses deep learning algorithms to create music from user-input prompts. The process entails processing user-provided prompts, representation of these prompts in musical form, and synthesis of related audio outputs. The model has been optimized to preserve rich musical structures, allowing for coherent and expressive music to be generated across various genres. The model is successful in preserving the spirit of user inputs, generating expressive and relevant melodies. The outputs reflect the ability of the model to generate contextually relevant and beautiful music, making it an important asset for filmmakers, content creators, and artists. This study identifies developments in AI-augmented music composition and its potential to transform the creative sector. Keywords: AI music generation, deep learning, music synthesis, Hugging Face, text-to-music, MusicGen.

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Goel, Ashok K., and David A. Joyner. "Using AI to Teach AI: Lessons from an Online AI Class." AI Magazine 38, no. 2 (July 1, 2017): 48–59. http://dx.doi.org/10.1609/aimag.v38i2.2732.

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In fall 2014, we launched a foundational course in artificial intelligence (CS7637: Knowledge-Based AI) as part of the Georgia Institute of Technology's Online Master of Science in Computer Science program. We incorporated principles and practices from the cognitive and learning sciences into the development of the online AI course. We also integrated AI techniques into the instruction of the course, including embedding 100 highly focused intelligent tutoring agents in the video lessons. By now, more than 2000 students have taken the course. Evaluations have indicated that OMSCS students enjoy the course compared to traditional courses, and more importantly, that online students have matched residential students' performance on the same assessments. In this article, we present the design, delivery, and evaluation of the course, focusing on the use of AI for teaching AI. We also discuss lessons we learned for scaling the teaching and learning of AI.

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Miyamoto, Michiko. "Measuring AI Governance, AI Adoption and AI Strategy of Japanese Companies." International Journal of Membrane Science and Technology 10, no. 1 (October 11, 2023): 649–57. http://dx.doi.org/10.15379/ijmst.v10i1.2627.

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Purpose: This study aims to measure the level of AI governance and AI adoption among Japanese companies. Theoretical Framework: The research investigates the extent to which Japanese companies have implemented AI governance frameworks and the degree of AI adoption in their operations. The study also explores the relationship between AI governance, AI adoption, and AI strategy, providing insights into the factors that influence successful AI implementation. Design / Methodology / Approach: a survey questionnaire was administered to a representative sample of Japanese companies across various industries. The questionnaire included items that assessed the presence and effectiveness of AI governance practices within the organizations. Findings: a positive correlation was observed between AI governance and AI adoption. Companies with well-established AI governance frameworks tended to have higher levels of AI adoption, suggesting that effective governance practices play a crucial role in facilitating successful AI implementation. These findings provide valuable insights into the current state of AI governance and AI adoption among Japanese companies. Conclusion: The results can assist organizations in benchmarking their AI initiatives against industry standards and identifying areas for improvement. Policymakers and regulators can also utilize these findings to develop guidelines and frameworks that promote responsible and effective AI implementation.

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Kumar, Neeraj Sachdev, and Mehra Aryan. "An Exploration of Philosophical Ideas of Select Thinkers in Artificial Intelligence." International Journal of Social Science And Human Research 05, no. 12 (December 9, 2022): 5455–57. https://doi.org/10.5281/zenodo.7417694.

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We attempt to explore the presence and working of philosophical ideas of select thinkers in the whole architecture of artificial intelligence. This exploration delves into the philosophical connections relating to the proof of the existence of artificial intelligence. It further examines the philosophical perspectives of strong and weak versions of artificial intelligence, language processing and the inner working of artificial intelligence itself.

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Chandra, Sekhar Veluru. "The Impact of Generative AI on Content Curation and Content Advancements in Education and Training." European Journal of Advances in Engineering and Technology 11, no. 4 (April 30, 2024): 121–30. https://doi.org/10.5281/zenodo.12737572.

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Generative AI, a branch of artificial intelligence, has gained significant attention and interest for its potential applications in various fields, including education and training. Its integration in educational settings has sparked curiosity and enthusiasm as it holds the potential to revolutionize instructional strategies and transform the learning experience. Generative AI is the utilization of machine learning algorithms to generate fresh content by drawing inspiration from pre-existing material. At the same time, many online education portals are making use of the open-ended resource (OER) educational resources or content curation education resources. The ability to perform search among these resources may be vast and the quality of the content extraction may be at stake, irrelevant to the students etc. The research highlights the benefits of incorporating Generative AI into content curation and generation, with personalized learning being a key advantage. Studies have shown that Generative AI can effectively personalize learning experiences by adapting content delivery to individual students' strengths, weaknesses, and learning pace. This personalized approach enhances learner engagement and knowledge retention. Additionally, Generative AI-generated content is dynamic and interactive, making education more engaging, immersive, and effective. In summary, Generative AI presents a promising outlook for the field of education by tailoring instructional approaches to address individual students' distinct needs and goals. By leveraging Generative AI, educators can create interactive and adaptive assessments, personalize learning experiences, and design curriculum that caters to individual students' needs. However, the article also highlights the ethical concerns and challenges that come with incorporating Generative AI in education. These concerns include ensuring data privacy, addressing algorithmic bias, and defining the role of educators in guiding AI-driven learning experiences.

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Lee, Sun-Hyung, and Byung-Keun Kim. "Analysis of Factors and Pathways Influencing Continued Usage Intention of AI Technology: Focusing on the Mediating Effects of AI Usage and AI Trust." JOURNAL OF THE KOREA CONTENTS ASSOCIATION 25, no. 4 (April 30, 2025): 282–94. https://doi.org/10.5392/jkca.2025.25.04.282.

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Lee, Jaeho, Seunggyu Lee, and Seunghoon Lee. "The Influence of AI Convergence Education on Students' Perception of AI." Journal of The Korean Association of Information Education 25, no. 3 (June 30, 2021): 483–90. http://dx.doi.org/10.14352/jkaie.2021.25.3.483.

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Baek, Daeun, WanSang Son, Ji Hoon Song, and MYUNGHYUN YOO. "Meta-analysis of learning effectiveness using generative AI." Korean Association for Educational Information and Media 30, no. 4 (August 30, 2024): 1261–85. http://dx.doi.org/10.15833/kafeiam.30.4.1261.

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Kang, Mun-Suk. "A study on prospective teachers’ perceptions of AI education and AI teachers." Regional Entrepreneurship Education Research Center 6, no. 4 (December 31, 2024): 29–47. https://doi.org/10.23108/decrc.2024.6.4.29.

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Purpose: This study investigate prospective teachers' perceptions of AI education and AI teachers. Method: For this purpose, after conducting a discussion class, data obtained through questionnaires, reflection journals, and interviews were collected and a constant comparison method among qualitative analysis methods was performed. Results: First, about AI education and AI teachers, prospective teachers responded, ‘The positive and good things are helpful: supporter,’ ‘I am concerned about the negative and skeptical things: Concerns,’ and ‘I am looking forward to the fun and interesting things: Expectator.’ Awareness of AI education and AI teachers will either be helpful or act as a complex factor in education, such as concerns and expectations. However, AI teachers were perceived as merely assisting learning, and were negative and skeptical about emotional support, social skills, and creativity development. Conclusion: These results reflect the perceptions of prospective teachers, suggesting that systematic education using AI can be implemented in teacher training colleges, and that development and research of AI curriculum or AI-based educational programs are necessary.

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Jang, Dodam, and Myeoun-Heum Yeoun. "A Proposal of AI Service Scenarios for the Development of AI Service Agent in the Near Future Public Space ; Based on AI Service Agent Types." Journal of Industrial Design Studies 52 (June 30, 2020): 105–16. http://dx.doi.org/10.37254/ids.2020.06.52.09.105.

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Tejesh, Reddy Singasani. "Enhancing Customer Experience through PEGA s AI Powered Decisioning." Journal of Scientific and Engineering Research 9, no. 12 (December 31, 2022): 191–95. https://doi.org/10.5281/zenodo.13753089.

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In this paper, we will examine the revolutionary effect of PEGA AI-powered decisioning on improving CX. Following AI breakthroughs, it has become clear that companies have their sights set on automatic customer service to deliver a more personable and efficient experience for customers. PEGA's AI decisioning engine uses data-driven real-time insights to predict desired outcomes, better optimize the overall experience and increase customer satisfaction. This paper discusses the AI mechanisms of PEGA, demonstrates how they work using different use cases and emphasizes their power in real-time scenarios.

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Pavan, Narayan L., Reddy R. Rohan, Gowda K. S. Punith, and P. V. Sagar. "AI-Based Heart Disease Prediction System." Recent Innovations in Wireless Network Security 7, no. 1 (February 3, 2025): 48–55. https://doi.org/10.5281/zenodo.14793767.

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<em>Heart disease is a leading cause of death globally, requiring early and accurate detection for effective management. This study proposes an AI-based heart disease prediction system using machine learning algorithms to analyse patient data. Key features influencing heart disease are identified to enhance model accuracy and interpretability. The system, trained on publicly available datasets, achieves high prediction accuracy, demonstrating its potential as a reliable, cost-effective diagnostic tool. Integrating AI into clinical workflows can improve personalized care and patient outcomes. Future work focuses on real-time data integration and advanced AI techniques for further improvement.</em>

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Dr., Amartya Saha. "Understanding the Scope and Problems of Artificial Intelligence and Assisted Learning in Higher Education." Social Science Journal for Advanced Research 4, no. 5 (September 3, 2024): 1–9. https://doi.org/10.5281/zenodo.13637234.

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Learning is a dynamic experience. We need to create learning experiences that assist all kinds of learners. Human beings have different patterns of learning. Assisted learning helps in decentralizing the learning experience in a more individualistic approach. Artificial Intelligence has helped in automating many tasks of administrative nature, evaluation and has even helped personalize learning, countering the problems of teacher exhaustion. Not just efficient grading use of AI through educational chatbots and applications can help in helping students get a 24/7 mentoring option which in a real-world scenario might be difficult for human teachers to cater to. With technology has come ease of global accessibility and insights that are backed by data. But it also brings along with it an enhanced scenario of digital divide, technological hurdle and inequality. The paper through case studies tries to analyze the scopes and problems of using Artificial Intelligence in Assisted Learning in Higher Education.

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