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Journal articles on the topic 'Artificial intelligence'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 November 2024

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1

Ahmad, Zubair. "Artificial Intelligence or Augmented Intelligence?" International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 1782–88. http://dx.doi.org/10.21275/sr231212220052.

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2

Dai, Peng, Mausam, and Daniel Weld. "Artificial Intelligence for Artificial Artificial Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1153–60. http://dx.doi.org/10.1609/aaai.v25i1.8096.

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Crowdsourcing platforms such as Amazon Mechanical Turk have become popular for a wide variety of human intelligence tasks; however, quality control continues to be a significant challenge. Recently, we propose TurKontrol, a theoretical model based on POMDPs to optimize iterative, crowd-sourced workflows. However, they neither describe how to learn the model parameters, nor show its effectiveness in a real crowd-sourced setting. Learning is challenging due to the scale of the model and noisy data: there are hundreds of thousands of workers with high-variance abilities. This paper presents an end-to-end system that first learns TurKontrol's POMDP parameters from real Mechanical Turk data, and then applies the model to dynamically optimize live tasks. We validate the model and use it to control a successive-improvement process on Mechanical Turk. By modeling worker accuracy and voting patterns, our system produces significantly superior artifacts compared to those generated through nonadaptive workflows using the same amount of money.

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3

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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4

VASYLKIVSKYI, Mikola, Ganna VARGATYUK, and Olga BOLDYREVA. "INTELLIGENT RADIO INTERFACE WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE." Herald of Khmelnytskyi National University. Technical sciences 217, no. 1 (February 23, 2023): 26–32. http://dx.doi.org/10.31891/2307-5732-2023-317-1-26-32.

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The peculiarities of the implementation of the 6G intelligent radio interface infrastructure, which will use an individual configuration for each individual subscriber application and flexible services with lower overhead costs, have been studied. A personalized infrastructure consisting of an AI-enabled intelligent physical layer, an intelligent MAC controller, and an intelligent protocol is considered, followed by a potentially novel AI-based end-to-end (E2E) device. The intelligent controller is investigated, in particular the intelligent functions at the MAC level, which may become key components of the intelligent controller in the future. The joint optimization of these components, which will provide better system performance, is considered. It was determined that instead of using a complex mathematical method of optimization, it is possible to use machine learning, which has less complexity and can adapt to network conditions. A 6G radio interface design based on a combination of model-driven and data-driven artificial intelligence is investigated and is expected to provide customized radio interface optimization from pre-configuration to self-learning. The specifics of configuring the network scheme and transmission parameters at the level of subscriber equipment and services using a personalized radio interface to maximize the individual user experience without compromising the throughput of the system as a whole are determined. Artificial intelligence is considered, which will be a built-in function of the radio interface that creates an intelligent physical layer and is responsible for MAC access control, network management optimization (such as load balancing and power saving), replacing some non-linear or non-convex algorithms in receiver modules or compensation of shortcomings in non-linear models. Built-in intelligence has been studied, which will make the 6G physical layer more advanced and efficient, facilitate the optimization of structural elements of the physical layer and procedural design, including the possible change of the receiver architecture, will help implement new detection and positioning capabilities, which, in turn, will significantly affect the design of radio interface components. The requirements for the 6G network are defined, which provide for the creation of a single network with scanning and communication functions, which must be integrated into a single structure at the stage of radio interface design. The specifics of carefully designing a communication and scanning network that will offer full scanning capabilities and more fully meet all key performance indicators in the communications industry are explored.

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5

Kambur, Emine. "Emotional Intelligence or Artificial Intelligence?: Emotional Artificial Intelligence." Florya Chronicles of Political Economy 7, no. 2 (2015): 147–68. http://dx.doi.org/10.17932/iau.fcpe.2015.010/fcpe_v07i2004.

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6

Floridi, Luciano. "Artificial artificial intelligence." Philosophers' Magazine, no. 64 (2014): 22–23. http://dx.doi.org/10.5840/tpm2014647.

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7

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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8

Senthil Kumar, R. "Artificial Intelligence in Education." International Journal of Science and Research (IJSR) 12, no. 8 (August 5, 2023): 424–25. http://dx.doi.org/10.21275/sr23802113939.

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9

Das, Subhashis. "Artificial Intelligence in Diagnosis." JOURNAL OF CLINICAL AND BIOMEDICAL SCIENCES 13, no. 4 (December 28, 2023): 100–101. http://dx.doi.org/10.58739/jcbs/v13i4.editorial.

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10

Youm, Jung-seop. "Commercialization of Artificial Intelligence and Artificial Intelligence." Journal of Korean Association for Buddhist Studies 85 (March 31, 2018): 65–94. http://dx.doi.org/10.22255/jkabs.85.3.

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11

Parikh, Purvish M. "Artificial Intelligence: ChatGPT to Artificial Intelligence Washing." Journal of Mahatma Gandhi University of Medical Sciences and Technology 8, no. 1 (April 20, 2024): 1–4. http://dx.doi.org/10.5005/jp-journals-10057-0231.

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12

Bernatzeder, Petra. "Artificial Intelligence ... »mental intelligence«." Digitale Welt 4, no. 1 (December 4, 2019): 15. http://dx.doi.org/10.1007/s42354-019-0224-5.

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13

DELIO, ILIA. "IS ARTIFICIAL INTELLIGENCE “ARTIFICIAL”?" Researcher. European Journal of Humanities & Social Science 3, no. 2 (July 10, 2020): 13–32. http://dx.doi.org/10.32777/r.2020.3.2.1.

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14

Valor, Josep. "Un-Artificial Intelligence." IESE Insight, no. 28 (March 15, 2016): 72. http://dx.doi.org/10.15581/002.opi-3584.

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15

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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16

Singh, Sarbjeet, and Sukhvinder Singh. "Artificial Intelligence." International Journal of Computer Applications 6, no. 6 (September 10, 2010): 21–23. http://dx.doi.org/10.5120/1081-1413.

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17

Meij, Jan Martijn. "Artificial Intelligence." Nature and Culture 15, no. 3 (December 1, 2020): 318–28. http://dx.doi.org/10.3167/nc.2020.150305.

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18

Hou, Jean, and Cynthia C. Nast. "Artificial Intelligence." Clinical Journal of the American Society of Nephrology 15, no. 10 (September 16, 2020): 1389–91. http://dx.doi.org/10.2215/cjn.13450820.

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19

Marinchak, Christina McDowell, Edward Forrest, and Bogdan Hoanca. "Artificial Intelligence." International Journal of E-Entrepreneurship and Innovation 8, no. 2 (July 2018): 14–24. http://dx.doi.org/10.4018/ijeei.2018070102.

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As marketers and consumers simultaneously adopt artificial intelligence (AI) services and applications, the dynamic of the process of exchange between the buyer and seller in the marketplace is being fundamentally altered. This article reviews the emerging patterns of adoption and rates of diffusion of AI applications by both marketers and consumers. On the marketers' side the authors will address the single most defining phenomenon that is affecting the marketer's role and function in the marketing process: the exponential increase in the number, variety and capability of marketing applications, platforms and services that perform, control, influence and/or integrate virtually every marketing task and decision.

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20

Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 3 (August 19, 2022): 47. http://dx.doi.org/10.1093/combul/bwac093.

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21

Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 2 (May 12, 2022): 65. http://dx.doi.org/10.1093/itnow/bwac065.

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22

Bali, Jatinder, and Shivam Nayak. "Artificial Intelligence." Journal of Clinical Ophthalmology and Research 8, no. 1 (2020): 1. http://dx.doi.org/10.4103/jcor.jcor_18_20.

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23

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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24

Carroll, Whende M. "Artificial intelligence." Nursing Management 52, no. 11 (November 2021): 29–32. http://dx.doi.org/10.1097/01.numa.0000795584.59335.f7.

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25

Matveev, V. I. "ARTIFICIAL INTELLIGENCE." Kontrol'. Diagnostika, no. 275 (May 2021): 56–63. http://dx.doi.org/10.14489/td.2021.05.pp.056-063.

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Artificial intelligence is becoming the main direction of the development of science and technology, making progress at a new level. Automation of production, the implementation of operations in hazardous and harmful areas, the implementation of routine actions in the environment are inevitable in the modern world. A person creates an analogue for himself, realizing the possible consequences and limiting them to legislative acts. The article provides positive examples of the implementation of the artificial intelligence project and legislative measures that limit its impact on the social environment.

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26

Commissariat, Tushna. "Artificial intelligence." Physics World 34, no. 5 (July 1, 2021): 17. http://dx.doi.org/10.1088/2058-7058/34/05/24.

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27

Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 1 (February 17, 2022): 41. http://dx.doi.org/10.1093/itnow/bwac021.

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28

Stephens, C. D. "Artificial intelligence." British Dental Journal 180, no. 8 (April 1996): 284. http://dx.doi.org/10.1038/sj.bdj.4809057.

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29

Gitomer, Jerry. "Artificial Intelligence." Journal of Information Systems Management 3, no. 1 (January 1986): 58–60. http://dx.doi.org/10.1080/07399018608965231.

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30

Gil, Yolanda. "Artificial intelligence." AI Matters 1, no. 1 (September 17, 2014): 4–5. http://dx.doi.org/10.1145/2639475.2639477.

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31

Hillman, Donald J. "Artificial Intelligence." Human Factors: The Journal of the Human Factors and Ergonomics Society 27, no. 1 (February 1985): 21–31. http://dx.doi.org/10.1177/001872088502700103.

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32

Wetzel, Randall C., Melissa Aczon, and David R. Ledbetter. "Artificial Intelligence." Pediatric Critical Care Medicine 19, no. 10 (October 2018): 1004–5. http://dx.doi.org/10.1097/pcc.0000000000001700.

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33

Kamaleswaran, Rishikesan, Oguz Akbilgic, Madhura A. Hallman, Alina N. West, Robert L. Davis, and Samir H. Shah. "Artificial Intelligence." Pediatric Critical Care Medicine 20, no. 4 (April 2019): 399. http://dx.doi.org/10.1097/pcc.0000000000001874.

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34

Sadiku, M. N. O. "Artificial intelligence." IEEE Potentials 8, no. 2 (May 1989): 35–39. http://dx.doi.org/10.1109/45.31596.

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35

Butterfield, William H. "Artificial Intelligence." Computers in Human Services 3, no. 1-2 (August 10, 1988): 23–35. http://dx.doi.org/10.1300/j407v03n01_03.

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36

Fluke, John D., and Gregory N. O'Beirne. "Artificial Intelligence." Computers in Human Services 4, no. 1-2 (August 1989): 101–9. http://dx.doi.org/10.1300/j407v04n01_14.

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37

Scotti, Veronica. "Artificial intelligence." IEEE Instrumentation & Measurement Magazine 23, no. 3 (May 2020): 27–31. http://dx.doi.org/10.1109/mim.2020.9082795.

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38

CACM Staff. "Artificial intelligence." Communications of the ACM 60, no. 2 (January 23, 2017): 10–11. http://dx.doi.org/10.1145/3034429.

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39

Weinstein, James N. "Artificial Intelligence." SPINE 44, no. 1 (January 2019): 1–4. http://dx.doi.org/10.1097/brs.0000000000002913.

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40

Hilton, Alexander D. "Artificial intelligence." AI Matters 5, no. 3 (December 6, 2019): 70–76. http://dx.doi.org/10.1145/3362077.3362088.

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41

Muspratt, Murray A. "Artificial Intelligence." Journal of Professional Issues in Engineering 112, no. 3 (July 1986): 158–69. http://dx.doi.org/10.1061/(asce)1052-3928(1986)112:3(158).

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42

Clarke, John R., David P. Cebula, and Bonnie L. Vebber. "ARTIFICIAL INTELLIGENCE." Journal of Trauma: Injury, Infection, and Critical Care 27, no. 7 (July 1987): 818. http://dx.doi.org/10.1097/00005373-198707000-00035.

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43

McGrow, Kathleen. "Artificial intelligence." Nursing 49, no. 9 (September 2019): 46–49. http://dx.doi.org/10.1097/01.nurse.0000577716.57052.8d.

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44

LAWRENCE, DAVID R., CÉSAR PALACIOS-GONZÁLEZ, and JOHN HARRIS. "Artificial Intelligence." Cambridge Quarterly of Healthcare Ethics 25, no. 2 (March 9, 2016): 250–61. http://dx.doi.org/10.1017/s0963180115000559.

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Abstract:It seems natural to think that the same prudential and ethical reasons for mutual respect and tolerance that one has vis-à-vis other human persons would hold toward newly encountered paradigmatic but nonhuman biological persons. One also tends to think that they would have similar reasons for treating we humans as creatures that count morally in our own right. This line of thought transcends biological boundaries—namely, with regard to artificially (super)intelligent persons—but is this a safe assumption? The issue concerns ultimate moral significance: the significance possessed by human persons, persons from other planets, and hypothetical nonorganic persons in the form of artificial intelligence (AI). This article investigates why our possible relations to AI persons could be more complicated than they first might appear, given that they might possess a radically different nature to us, to the point that civilized or peaceful coexistence in a determinate geographical space could be impossible to achieve.

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45

Sheikh, Ahmad Y., and James I. Fann. "Artificial Intelligence." Thoracic Surgery Clinics 29, no. 3 (August 2019): 339–50. http://dx.doi.org/10.1016/j.thorsurg.2019.03.011.

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46

Leff, Laurence L. "Artificial intelligence." Artificial Intelligence in Engineering 2, no. 1 (January 1987): 45–47. http://dx.doi.org/10.1016/0954-1810(87)90070-7.

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47

Khemani, Deepak. "Artificial Intelligence." Resonance 25, no. 1 (January 2020): 33–41. http://dx.doi.org/10.1007/s12045-019-0920-3.

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48

Khemani, Deepak. "Artificial Intelligence." Resonance 25, no. 1 (January 2020): 43–58. http://dx.doi.org/10.1007/s12045-019-0921-2.

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49

Lacombe, Michael A. "Artificial intelligence." American Journal of Medicine 83, no. 1 (July 1987): 127–28. http://dx.doi.org/10.1016/0002-9343(87)90506-7.

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50

Jahrreiss, Victoria, Julian Veser, Christian Seitz, and Mehmet Özsoy. "Artificial intelligence." Current Opinion in Urology 30, no. 2 (March 2020): 196–99. http://dx.doi.org/10.1097/mou.0000000000000707.

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