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Journal articles on the topic 'Cognitive Computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Wang, Yingxu, Victor Raskin, Julia Rayz, George Baciu, Aladdin Ayesh, Fumio Mizoguchi, Shusaku Tsumoto, Dilip Patel, and Newton Howard. "Cognitive Computing." International Journal of Software Science and Computational Intelligence 10, no. 1 (January 2018): 1–14. http://dx.doi.org/10.4018/ijssci.2018010101.

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Cognitive Computing (CC) is a contemporary field of studies on intelligent computing methodologies and brain-inspired mechanisms of cognitive systems, cognitive machine learning and cognitive robotics. The IEEE conference ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This article reports the plenary panel (Part II) in IEEE ICCI*CC'17 at Oxford University. The summary is contributed by distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of cognitive computing.

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Modha, Dharmendra S., Rajagopal Ananthanarayanan, Steven K. Esser, Anthony Ndirango, Anthony J. Sherbondy, and Raghavendra Singh. "Cognitive computing." Communications of the ACM 54, no. 8 (August 2011): 62–71. http://dx.doi.org/10.1145/1978542.1978559.

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3

Pagel, Peter, Edy Portmann, and Karin Vey. "Cognitive Computing." Informatik-Spektrum 41, no. 1 (February 2018): 1–4. http://dx.doi.org/10.1007/s00287-018-1091-4.

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D’Onofrio, Sara, Edy Portmann, Michel Franzelli, and Christoph Bürki. "Cognitive Computing." Informatik-Spektrum 41, no. 2 (March 7, 2018): 113–22. http://dx.doi.org/10.1007/s00287-018-1095-0.

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5

Sridharan, Mohan, Gerald Tesauro, and James Hendler. "Cognitive Computing." IEEE Intelligent Systems 32, no. 4 (2017): 3–4. http://dx.doi.org/10.1109/mis.2017.3121554.

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6

Demirkan, Haluk, Seth Earley, and Robert R. Harmon. "Cognitive Computing." IT Professional 19, no. 4 (2017): 16–20. http://dx.doi.org/10.1109/mitp.2017.3051332.

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7

Wang, Yingxu, George Baciu, Yiyu Yao, Witold Kinsner, Keith Chan, Bo Zhang, Stuart Hameroff, et al. "Perspectives on Cognitive Informatics and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (January 2010): 1–29. http://dx.doi.org/10.4018/jcini.2010010101.

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Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

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Farrell, Robert G., Jonathan Lenchner, Jeffrey O. Kephjart, Alan M. Webb, MIchael J. Muller, Thomas D. Erikson, David O. Melville, et al. "Symbiotic Cognitive Computing." AI Magazine 37, no. 3 (October 7, 2016): 81–93. http://dx.doi.org/10.1609/aimag.v37i3.2628.

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IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing. We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.

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Wang, Yingxu. "On Cognitive Computing." International Journal of Software Science and Computational Intelligence 1, no. 3 (July 2009): 1–15. http://dx.doi.org/10.4018/jssci.2009070101.

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Pagel, Peter, Edy Portmann, and Karin Vey. "Cognitive Computing – Teil 2." Informatik-Spektrum 41, no. 2 (April 2018): 81–84. http://dx.doi.org/10.1007/s00287-018-1101-6.

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Dubhashi, Devdatt. "Singularities and Cognitive Computing." Proceedings 1, no. 3 (June 9, 2017): 189. http://dx.doi.org/10.3390/is4si-2017-04027.

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12

Wang, Yu. "The Application of Computer-Based Multimedia Technology in Cognitive Computing." Computational Intelligence and Neuroscience 2022 (February 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/3354576.

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With the continuous development of science and technology, people’s research on computer-related technologies is gradually deepening. The proposal of artificial intelligence makes the research of intelligent AI in urgent need for seeking breakthroughs. Among them, cognitive computing methods are important for computers and human brain thinking. Model learning is even more meaningful. This article aims to study the application of computer-based multimedia technology in cognitive computing methods. For this, this article proposes a method of distributed multimedia technology and a method of two-way cognition for cognitive computing through this technology to deepen the rapid improvement of cognition. In addition, this article finally designed relating experiments to study it. The experimental results show that the cognitive accuracy of the improved cognitive computing method has increased by 32.9%, and the cognitive ability has also been greatly improved compared to the past.

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13

Veres, Csaba. "Strong Cognitive Symbiosis: Cognitive Computing for Humans." Big Data and Cognitive Computing 1, no. 1 (November 10, 2017): 6. http://dx.doi.org/10.3390/bdcc1010006.

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14

Gain, Ulla. "The cognitive function and the framework of the functional hierarchy." Applied Computing and Informatics 16, no. 1/2 (March 13, 2018): 81–116. http://dx.doi.org/10.1016/j.aci.2018.03.003.

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Cognitive computing is part of AI and cognitive applications consists of cognitive services, which are building blocks of the cognitive systems. These applications mimic the human brain functions, for example, recognize the speaker, sense the tone of the text. On this paper, we present the similarities of these with human cognitive functions. We establish a framework which gathers cognitive functions into nine intentional processes from the substructures of the human brain. The framework, underpins human cognitive functions, and categorizes cognitive computing functions into the functional hierarchy, through which we present the functional similarities between cognitive service and human cognitive functions to illustrate what kind of functions are cognitive in the computing. The results from the comparison of the functional hierarchy of cognitive functions are consistent with cognitive computing literature. Thus, the functional hierarchy allows us to find the type of cognition and reach the comparability between the applications.

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15

WANG, YINGXU. "ON FORMAL AND COGNITIVE SEMANTICS FOR SEMANTIC COMPUTING." International Journal of Semantic Computing 04, no. 02 (June 2010): 203–37. http://dx.doi.org/10.1142/s1793351x10000833.

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Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.

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16

Wang, Yingxu, Newton Howard, Janusz Kacprzyk, Ophir Frieder, Phillip Sheu, Rodolfo A. Fiorini, Marina L. Gavrilova, Shushma Patel, Jun Peng, and Bernard Widrow. "Cognitive Informatics." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 1 (January 2018): 1–13. http://dx.doi.org/10.4018/ijcini.2018010101.

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Cognitive Informatics (CI) is a contemporary field of basic studies on the brain, computational intelligence theories and underpinning denotational mathematics. Its applications include cognitive systems, cognitive computing, cognitive machine learning and cognitive robotics. IEEE ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This paper reports the plenary panel (Part I) at IEEE ICCI*CC'17 held at Oxford University. The summary is contributed by invited keynote speakers and distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and cognitive computing.

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17

Manjula, T., and T. Sudha. "Cognitive Computing For Sustainable Agriculture." Asian Journal of Computer Science and Technology 8, no. 3 (November 15, 2019): 32–34. http://dx.doi.org/10.51983/ajcst-2019.8.3.2738.

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Cognitive computing in agriculture is going to be a big revolution like the green revolution. Agriculture is a big step that accompanied the humanity to evolve from the ancient times to the modern days and has fulfilled the basic need for food supply. Today still remains it’s at most importance. Cognitive computing uses cognitive technologies in agriculture that help to understand, learn from experiences and environment, reason, interact and thus increase the efficiency. Civilization has led to more urbanization. There are more people than available food. There is a great necessity to increase the per meter yield, So many techniques have been for seen in agriculture in terms of usage of pesticides and fertilizers, use of hybridization and green revolution to increase the production in agriculture. Now the use of modern technologies such as artificial intelligence and cognitive computation is going to bring a new big revolution for sustainable agriculture. The present paper focuses on the problems faced by the modern society in agriculture and how the cognitive computation provides an ultimate solution to the problems. We also discuss some illustrations for the usage of cognitive technologies and machine learning in the field of agriculture.

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18

Dodig-Crnkovic, Gordana, and Robert Lowe. "Morphological Computing and Cognitive Agency." Proceedings 1, no. 3 (September 7, 2017): 185. http://dx.doi.org/10.3390/proceedings1030185.

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19

Kumar, M., W. P. Horn, J. Kepner, J. E. Moreira, and P. Pattnaik. "IBM POWER9 and cognitive computing." IBM Journal of Research and Development 62, no. 4/5 (July 1, 2018): 10:1–10:12. http://dx.doi.org/10.1147/jrd.2018.2846958.

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20

Ogiela, Marek R., Ilsun You, Fang-Yie Leu, and Makoto Takizawa. "Modern cognitive and ubiquitous computing." Neurocomputing 122 (December 2013): 1–2. http://dx.doi.org/10.1016/j.neucom.2013.05.009.

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21

D’Onofrio, Sara, and Edy Portmann. "Cognitive Computing in Smart Cities." Informatik-Spektrum 40, no. 1 (November 9, 2016): 46–57. http://dx.doi.org/10.1007/s00287-016-1006-1.

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22

Banavar, Guruduth, and Martin Cooper. "Turing Lecture 2017 Cognitive Computing." ITNOW 58, no. 4 (November 17, 2016): 62–63. http://dx.doi.org/10.1093/itnow/bww117.

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23

Schizas, Christos N. "Cognitive computing for supporting eHealth." Health and Technology 7, no. 1 (December 28, 2016): 11–12. http://dx.doi.org/10.1007/s12553-016-0162-2.

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24

Sellmann, Meinolf. "Meta-Algorithms in Cognitive Computing." IEEE Intelligent Systems 32, no. 4 (2017): 35–39. http://dx.doi.org/10.1109/mis.2017.3121549.

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25

Earley, Seth. "Cognitive Computing, Analytics, and Personalization." IT Professional 17, no. 4 (July 2015): 12–18. http://dx.doi.org/10.1109/mitp.2015.55.

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26

Lu, Huimin, and Yujie Li. "Cognitive Computing for Intelligence Systems." Mobile Networks and Applications 25, no. 4 (January 17, 2020): 1434–35. http://dx.doi.org/10.1007/s11036-019-01428-y.

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27

Wang, Yingxu, Witold Pedrycz, George Baciu, Ping Chen, Guoyin Wang, and Yiyu Yao. "Perspectives on Cognitive Computing and Applications." International Journal of Software Science and Computational Intelligence 2, no. 4 (October 2010): 32–44. http://dx.doi.org/10.4018/jssci.2010100103.

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Cognitive Computing (CC) is an emerging paradigm of intelligent computing theories and technologies based on cognitive informatics, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. The development of Cognitive Computers (cC) is centric in cognitive computing methodologies. A cC is an intelligent computer for knowledge processing as that of a conventional von Neumann computer for data processing. This paper summarizes the presentations of a set of 6 position papers presented in the ICCI’10 Plenary Panel on Cognitive Computing and Applications contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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28

Wang, Yingxu, Robert C. Berwick, Simon Haykin, Witold Pedrycz, Witold Kinsner, George Baciu, Du Zhang, and C. Bhavsar. "Cognitive Informatics and Cognitive Computing in Year 10 and Beyond." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (October 2011): 1–21. http://dx.doi.org/10.4018/jcini.2011100101.

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Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. The latest advances in CI leads to the establishment of cognitive computing theories and methodologies, as well as the development of Cognitive Computers (CogC) that perceive, infer, and learn. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC’11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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29

Wang, Yingxu, Edmund T. Rolls, Newton Howard, Victor Raskin, Witold Kinsner, Fionn Murtagh, Virendrakumar C. Bhavsar, Shushma Patel, Dilip Patel, and Duane F. Shell. "Cognitive Informatics and Computational Intelligence." International Journal of Software Science and Computational Intelligence 7, no. 2 (April 2015): 50–69. http://dx.doi.org/10.4018/ijssci.2015040103.

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Cognitive Informatics (CI) is a contemporary multidisciplinary field spanning across computer science, information science, cognitive science, brain science, intelligence science, knowledge science, cognitive linguistics, and cognitive philosophy. Cognitive Computing (CC) is a novel paradigm of intelligent computing methodologies and systems based on CI that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This paper reports a set of position statements presented in the plenary panel of IEEE ICCI*CC'14 on Cognitive Informatics and Cognitive Computing. The summary is contributed by invited panelists who are part of the world's renowned researchers and scholars in the transdisciplinary field of cognitive informatics and cognitive computing.

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30

Cheng, Yihang, Xi Zhang, Xiaojiong Wang, Hongke Zhao, Yao Yu, Xianhai Wang, and Patricia Ordoñez de Pablos. "Rethinking the Development of Technology-Enhanced Learning and the Role of Cognitive Computing." International Journal on Semantic Web and Information Systems 17, no. 1 (January 2021): 67–96. http://dx.doi.org/10.4018/ijswis.2021010104.

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Technology-enhanced learning (TEL) is important in social web. Recently, cognitive computing became significant to analyze sentiment and improve effectiveness in TEL field. So analyzing the development of cognitive computing, what and how its abilities improve TEL are necessary. For solving these issues, this study used systematic review approach based on technology view and enhancement view of TEL. Specifically, this study used topic search results in computer science field of “cognitive computing” and “anticipatory computing” in Web of Science database to do map analysis. Besides development footprints, the manuscript describes three development stages and key technologies of cognitive computing through burst study and step-by-step clustering. Finally, this study proposed influencing framework of cognitive computing on TEL and some research trends. This work provides an advanced background of TEL and a systemic review of cognitive computing, contributing to theory development and application of cognitive computing in TEL.

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31

Wang, Yingxu, James A. Anderson, George Baciu, Gerhard Budin, D. Frank Hsu, Mitsuru Ishizuka, Witold Kinsner, et al. "Perspectives on eBrain and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 4 (October 2012): 1–21. http://dx.doi.org/10.4018/jcini.2012100101.

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Cognitive Informatics (CI) is a discipline spanning across computer science, information science, cognitive science, brain science, intelligence science, knowledge science, and cognitive linguistics. CI aims to investigate the internal information processing mechanisms and processes of the brain, the underlying abstract intelligence theories and denotational mathematics, and their engineering applications in cognitive computing and computational intelligence. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC’12 on eBrain and Cognitive Computers contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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32

Chen, Jun, Chao Lu, Haifeng Huang, Dongwei Zhu, Qing Yang, Junwei Liu, Yan Huang, Aijun Deng, and Xiaoxu Han. "Cognitive Computing-Based CDSS in Medical Practice." Health Data Science 2021 (July 22, 2021): 1–13. http://dx.doi.org/10.34133/2021/9819851.

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Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade. Highlights. (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction. Conclusion. Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.

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33

Haun, Matthias. "A Plea for the Cognitive Solution Attempt: Cognitive Robotics, Cognitive Factory and Cognitive Solutions. Cognitive Robotics as a Model for the Development of Cognitive Solutions." Applied Mechanics and Materials 613 (August 2014): 3–10. http://dx.doi.org/10.4028/www.scientific.net/amm.613.3.

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A cognitive approach conceptualizes the goods producing enterprise and its production factors (such as divisions, units, sections or resources like machines or robots) as well as the enterprise’s products (such as valves) as cognitive models. These models are then put into shape with the help of cognitive computing techniques.

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34

DONG, ANDY. "Special Issue: Design computing and cognition." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, no. 4 (November 2005): 227–28. http://dx.doi.org/10.1017/s0890060405050158.

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The field of research in design computing and cognition focuses on computational theories and systems that enact design. Design computing and cognition produces a unifying framework to model and explain design beyond the description of “design computing and cognition,” as in “design computing” and “design cognition” as two cognate disciplines. Research in design computing and cognition recognizes not only the essential relationship between human cognitive processes as models of computation but also how models of computation inspire conceptual realizations of human cognition in design. The articles in this Special Issue address the concomitant key areas of research in design computing and cognition: computational models of design, computational representations in design, computational design systems, and design cognition. The computationally inspired perspectives, metaphors, models, and theories that the papers deliver create a base for computing and cognition to (re)shape design practice and its role in design science and inquiry.

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35

Zhanatauov, S. U. "COGNITIVE COMPUTING: MODELS, CALCULATIONS, APPLICATIONS, RESULTS." Theoretical & Applied Science 97, no. 05 (May 30, 2021): 594–610. http://dx.doi.org/10.15863/tas.2021.05.97.91.

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36

POZNANSKI, ROMAN R. "MODEL-BASED NEUROIMAGING FOR COGNITIVE COMPUTING." Journal of Integrative Neuroscience 08, no. 03 (September 2009): 345–69. http://dx.doi.org/10.1142/s021963520900223x.

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37

Ogiela, Marek R., Ilsun You, Fatos Xhafa, and Hoon Ko. "Towards context, cognitive, and secure computing." Computers & Mathematics with Applications 63, no. 2 (January 2012): 337–38. http://dx.doi.org/10.1016/j.camwa.2011.12.056.

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38

Yao, Yiyu. "Three-Way Decisions and Cognitive Computing." Cognitive Computation 8, no. 4 (March 15, 2016): 543–54. http://dx.doi.org/10.1007/s12559-016-9397-5.

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39

Sager, Sebastian, Katja Mombaur, and Joachim Funke. "Scientific computing for the cognitive sciences." Journal of Computational Science 4, no. 4 (July 2013): 242–44. http://dx.doi.org/10.1016/j.jocs.2012.12.001.

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40

Wang, Guoyin. "DGCC: data-driven granular cognitive computing." Granular Computing 2, no. 4 (July 27, 2017): 343–55. http://dx.doi.org/10.1007/s41066-017-0048-3.

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41

Zhou, Zhenhua. "Emotional thinking as the foundation of consciousness in artificial intelligence." Cultures of Science 4, no. 3 (September 2021): 112–23. http://dx.doi.org/10.1177/20966083211052651.

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Current theories of artificial intelligence (AI) generally exclude human emotions. The idea at the core of such theories could be described as ‘cognition is computing’; that is, that human psychological and symbolic representations and the operations involved in structuring such representations in human thinking and intelligence can be converted by AI into a series of cognitive symbolic representations and calculations in a manner that simulates human intelligence. However, after decades of development, the cognitive computing doctrine has encountered many difficulties, both in theory and in practice; in particular, it is far from approaching real human intelligence. Real human intelligence runs through the whole process of the emotions. The core and motivation of rational thinking are derived from the emotions. Intelligence without emotion neither exists nor is meaningful. For example, the idea of ‘hot thinking’ proposed by Paul Thagard, a philosopher of cognitive science, discusses the mechanism of the emotions in human cognition and the thinking process. Through an analysis from the perspectives of cognitive neurology, cognitive psychology and social anthropology, this article notes that there may be a type of thinking that could be called ‘emotional thinking’. This type of thinking includes complex emotional factors during the cognitive processes. The term is used to refer to the capacity to process information and use emotions to integrate information in order to arrive at the right decisions and reactions. This type of thinking can be divided into two types according to the role of cognition: positive and negative emotional thinking. That division reflects opposite forces in the cognitive process. In the future, ‘emotional computing’ will cause an important acceleration in the development of AI consciousness. The foundation of AI consciousness is emotional computing based on the simulation of emotional thinking.

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Wang, Yingxu, Bernard Carlos Widrow, Bo Zhang, Witold Kinsner, Kenji Sugawara, Fuchun Sun, Jianhua Lu, Thomas Weise, and Du Zhang. "Perspectives on the Field of Cognitive Informatics and its Future Development." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 1 (January 2011): 1–17. http://dx.doi.org/10.4018/jcini.2011010101.

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The contemporary wonder of sciences and engineering has recently refocused on the beginning point of: how the brain processes internal and external information autonomously and cognitively rather than imperatively like conventional computers. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of eight position statements presented in the plenary panel of IEEE ICCI’10 on Cognitive Informatics and Its Future Development contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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43

Wang, Yingxu. "Paradigms of Denotational Mathematics for Cognitive Informatics and Cognitive Computing." Fundamenta Informaticae 90, no. 3 (2009): 283–303. http://dx.doi.org/10.3233/fi-2009-0019.

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44

Niu, Jiaojiao, Chenchen Huang, Jinhai Li, and Min Fan. "Parallel computing techniques for concept-cognitive learning based on granular computing." International Journal of Machine Learning and Cybernetics 9, no. 11 (February 9, 2018): 1785–805. http://dx.doi.org/10.1007/s13042-018-0783-z.

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45

Faix, Marvin, Emmanuel Mazer, Raphaël Laurent, Mohamad Othman Abdallah, Ronan Le Hy, and Jorge Lobo. "Cognitive Computation." International Journal of Software Science and Computational Intelligence 9, no. 3 (July 2017): 37–58. http://dx.doi.org/10.4018/ijssci.2017070103.

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Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.

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Wang, Yingxu, Bernard Widrow, Lotfi A. Zadeh, Newton Howard, Sally Wood, Virendrakumar C. Bhavsar, Gerhard Budin, et al. "Cognitive Intelligence." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 4 (October 2016): 1–20. http://dx.doi.org/10.4018/ijcini.2016100101.

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The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

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Serb, A., I. Kobyzev, J. Wang, and T. Prodromakis. "A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2164 (December 23, 2019): 20190162. http://dx.doi.org/10.1098/rsta.2019.0162.

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Abstract:

One of the main, long-term objectives of artificial intelligence is the creation of thinking machines. To that end, substantial effort has been placed into designing cognitive systems; i.e. systems that can manipulate semantic-level information. A substantial part of that effort is oriented towards designing the mathematical machinery underlying cognition in a way that is very efficiently implementable in hardware. In this work, we propose a ‘semi-holographic’ representation system that can be implemented in hardware using only multiplexing and addition operations, thus avoiding the need for expensive multiplication. The resulting architecture can be readily constructed by recycling standard microprocessor elements and is capable of performing two key mathematical operations frequently used in cognition, superposition and binding, within a budget of below 6 pJ for 64-bit operands. Our proposed ‘cognitive processing unit’ is intended as just one (albeit crucial) part of much larger cognitive systems where artificial neural networks of all kinds and associative memories work in concord to give rise to intelligence. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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Solanki, Arun, and Deepak Kumar Jain. "Emerging Trends and Applications in Cognitive Computing." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 812–17. http://dx.doi.org/10.2174/266625581305201028104513.

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Mi, Yunlong, Pei Quan, Yong Shi, and Zongrun Wang. "Concept-cognitive computing system for dynamic classification." European Journal of Operational Research 301, no. 1 (August 2022): 287–99. http://dx.doi.org/10.1016/j.ejor.2021.11.003.

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YAMAMICHI, Shintaro. "Electronic Packaging Technology for Cognitive Computing Era." Journal of the Surface Finishing Society of Japan 66, no. 2 (2015): 28–32. http://dx.doi.org/10.4139/sfj.66.28.

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