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Journal articles on the topic 'The Knowledge Machine'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Zenkert, Johannes, Christian Weber, Mareike Dornhöfer, Hasan Abu-Rasheed, and Madjid Fathi. "Knowledge Integration in Smart Factories." Encyclopedia 1, no. 3 (August 16, 2021): 792–811. http://dx.doi.org/10.3390/encyclopedia1030061.

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Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.

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2

Nirenburg, Sergei. "Knowledge-based machine translation." Machine Translation 4, no. 1 (March 1989): 5–24. http://dx.doi.org/10.1007/bf00367750.

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3

Walch, Michael, and Dimitris Karagiannis. "Design Thinking and Knowledge Engineering: A Machine Learning Case." International Journal of Machine Learning and Computing 10, no. 6 (December 2020): 765–70. http://dx.doi.org/10.18178/ijmlc.2020.10.6.1003.

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4

Refenes, Apostolos N. "Parallelism in knowledge-based machines." Knowledge Engineering Review 4, no. 1 (March 1989): 53–71. http://dx.doi.org/10.1017/s0269888900004744.

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AbstractThe application area of knowledge-based expert systems is currently providing the main stimulus for developing powerful, parallel computer architectures. Languages for programming knowledge-based applications divide into four broad classes: Functional languages (e.g. LISP), Logic languages (e.g. PROLOG), Rule-Based languages (e.g. OPS5), and, what we refer to as self-organizing networks (e.g. BOLTZMANN machines).Despite their many differences, a common problem for all language classes and their supporting machine architectures is parallelism: how to de-compose a single computation into a number of parallel tasks that can be distributed across an ensemble of processors. The aim of this paper is to review the four types of language for programming knowledge-based expert systems, and their supporting parallel machine architectures. In doing so we analyze the concepts and relationships that exist between the programming languages and their parallel machine architectures in terms of their strengths and limitations for exploiting parallelization.

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5

Li, Fashen, Lian Li, Jianping Yin, Liang Huang, Qingguo Zhou, Ning An, Yong Zhang, et al. "Machine knowledge and human cognition." Big Data Mining and Analytics 3, no. 4 (December 2020): 292–99. http://dx.doi.org/10.26599/bdma.2020.9020009.

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6

Bergadano, F., Y. Kodratoff, and K. Morik. "Machine Learning and Knowledge Acquisition." AI Communications 5, no. 1 (1992): 19–24. http://dx.doi.org/10.3233/aic-1992-5102.

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7

Tandon, Niket, Aparna S. Varde, and Gerard de Melo. "Commonsense Knowledge in Machine Intelligence." ACM SIGMOD Record 46, no. 4 (February 22, 2018): 49–52. http://dx.doi.org/10.1145/3186549.3186562.

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8

Benker, H., J. M. Beacco, M. Dorochevsky, Th Jeffré, A. Pöhlmann, J. Noyé, B. Poterie, J. C. Syre, O. Thibault, and G. Watzlawik. "KCM: a knowledge crunching machine." ACM SIGARCH Computer Architecture News 17, no. 3 (June 1989): 186–94. http://dx.doi.org/10.1145/74926.74947.

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9

Suer, Gursel A., and Cihan Dagli. "Knowledge-based single machine scheduling." Computers & Industrial Engineering 23, no. 1-4 (November 1992): 149–52. http://dx.doi.org/10.1016/0360-8352(92)90085-x.

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10

Li, Fashen, Lian Li, Jianping Yin, Yong Zhang, Qingguo Zhou, and Kun Kuang. "How to Interpret Machine Knowledge." Engineering 6, no. 3 (March 2020): 218–20. http://dx.doi.org/10.1016/j.eng.2019.11.013.

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11

Weikum, Gerhard, Xin Luna Dong, Simon Razniewski, and Fabian Suchanek. "Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases." Foundations and Trends® in Databases 10, no. 2-4 (2021): 108–490. http://dx.doi.org/10.1561/1900000064.

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12

Weber, Patrick, Nicolas Weber, Michael Goesele, and Rüdiger Kabst. "Prospect for Knowledge in Survey Data." Social Science Computer Review 36, no. 5 (September 12, 2017): 575–90. http://dx.doi.org/10.1177/0894439317725836.

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Policy making depends on good knowledge of the corresponding target audience. To maximize the designated outcome, it is essential to understand the underlying coherences. Machine learning techniques are capable of analyzing data containing behavioral aspects, evaluations, attitudes, and social values. We show how existing machine learning techniques can be used to identify behavioral aspects of human decision-making and to predict human behavior. These techniques allow to extract high resolution decision functions that enable to draw conclusions on human behavior. Our focus is on voter turnout, for which we use data acquired by the European Social Survey on the German national vote. We show how to train an artificial expert and how to extract the behavioral aspects to build optimized policies. Our method achieves an increase in adjusted R2 of 102% compared to a classic logistic regression prediction. We further evaluate the performance of our method compared to other machine learning techniques such as support vector machines and random forests. The results show that it is possible to better understand unknown variable relationships.

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13

Wang, Xing, Zhaopeng Tu, and Min Zhang. "Incorporating Statistical Machine Translation Word Knowledge Into Neural Machine Translation." IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, no. 12 (December 2018): 2255–66. http://dx.doi.org/10.1109/taslp.2018.2860287.

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14

Briken, Kendra. "Welcome in the machine: Human–machine relations and knowledge capture." Capital & Class 44, no. 2 (March 23, 2020): 159–71. http://dx.doi.org/10.1177/0309816819899418.

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This article discusses new technologies in regard to their potential to capture workers’ situated knowledge. Machines are said to substitute but also to contribute to the labour process in collaboration with human skill sets. ‘Industry 4.0’ became the policy-wide shorthand to describe the new quality of real-time interconnectedness and feedback loops, known as cyber-physical systems within industry and engineering sciences. Data flows generated in these systems are used to continuously improve work processes by extracting information down to the very micro-level of neuroergonomics. In this process, workers’ interactions with the system are extracted, fed back and processed for future use and improvement. The article argues that in addition to the potential for extraction of new (bodily) knowledge, shifting skill use and the potential for new forms of control, new technologies contain the potential to extract situated knowledge owned by the worker and crucial for resistance and collective struggles.

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15

Humphrey, Matt, Sally Jo Cunningham, and Ian H. Witten. "Knowledge Visualization Techniques for Machine Learning." Intelligent Data Analysis 2, no. 4 (October 1, 1998): 333–47. http://dx.doi.org/10.3233/ida-1998-2406.

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16

Li, Qiang, Derek F. Wong, Lidia S. Chao, Muhua Zhu, Tong Xiao, Jingbo Zhu, and Min Zhang. "Linguistic Knowledge-Aware Neural Machine Translation." IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, no. 12 (December 2018): 2341–54. http://dx.doi.org/10.1109/taslp.2018.2864648.

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17

HUMPHREY, M., S. CUNNINGHAM, and I. WITTEN. "Knowledge visualization techniques for machine learning." Intelligent Data Analysis 2, no. 1-4 (1998): 333–47. http://dx.doi.org/10.1016/s1088-467x(98)00029-8.

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18

Leitch, Isabella. "MACHINE RETRIEVAL OF KNOWLEDGE OF NUTRITION." Nutrition Reviews 20, no. 3 (April 27, 2009): 65–67. http://dx.doi.org/10.1111/j.1753-4887.1962.tb04550.x.

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19

Haigh, Thomas. "Emanuel Goldberg and his knowledge machine." Journal of the American Society for Information Science and Technology 63, no. 2 (October 12, 2011): 427–28. http://dx.doi.org/10.1002/asi.21658.

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20

Deng, Changyu, Xunbi Ji, Colton Rainey, Jianyu Zhang, and Wei Lu. "Integrating Machine Learning with Human Knowledge." iScience 23, no. 11 (November 2020): 101656. http://dx.doi.org/10.1016/j.isci.2020.101656.

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21

Westerhoff, Martin. "Vehicle 5.0 The Knowledge-based Machine." MTZ worldwide 78, no. 1 (December 9, 2016): 8–13. http://dx.doi.org/10.1007/s38313-016-0173-4.

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22

Westerhoff, Martin. "Vehicle 5.0 The Knowledge-based Machine." ATZelektronik worldwide 12, no. 1 (February 2017): 8–13. http://dx.doi.org/10.1007/s38314-016-0100-0.

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23

Quantz, Joachim, and Birte Schmitz. "Knowledge-based disambiguation for machine translation." Minds and Machines 4, no. 1 (February 1994): 39–57. http://dx.doi.org/10.1007/bf00974203.

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24

Westerhoff, Martin. "Vehicle 5.0 The Knowledge-based Machine." ATZ worldwide 119, no. 1 (January 2017): 8–13. http://dx.doi.org/10.1007/s38311-016-0179-1.

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25

Nédellec, Claire. "Integration of Machine Learning and Knowledge Acquisition." Knowledge Engineering Review 10, no. 1 (March 1995): 77–81. http://dx.doi.org/10.1017/s026988890000730x.

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“Integration of Machine Learning and Knowledge Acquisition” may be a surprising title for an ECAI-94 workshop, since most machine learning (ML) systems are intended for knowledge acquisition (KA). So what seems problematic about integrating ML and KA? The answer lies in the difference between the approaches developed by what is referred to as ML and KA research. Apart from sonic major exceptions, such as learning apprentice tools (Mitchell et al., 1989), or libraries like the Machine Learning Toolbox (MLT Consortium, 1993), most ML algorithms have been described without any characterization in terms of real application needs, in terms of what they could be effectively useful for. Although ML methods have been applied to “real world” problems few general and reusable conclusions have been drawn from these knowledge acquisition experiments. As ML techniques become more and more sophisticated and able to produce various forms of knowledge, the number of possible applications grows. ML methods tend then to be more precisely specified in terms of the domain knowledge initially required, the control knowledge to be set and the nature of the system output (MLT Consortium, 1993; Kodratoff et al., 1994).

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26

Vámos, T., and F. Katona. "Knowledge-based Pattern-supported Man-Machine Interaction." IFAC Proceedings Volumes 25, no. 9 (June 1992): 83–88. http://dx.doi.org/10.1016/s1474-6670(17)50174-3.

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27

POTTER, S., M. J. DARLINGTON, S. J. CULLEY, and P. K. CHAWDHRY. "Design synthesis knowledge and inductive machine learning." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 3 (June 2001): 233–49. http://dx.doi.org/10.1017/s0890060401153047.

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A crucial early stage in the engineering design process is the conceptual design phase, during which an initial solution design is generated. The quality of this initial design has a great bearing on the quality and success of the produced artefact. Typically, the knowledge required to perform this task is only acquired through many years of experience, and so is often at a premium. This has led to a number of attempts to automate this phase using intelligent computer systems. However, the knowledge of how to generate designs has proved difficult to acquire directly from human experts, and as a result, is often unsatisfactory in these systems. The application of inductive machine learning techniques to the acquisition of this sort of knowledge has been advocated as one approach to overcoming the difficulties surrounding its capture. Rather than acquiring the knowledge from human experts, the knowledge would be inferred automatically from a set of examples of the design process. This paper describes the authors' investigations into the general viability of this approach in the context of one particular conceptual design task, that of the design of fluid power circuits. The analysis of a series of experiments highlights a number of issues that would seem to arise regardless of the working domain or particular machine learning algorithm used. These issues, presented and discussed here, cast serious doubts upon the practicality of such an approach to knowledge acquisition, given the current state of the art.

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28

Nacsa, János, and Aitor Alzaga. "Knowledge Management Support for Machine Tool Designers." IFAC Proceedings Volumes 36, no. 3 (April 2003): 61–66. http://dx.doi.org/10.1016/s1474-6670(17)37736-4.

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29

Johannsen, G. "Knowledge Based Design of Human-Machine Interfaces." IFAC Proceedings Volumes 26, no. 2 (July 1993): 1113–17. http://dx.doi.org/10.1016/s1474-6670(17)48643-5.

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30

MacInnes, J., S. Santosa, and W. Wright. "Visual Classification: Expert Knowledge Guides Machine Learning." IEEE Computer Graphics and Applications 30, no. 1 (January 2010): 8–14. http://dx.doi.org/10.1109/mcg.2010.18.

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31

Karayel, Durmuş, S. Serdar Ozkan, and Fahri Vatansever. "Integrated Knowledge-Based System for Machine Design." Advances in Mechanical Engineering 5 (January 2013): 702590. http://dx.doi.org/10.1155/2013/702590.

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32

NIRENBURG, S. "Lexicographic Support for Knowledge-Based Machine Translation." Literary and Linguistic Computing 4, no. 3 (July 1, 1989): 185–90. http://dx.doi.org/10.1093/llc/4.3.185.

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33

Yan, Ruqiang, Fei Shen, Chuang Sun, and Xuefeng Chen. "Knowledge Transfer for Rotary Machine Fault Diagnosis." IEEE Sensors Journal 20, no. 15 (August 1, 2020): 8374–93. http://dx.doi.org/10.1109/jsen.2019.2949057.

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34

Evett, M. P., J. A. Hendler, and L. Spector. "Parallel Knowledge Representation on the Connection Machine." Journal of Parallel and Distributed Computing 22, no. 2 (August 1994): 168–84. http://dx.doi.org/10.1006/jpdc.1994.1079.

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35

Heragu, Sunderesh S. "Knowledge based approach to machine cell layout." Computers & Industrial Engineering 17, no. 1-4 (January 1989): 37–42. http://dx.doi.org/10.1016/0360-8352(89)90033-8.

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36

Yu, Jingyuan, Xiaoji Zhou, and Shan Feng. "Man-machine collaborated knowledge creation in HWMSE." Journal of Systems Science and Systems Engineering 14, no. 4 (December 2005): 462–75. http://dx.doi.org/10.1007/s11518-006-0205-8.

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37

Bratko, Ivan. "Applications of Machine Learning: Towards knowledge synthesis." New Generation Computing 11, no. 3-4 (September 1993): 343–60. http://dx.doi.org/10.1007/bf03037182.

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38

Durmus, Yunus, and Ertan Onur. "Service Knowledge Discovery in Smart Machine Networks." Wireless Personal Communications 81, no. 4 (March 8, 2015): 1455–80. http://dx.doi.org/10.1007/s11277-015-2483-2.

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39

Johannsen, G. "Knowledge-based design of human-machine interfaces." Control Engineering Practice 3, no. 2 (February 1995): 267–73. http://dx.doi.org/10.1016/0967-0661(94)00085-u.

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40

Twine, S. "Knowledge representation and organization in machine learning." Information and Software Technology 32, no. 7 (September 1990): 510–11. http://dx.doi.org/10.1016/0950-5849(90)90171-m.

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41

Elofson, Gregg. "Using machine apprentices for transient knowledge work." Information and Software Technology 36, no. 2 (January 1994): 97–106. http://dx.doi.org/10.1016/0950-5849(94)90089-2.

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42

Kawasaki, Zenshiro, Fumiyuki Yamano, and Noriyuki Yamasaki. "Translator knowledge base for machine translation systems." Machine Translation 6, no. 4 (1991): 265–78. http://dx.doi.org/10.1007/bf00417652.

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43

Cozman, Fabio Gagliardi, and Hugo Neri Munhoz. "Some thoughts on knowledge-enhanced machine learning." International Journal of Approximate Reasoning 136 (September 2021): 308–24. http://dx.doi.org/10.1016/j.ijar.2021.06.003.

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44

Nadin, Mihai. "Semiotic Machine." Public Journal of Semiotics 1, no. 1 (January 1, 2007): 57–75. http://dx.doi.org/10.37693/pjos.2007.1.8815.

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A semiotic machine, no matter how it is embodied or expressed, has to reflect the various understandings of what the knowledge domain of semiotics is. It also has to reflect what methods and means support further acquiring knowledge of semiotics. Moreover, it has to express ways in which knowledge of semiotics is tested, improved, and evaluated. Given the scope of the endeavor of defining the semiotic machine, the methodological approach must be anchored in the living experience of semiotics. Accordingly, the cultural-historic perspective, which is the backbone of any encyclopedic endeavor, is very much like a geological survey for a foundation conceived from a dynamic perspective. The various layers could shed light on a simple aspect of the subject: At which moment in the evolution of semiotics does it make sense to make the association (in whatever form) to tools and to what would become the notion of a machine? Reciprocally, we would have to explain how the various understandings of the notions tool and machine are pertinent to whatever was the practice of semiotics at a certain juncture. Yet another reference cannot be ignored: The reductionist-deterministic view, celebrated in what is known as the Cartesian Revolution. Since that particular junction in our understanding of the world, the reduction of semiotic processes to machine descriptions is no longer a matter of associations (literal or figurative), but a normative dimension implicitly or explicitly expressed in semiotic theories. Given this very intricate relation, we will have to systematize the variety of angles from which various understandings of the compound expression semiotic machine can be defined.In our days, such understandings cover a multitude of aspects, ranging from the desire to build machines that can perform particular semiotic operations to a new understanding of the living, in view of our acquired knowledge of genetics, molecular biology, and information biology. That the computer—a particular form of machine—as an underlying element of a civilization defined primarily as one of information processing, could be and has been considered a semiotic machine deserves further consideration.

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45

Hossayni, Hicham, Imran Khan, Mohammad Aazam, Amin Taleghani-Isfahani, and Noel Crespi. "SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs." Applied Sciences 10, no. 18 (September 11, 2020): 6325. http://dx.doi.org/10.3390/app10186325.

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The recent focus on sustainability and improved efficiency requires innovative approaches in industrial automation. We present SemKoRe, a knowledge graph developed to improve machine maintenance in the industrial domain. SemKoRe is vendor-agnostic, it helps Original Equipment Manufacturers (OEMs) to capture, share and exploit the failure knowledge generated by their customers machines located around the world. Based on our interactions with actual customers, it usually takes several hours to days to fix a machine-related issue. During this time, production stops and incurs cost in terms of lost production. SemKoRe significantly enhances the maintenance process by reducing the failure diagnostic time, and by centralizing machine maintenance knowledge fed by the experts and technicians around the world. We developed flexible architecture to cover our customers’ varying needs, along with failure and machine domain ontologies. To demonstrate the feasibility of SemKoRe, a proof-of-concept is developed. SemKoRe gathers all failure related data in the knowledge graph, and shares it among all connected customers in order to easily solve future failures of the same type. SemKoRe received the approval of several substantial clients located in USA, UK, France, Germany, Italy and China, associated with various segments such as pharmaceutical, automotive, HVAC and food and beverage.

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46

Cliff, Dave, and Noble Jason. "Knowledge-based vision and simple visual machines." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, no. 1358 (August 29, 1997): 1165–75. http://dx.doi.org/10.1098/rstb.1997.0100.

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The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the ‘knowledge’ in knowledge–based vision or form the ‘modelsrsquo; in model–based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place–holders for yet–to–be–identified causal mechanistic interactions. That is, applying the knowledge–based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong.

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47

Rijanto, Achmad, and Suesthi Rahayuningsih. "PENERAPAN MESIN PARUT BERBAHAN BAKAR GAS PADA USAHA MIKRO KERUPUK SAMILER DI DESA KEMASANTANI MOJOKERTO." Martabe : Jurnal Pengabdian Kepada Masyarakat 2, no. 2 (October 31, 2019): 55. http://dx.doi.org/10.31604/jpm.v2i2.55-61.

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This community service activity was in partnership with a samiler cracker micro entrepreneur in the Kemasantani village, Gondang sub-district, Mojokerto district. The problems faced by a partner were a partner who does not yet have a gas-fueled scarring machine, lack of knowledge and skills of a partner in the operation of gas-fueled scarring machines. The solutions to the partner's problem were to provide a gas-fueled grate machine, improve the knowledge and skills of operating a gas-fueled grate machine. The methods used were the procurement, training and assistance of gas-fueled grate machines for micro business owner. The results achieved from this dedication were a partner already had a gas-fueled scarring unit and an increase in knowledge and skills of a partner operating gas-fueled scarring by an average of 50%.

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48

Brecher, Ch, R. Herzog, A. Naumann, R. Spierling, and F. Tzanetos. "OPTIMAL POSITIONING METHODS OF INTEGRAL DEFORMATION SENSORS – EXPERT KNOWLEDGE VERSUS MATHEMATICAL OPTIMIZATION." MM Science Journal 2021, no. 3 (June 30, 2021): 4628–35. http://dx.doi.org/10.17973/mmsj.2021_7_2021069.

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Up to 75 % of the overall work piece error can be caused by the thermo-elastic behavior of the machine tool. Therefore, correction methods based on machine-integrated sensors were intensively researched during the last years, in order to determine the error of the Tool Center Point (TCP) parallel to the process. One of these methods includes the integral deformation sensor (IDS), which detects the deformation along the length of a structural component of the machine. The error of the TCP is modelled based on the measured structural deformations, a mechanical model of the structural parts and a kinematic model of the machine tool. Currently, the sensor setup for specific machines is usually defined by an expert with the help of his or her domain knowledge. There are existing mathematical methods for optimal sensor positioning. The aim of this work is the evaluation of the expert positioning versus the mathematical methods. The parameters to be varied are the lengths and positions of the IDS. Criteria for the evaluation are the achievable accuracy of the TCP error prediction and the sensitivity to small variations of the optimal position, as they might occur during the installation.

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49

Ji Chan, Yum. "Improvements to Linear and Nonlinear Models of Machine Key Components." Impact 2021, no. 1 (February 5, 2021): 15–17. http://dx.doi.org/10.21820/23987073.2021.1.15.

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Precision machinery has come a long way over the years. Factories that once relied on manpower now use machines, and this development has brought with it innumerable benefits including improvements to accuracy, repeatability, productivity and efficiency. Naturally, though, machines are imperfect in that precision of a batch of machines vary slightly. On top of that, machines experience wear and tear or even break-downs. These unpredictable events can be costly to manufacturers. This is why research to better understand factors that affect a machine's precision is important. This knowledge can be used to reduce the issues that occur with machine tools and thereby maximise the efficiency and quality of production. This is the goal of Dr Yum-Ji Chan, Department of Mechanical Engineering, National Chung Hsing University, Taiwan. His research on vibration engineering, structural dynamics and the dynamics of rotors is seeking to better understand machine tools and, in doing so, improve their performance. He believes more research is required to understand the behaviour of specific components in machine tools, and he is seeking to fill this gap in knowledge. This involves understanding the vibration phenomena that occur in components in machine tools and, to do so, Chan and his team are producing accurate dynamic behaviour in machine tool models. This will, in turn, enable researchers to develop virtual machine tools that can monitor the condition of machines.

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50

Asef, Mario. "A Diagram is a Trivial Machine." ARTMargins 5, no. 2 (June 2016): 74–86. http://dx.doi.org/10.1162/artm_a_00148.

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I generate diagrams with the purpose of understanding the narratives, form, and aesthetics of sociocultural and political structures. This leaves room for the production of artistic works that can be introduced into the machinery of everyday life. The idea of the diagram emerged almost at the same time as the idea of the machine, although we cannot really tell which existed first. However, it seems clear that both are intrinsically connected. Machines and diagrams can be seen as a representation of a narrative system that leads the process of the creation of knowledge. What they share is essentially narrative: we create machines using diagrammatic narratives, and with those same narratives, we create knowledge. Narratives are the real machines.

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