Machine learning in Cyber-Physical Systems and manufacturing singularity – It does not mean total automation, human is still in the centre: Part II – In-CPS and a view from community on Industry 4.0 impact on society
Goran D. Putnik 1, 2  
,   Vaibhav Shah 3, 2,   Zlata Putnik 2,   Luis Ferreira 4
 
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1
University of Minho, School of Engineering, Department of Production and Systems Engineering, Portugal
2
ALGORITMI Research Centre, Universidade do Minho, Portugal
3
University of Minho, School of Engineering, Department of Information Systems, Portugal
4
2Ai – School of Technology, Polytechnic Institute of Cávado and Ave, Portugal
Final revision date: 2021-03-10
Acceptance date: 2021-03-11
Online publication date: 2021-03-29
Publication date: 2021-03-29
 
Journal of Machine Engineering 2021;21(1):133–153
 
KEYWORDS
ABSTRACT
In many discourses, popular as well as scientific, it is suggested that the “massive” use of Artificial Intelligence (AI), including Machine Learning (ML), and reaching the point of “singularity” through so-called Artificial General Intelligence (AGI), and Artificial Super-Intelligence (ASI), will completely exclude humans from decision making, resulting in total dominance of machines over human race. Speaking in terms of manufacturing systems, it would mean that the intelligence and total automation would be achieved (once the humans are excluded). The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, the ML algorithms will be not able to become totally autonomous and, consequently, the human role will be indispensable. In the context of the question, the authors of this paper introduce the notion of the manufacturing singularity and present an intelligent machine architecture towards the manufacturing singularity, arguing that the intelligent machine will always be human dependent. In addition, concerning the manufacturing, the human will remain in the centre of Cyber-Physical Systems (CPS) and in Industry 4.0. The methodology to support this argument is inductive, similarly to the methodology applied in a number of texts found in literature, and based on computational requirements of inductive inference based machine learning. The argumentation is supported by several experiments that demonstrate the role of human within the process of machine learning. Based on the exposed considerations, a generic architecture of intelligent CPS, with embedded ML functional modules in multiple learning loops, is proposed in order to evaluate way of use of ML functionality in the context of CPS. Similar to other papers found in literature, due to the (informal) inductive methodology applied, considering that this methodology does not provide an absolute proof in favour of, or against, the hypothesis defined, the paper represents a kind of position paper. The paper is divided into two parts. In the first part a review of argumentation from literature in favour of and against the thesis on the human role in future was presented, as well as the concept of the manufacturing singularity was introduced. Furthermore, an intelligent machine architecture towards the manufacturing singularity was proposed, arguing that the intelligent machine will be always human dependent and, concerning the manufacturing, the human will remain in the centre. The argumentation is based on the phenomenon related to computational machine learning paradigm, as intrinsic feature of the AI/ML , through the inductive inference based ML algorithms, whose effectiveness is conditioned by the human participation. In the second part, an architecture of the Cyber-Physical (Production) Systems (CPPS) with multiple learning loops is presented, together with a set of experiments demonstrating the indispensable human role. Finally, a discussion of the problem from the manufacturing community point of view on future of human role in Industry 4.0 as the environment for advanced AI/ML applications is registered.
 
REFERENCES (70)
1.
HAWKING S., RUSSELL S., TEGMARK M., WILCZEK F., 2017, Transcendence Looks at the Implications of Artificial Intelligence - but are We Taking AI Seriously Enough? The Independent, 23-Oct-2017.
 
2.
MARTIN B., 2014, AI Could Kill Us All: Meet the Man Taking the Threat Seriously, The Next Web, https://thenextweb.com/insider..., [Accessed: 04-Mar-2021].
 
3.
ARMSTRONG S., 2014, Artificial Intelligence Poses “Extinction Risk” to Humanity Says Oxford University’s Stuart Armstrong, HuffPost UK, Huffington Post UK, https://www.huffingtonpost.co...., [Accessed: 04-Mar-2021].
 
4.
WHEWELL W., The Philosophy of the Inductive Sciences: (Volume 1) Founded Upon Their History, Cambridge Library Collection – Philosophy, 1840.
 
5.
SCHNEIDER S., WILEY J., 2008, Science Fiction and Philosophy: From Time Travel to Superintelligence, Wiley-Blackwell.
 
6.
RAHMAN W., 2020, What Artificial Intelligence is, isn’t and migh Become? AI and Machine Learning, SAGE, 10–14.
 
7.
VOSS P., 2007, Essentials of General Intelligence: The Direct Path to Artificial General Intelligence, Cogn. Technol., 8, 131–157.
 
8.
BRINGSJORD S., 1997, Strong AI Is Simply Silly, AI Mag., 18, 9-10.
 
9.
ZHANG X., MING X., LIU Z., YIN D., CHEN Z., CHANG Y., 2019, A Reference Framework and Overall Planning of Industrial Artificial Intelligence (I-AI) for New Application Scenarios, The International Journal of Advanced Manufacturing Technology, 101, 2367–2389.
 
10.
WILLIAMS A.E., 2020, The Necessity of General Collective Intelligence Driven Processes in Achieving Pervasive Manufacturing, https://osf.io/preprints/afric....
 
11.
PUTNIK G.D., PUTNIK Z., 2010, A Semiotic Framework for Manufacturing Systems Integration -Part I: Generative Integration Model, International Journal of Computer Integrated Manufacturing, 23/8, 691–709.
 
12.
BAICUN W., JIYUAN Z., XIANMING Q., JINGCHEN D., YANHONG Z., 2018, Research on New-Generation Intelligent Manufacturing Based on Human-Cyber-Physical Systems, Strategic Study of Chinese Academy of Engineering, 20/4, 29–34.
 
13.
DIMIDUK D.M., HOLM E.A., NIEZGODA S.R., 2018., Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering, Integrating Materials and Manufacturing Innovation, 7/3, 157–172.
 
14.
FUTCHER M., 2018, Competitive Advantage During Industry 4.0: the Case for South African Manufacturing SMEs, MSc dissertation, University of the Witwatersrand, Johannesburg, South Africa.
 
15.
HEIN A.M., 2018., Designing As Computing: Towards Designing Machines. https://www.researchgatenet/ publication/322804487_DESIGNING_AS_COMPUTING_TOWARDS_DESIGNING_MACHINES.
 
16.
NOOR A.K., 2017, AI and the Future of the Machine Design, Mechanical Engineering, 139/10, 38–43.
 
17.
OLIFF H., LIU Y., KUMAR M., WILLIAMS M., RYAN M., 2020., Reinforcement Learning for Facilitating Human-Robot-Interaction in Manufacturing, Journal of Manufacturing Systems, 56, 326–340.
 
18.
MOU X., 2019, Artificial Intelligence: Investment Trends and Selected Industry Uses, International Finance Corporation, 1–8, https://openknowledge.worldban....
 
19.
HEIN A.M., BAXTER S., 2018, Artificial Intelligence for Interstellar Travel, arXiv preprint 1811.06526.
 
20.
MILLER J.D., FELTON D., 2017, The Fermi Paradox, Bayes’ Rule, and Existential Risk Management, Futures, 86, 44–57.
 
21.
ASHBY W.R., 2011, An Introduction to Cybernetics, J. Wiley.
 
22.
PUTNIK G.D., SHAH V., PUTNIK Z., FERREIRA L., 2020, Machine Learning in Cyber-Physical Systems and Manufacturing Singularity–it Does not Mean Total Automation, Human is Still in the Centre: Part I–Manufacturing Singularity and an Intelligent Machine Architecture, Journal of Machine Engineering, 20/4, 161–184.
 
23.
PUTNIK G.D., FERREIRA L., LOPES N., PUTNIK Z., 2019, What is a Cyber-Physical System: Definitions and Models Spectrum, FME Transactions, 47, 663–674.
 
24.
LEE E.A., 2008, Cyber Physical Systems: Design Challenges, 2008, 11th IEEE Int. Symp. Object Component-Oriented Real-Time Distrib. Comput., 363–369.
 
25.
JEMIELNIAK K., 2019, Contemporary Challenges in Tool Condition Monitoring, Journal of Machine Engineering, 19/1, 48–61.
 
26.
GRZESIK W., RECH J., 2019, Development of Tribo-Testers for Predicting Metal Cutting Friction, Journal.
 
27.
of Machine Engineering, 19/1, 62–70.
 
28.
SCHÖRGHOFER P., PAUKER F., LEDER N., MANGLER J., RAMSAUER C., BLEICHER F., 2019, Using Sensory Tool Holder Data for Optimizing Production Processes, Journal of Machine Engineering, 19/3, 43–55.
 
29.
JEDRZEJEWSKI J., WINIARSKI Z., KWASNY W., 2020, Research on Forced Cooling of Machine Tools and its Operational Effects, Journal of Machine Engineering, 20/2, 18–38.
 
30.
STRYCZEK R., SZCZEPKA W., 2019, Simulation Tests of Adaptive Control Strategies for CNC Machine Tools, Journal of Machine Engineering, 19/2, 73–82.
 
31.
BRECHER C., WETZEL A., BERNERS T., EPPLE A., 2019, Increasing Productivity of Cutting Processes by Real-Time Compensation of Tool Deflection Due to Process Forces, Journal of Machine Engineering, 19/1, 16–27.
 
32.
DENKENA B., BERGMANN B., HANDRUP M., WITT M., 2020, Material Identification During Turning by Neural Network, Journal of Machine Engineering, 20/2, 65–76.
 
33.
GLÄNZEL J., NAUMANN A., KUMAR T.S., 2019, Parallel Computing in Automation of Decoupled Fluid-Thermostructural Simulation Approach, Journal of Machine Engineering, 2020, 20/2, 39–52.
 
34.
ANDRES-MALDONADO P., AMEIGEIRAS P., PRADOS-GARZON J., NAVARRO-ORTIZ J., LOPEZ-SOLER J.M., 2017, Narrowband IoT Data Transmission Procedures for Massive Machine-Type Communications, IEEE Netw., 31/6, 8–15.
 
35.
]34] DAHLEM P., EMONTS D., SANDERS M.P., SCHMITT R.H., 2020, A Review on Enabling Technologies for Resilient and Traceable On-Machine Measurements, Journal of Machine Engineering, 20/2, 5–17.
 
36.
FUJISHIMA M., MORI M., NARIMATSU K., IRINO N., 2019, Utilisation of IoT and Sensing for Machine Tools, Journal of Machine Engineering, 19/1, 38–47.
 
37.
JĘDRZEJEWSKI J., KWAŚNY W., 2015, Discussion of Machine Tool Intelligence, Based on Selected Concepts and Research, Journal of Machine Engineering, 15/4, 5–26.
 
38.
DITTRICH M.A., DENKENA B., BOUJNAH H., UHLICH F., 2019, Autonomous Machining–Recent Advances in Process Planning and Control, Journal of Machine Engineering, 19/1, 28–37.
 
39.
VALIANT L., 1984, A Theory of the Learnable, Communications of the ACM, 27/11, 1134–1142.
 
40.
MICLET L., 1990, Grammatical Inference, Bunke H. & Sanfeliu A. (Eds.) Syntactic and Structural Pattern Recognition—Theory and Applications, World Scientific. 237–290.
 
41.
PUTNIK G.D., ROSAS J.A., 2001, Manufacturing System Design: Towards Application of Inductive Inference, in Proceedings of the International Workshop on Emerging Synthesis - IWES 01, CIRP Sponsored, Bled, Slovenia.
 
42.
SHAH V., PUTNIK G.D., 2019, Machine Learning Based Manufacturing Control System for Intelligent Cyber-Physical Systems, FME Transactions, 47/4, 802–809.
 
43.
DENNING P.J., DENNIS J.B., QUALITZ J.E., 1978, Machines, Languages, and Computation, Prentice-Hall.
 
44.
Shah V., 2015, Contribution to Automatic Synthesis of Formal Theories of Production Systems and Virtual Enterprises, Doctoral Thesis, University of Minho, Guimarães.
 
45.
PUTNIK G., 2001, BM_Virtual Enterprise Architecture Reference Model, A. Gunasekaran (Ed.), Agile Manufacturing, 21st Century Manufacturing Strategy, Elsevier Science Publ., 73–93.
 
46.
ANGLUIN D., 1987, Learning Regular Sets from Queries and Counterexamples, Information and Computation, 75/2, 87–106.
 
47.
NATARAJAN B.K., 2014, Machine Learning: a Theoretical Approach, Elsevier.
 
48.
RICHETIN M., VERNADAT F., 1984, Efficient Regular Grammatical Inference for Pattern Recognition, Pattern Recognition, 17/2, 245–250.
 
49.
RODGER R.S., ROSEBRUGH R.D., 1979, Computing a Grammar for Sequences of Behavioural Acts, Animal Behaviour, 27, 737–749.
 
50.
SAKAKIBARA Y., 1990, Learning Context-Free Grammars from Structural Data in Polynomial Time, Theoretical Computer Science, 76/2–3, 223–242.
 
51.
LEE K.F., 2018, AI superpowers: China, Silicon Valley, and the New World Order, Houghton Mifflin Harcourt.
 
52.
REISINGER D., 2019, AI Expert Says Automation Could Replace 40% of Jobs in 15 Years, Fortune, https://fortune.com/2019/01/10....
 
53.
NICA E., MANOLE C., STAN C.I., 2018, A Laborless Society? How Highly Automated Environments and Breakthroughs in Artificial Intelligence Bring About Innovative Kinds of Skills and Employment Disruptions, Altering the Nature of Business Process and Affecting the Path of Economic Growth, Journal of Self-Governance & Management Economics, 6/4, 25–30.
 
54.
NILSSON N.J., 1984, Artificial Intelligence, Employment, and Income, AI magazine, 5/2, 5–14.
 
55.
HAMAGUCHI N., KONDO K., 2018, Regional Employment and Artificial Intelligence in Japan, Research Institute of Economy, Trade and Industry, RIETI, Japan.
 
56.
HALAl W., KOLBER J., DAVIES O., GLOBAL T., 2016, Forecasts of AI and Future Jobs in 2030: Muddling Through Likely, with Two Alternative Scenarios, Journal of Futures Studies, 21/2, 83–96.
 
57.
MENON J., 2019, Why the Fourth Industrial Revolution Could Spell More Jobs – Not Fewer, World Economic Forum, https://www.weforum.org/agenda....
 
58.
SCHILLER B., 2015, How the Robots Will Take Your Job and Kill the Economy, FastCompany, https://www.fastcompany.com/30....
 
59.
Drives&Control, 2016, Industry 4.0 will be a factor in 5m job losses in next five years, Drives&Control, https://drivesncontrols.com/ne....
 
60.
DAVIDSON P., 2017, Automation Could Kill 73 Million U.S. Jobs by 2030, USA Today, https://eu.usatoday. com/story/money/2017/11/29/automation-could-kill-73-million-u-s-jobs-2030/899878001/.
 
61.
GARIMELLA K., 2018, Job Loss from AI? There’s MORE to Fear, FORBES, https://www.forbes.com/sites/ cognitiveworld/2018/08/07/job-loss-from-ai-theres-more-to-fear/#4179fab023eb.
 
62.
WALKER W., 2019, A Third of Manufacturers Expect Job Losses Due to Industry 4.0 Advancements, Finds Make UK Study, British Plastics and Rubber Magazine, https://www.britishplastics.co... turers-expect-job-losses-due-to-industry-4/.
 
63.
LA DUKE P., 2018, Robots are Stealing our Jobs, Entrepreneur, https://www.entrepreneur.com/a....
 
64.
VERMEULEN B., KESSELHUT J., PYKA A., SAVIOTTI P.P., 2018, The Impact of Automation on Employment: Just the Uvula Structural Change? Sustainability, 10/5, 1661.
 
65.
DAHLIN E., 2019, Are Robots Stealing Our Jobs? Socius: Sociological Research for a Dynamic World, 5, 1–14.
 
66.
MANYIKA J., et al., 2017, Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation, McKinsey Global Institute.
 
67.
LEOPOLD A.T., RATCHEVA V.S., ZAHIDI S., 2018, The Future of Jobs Report 2018, World Economic Forum.
 
68.
WONG C., 2019, Industry 4.0 Could Create Millions of New Jobs, Futurithmic, https://www.futurithmic.com/ 2019/02/13/industry-4-0-could-create-millions-new-jobs/.
 
69.
GENTILI A., COMPAGNUCCI F., GALLEGATI M., VALENTINI E., 2020, Are Machines Stealing our Jobs? Cambridge Journal of Regions, Economy and Society, 13/1, 153–173.
 
70.
COLAGROSSI M., 2019, Will Robots Steal our Jobs? World Economic Forum.
 
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