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
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University of Minho, School of Engineering, Department of Production and Systems Engineering, Portugal
University of Minho, School of Engineering, Department of Information Systems, Portugal
ALGORITMI Research Centre, Universidade do Minho, Portugal
Polytechnic Institute of Cávado and Ave, School of Technology, Portugal
Submission date: 2020-10-08
Acceptance date: 2020-11-28
Online publication date: 2020-12-04
Publication date: 2020-12-16
Journal of Machine Engineering 2020;20(4):161-184
In many popular, as well scientific, discourses it is suggested that the "massive" use of Artificial Intelligence, including Machine Learning, 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 there will be achieved intelligent and total automation (once the humans will be excluded). The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, that the ML algorithms will be not able to became totally autonomous and, consequently, that 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 an intelligent machine architecture towards the manufacturing singularity, arguing that the intelligent machine will be always human dependent, and that, concerning the manufacturing, the human will remain in the centre of Cyber-Physical Systems (CPS) and in I4.0. 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, in order to evaluate way of use of ML functionality in the context of CPPS/CPS. Similarly to other papers found in literature, due to the (informal) inductive methodology applied, considering that this methodology doesn’t 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, both in favor of and against the thesis on the human role in future, is presented. In this part a concept of the manufacturing singularity is introduced, as well as an intelligent machine architecture towards the manufacturing singularity is presented, arguing that the intelligent machine will always be human dependent, and that, concerning the manufacturing, the human will remain in the centre. The argu-mentation is based on the phenomenon related to computational machine learning paradigm, as intrinsic feature of the AI/MI through the inductive inference based machine learning algorithms, whose effectiveness is conditioned by the human participation. In the second part, an architecture of the Cyber-Physical (Production) Systems with multiple learning loops is presented, together with a set of experiments demonstrating the indispensable human role. Also, 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 included in this part.
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