Artificial Intelligence

In the last few years, quantum computing has achieved new successes, such as Google’s quantum supremacy experiment [1], and has been showing adoption by large industrial firms to tackle complex problems. But what has led up to these developments? What kinds of problems can we expect to be able to solve in the near term with quantum computing? What are the challenges that we encounter with this technology and deploying within industrial settings?

Engineers are often uncertain about the application of machine learning (ML) due to the amount of different machine learning methods and the complexity of modeling. Thus, the use of ML applications in manufacturing companies remains behind the technical possibilities. This paper presents an intuitive ML guideline for engineers to reduce this uncertainty. The guideline comprises a process model with AI-based solutions to common problems of product development and production. An industrial example is used to demonstrate the functionality and the possibilities of the guide.

Industry 4.0 is a political concept intended to help German manufacturing companies to exploit data potential. Today, maintenance activities are not proactive by current approaches. Decision support systems based on artificial intelligence allow a change here by even foresighted machine maintenance. However, AI’s opaque decision-making process represents a barrier for users, which endangers its effectiveness. Therefore, this article sheds light on both: the technological as well as social factor for the adoption of AI in Industry 4.0.

With increasing availability of data, the use of machine learning to optimize supply chains becomes attractive, as the accuracy of data analysis can be increased and simultaneously the effort can be reduced. Based on the SCOR model, exemplary approaches are described as a guidance and suitable machine learning methods are presented.

Due to the possibility of operator intervention, semi-autonomous systems allow for a better handling of complexity than fully autonomous systems. The use of a digital twin provides a novel interface for interaction with such systems. This paper describes the implementation of the control and user interface in a system with a digital twin. It is shown how the developed control architecture can be combined with different methods of human-machine interaction and virtual training. With this extended use of the control system by a digital twin the concept can be extended beyond the operation phase and can be used in other phases of the product life cycle.

Due to increasingly specialised, diverse and also new competencies and competency profiles it becomes progressively more difficult to interpret educational achievements and to match requirement profiles with the competencies of individual persons or groups. The computational support with regards to keeping the information up to date, communication, search and analysis is limited if the competencies are described in natural language only. Thus, it seems advantageous to model competencies in a formal and machine-readable specification language. The following article suggests the notion of a generic formal syntax for learning outcomes. We outline how this would allow expressing intricate learning outcomes in a machine-readable ontology and their further processing with the Linked Data- and Semantic Web-approaches.

We are currently experiencing rapid transformation in technologies and society. Due to the convergence of various megatrends, these changes have considerable impacts on everyday life. Our study aims to identify relevant strategies for the digital future of a macro-region (Tyrol, South Tyrol and Veneto). The study conducts semi-structured interviews with representatives of companies, universities and local governments, using the approach of a triple helix model. Based on the empirical analysis, we develop an action plan for the digital transformation of the macro-region.

Everyone is talking about artificial intelligence and machine learning. However, knowledge about what the terms actually mean is often not yet extensively available. The article presents some basic knowledge and shows which application possibilities and added values machine learning can offer for production. Robotics, for example a bin-picking system, benefits in particular from the technologies described. Finally, the article deals with the topic of explainability of machine learning processes. For technical, legal and social reasons, decoding the “black box” is an essential task.