Should you trust industrial robots

2021 ◽  
pp. 47-49
Keyword(s):  
Artificial Intelligence ◽  
Human Factor ◽  
Cyber Physical Systems ◽  
Industrial Robots ◽  
Manual Labor ◽  
Physical Systems ◽  
Self Organizing

The owners of advanced enterprises try to exclude the human factor from the process as much as possible, confident that artificial intelligence is many times more effective. But among the leaders there are those who deliberately do not trust robots for key stages of production, using them exclusively for rough work. How and why manual labor is used in the age of self-organizing cyber-physical systems is described in the article.

scholarly journals Artificial Intelligence and the Internet of Things in Industry 4.0

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Omar Santos
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Leading Industry ◽  
Key Technologies ◽  
The Internet Of Things ◽  
State Of Art ◽  
Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

2021 ◽  
pp. 42-67
Author(s):  
Evren Daglarli
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Black Box ◽  
The Internet ◽  
Learning Models ◽  
Physical Systems ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Meta Learning ◽  
Black Box Models

Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.

Artificial intelligence for securing industrial-based cyber–physical systems

2021 ◽  
Vol 117 ◽  
pp. 291-298
Author(s):  
Zhihan Lv ◽  
Dongliang Chen ◽  
Ranran Lou ◽  
Ammar Alazab
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Artificial intelligence in cyber physical systems

AI & Society ◽  
2020 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Max Van Kleek ◽  
Omar Santos ◽  
Uchenna Ani
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Artificial intelligence in cyber physical systems

2020 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Max Van Kleek ◽  
Omar Santos ◽  
Uchenna Ani
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Literature Review ◽  
Conceptual Framework ◽  
Cyber Physical Systems ◽  
Industrial Internet Of Things ◽  
Physical Systems ◽  
Industrial Internet ◽  
Selection Decisions ◽  
Future Challenges

Abstract This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.

Sign in / Sign up

Export Citation Format

Share Document