Artificial cognitive systems: the next generation of the digital twin. An opinion.

David Jones
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引用次数: 2

Abstract

The digital twin is often presented as the solution to Industry 4.0 and, while there are many areas where this may be the case, there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability, cause and effect, and planning that Industry 4.0 requires. As the limitations of machine learning are beginning to be understood, the paradigm of strong artificial intelligence is emerging. The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world. This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly. This argument is based on the inherent similarities between the digital twin and artificial cognitive systems, and the insights that can already be seen in aligning the two approaches.
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人工认知系统:下一代数字孪生。一个意见。
数字孪生通常被认为是工业4.0的解决方案,尽管在许多领域可能会出现这种情况,但依赖现有的机器学习方法可能无法提供工业4.0所需的高水平认知能力,如适应性、因果关系和规划。随着人们开始理解机器学习的局限性,强人工智能的范式正在出现。人工认知系统领域是强大的人工智能范式的一部分,旨在生成能够在学习和与世界互动中模仿生物系统的计算系统。本文认为,人工认知系统为工业4.0更高层次的认知挑战提供了解决方案,数字孪生研究应相应地朝着人工认知的方向发展。这一论点基于数字孪生和人工认知系统之间固有的相似性,以及在调整这两种方法时已经可以看到的见解。
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Digital Twin
Digital Twin digital twin technologies-
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期刊介绍: Digital Twin is a rapid multidisciplinary open access publishing platform for state-of-the-art, basic, scientific and applied research on digital twin technologies. Digital Twin covers all areas related digital twin technologies, including broad fields such as smart manufacturing, civil and industrial engineering, healthcare, agriculture, and many others. The platform is open to submissions from researchers, practitioners and experts, and all articles will benefit from open peer review.  The aim of Digital Twin is to advance the state-of-the-art in digital twin research and encourage innovation by highlighting efficient, robust and sustainable multidisciplinary applications across a variety of fields. Challenges can be addressed using theoretical, methodological, and technological approaches. The scope of Digital Twin includes, but is not limited to, the following areas:  ● Digital twin concepts, architecture, and frameworks ● Digital twin theory and method ● Digital twin key technologies and tools ● Digital twin applications and case studies ● Digital twin implementation ● Digital twin services ● Digital twin security ● Digital twin standards Digital twin also focuses on applications within and across broad sectors including: ● Smart manufacturing ● Aviation and aerospace ● Smart cities and construction ● Healthcare and medicine ● Robotics ● Shipping, vehicles and railways ● Industrial engineering and engineering management ● Agriculture ● Mining ● Power, energy and environment Digital Twin features a range of article types including research articles, case studies, method articles, study protocols, software tools, systematic reviews, data notes, brief reports, and opinion articles.
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