Cyber-Physical-Social Intelligence: On Human-Machine-Nature Symbiosis

by Hai Zhuge

Overview

This book explores next-generation artificial intelligence based on the symbiosis between humans, machines and nature, including the rules and emerging patterns of recognition, and the integration and optimization of various flows through cyberspace, physical space and social space. It unveils a reciprocal human-machine-nature symbiotic mechanism together with relevant rules on structuring and evolving reality, and also proposes a multi-dimensional space for modelling reality and managing the methodologies for exploring reality. As such it lays the foundation for the emerging research area cyber-physical-social intelligence. Inspiring researchers and university students to explore the development of intelligence and scientific methodology, it is intended for researchers and broad readers with a basic understanding of computer science and the natural sciences.

Next-generation artificial intelligence will extend machine intelligence and human intelligence to cyber-physical-social intelligence rendered by various interactions in cyberspace, physical space and social space. With the transformational development of science and society, a multi-dimensional reality is emerging and evolving, leading to the generation and development of various spaces obeying different principles. A fundamental scientific challenge is uncovering the essential mechanisms and principles that structure and evolve the reality emerging and evolving along various dimensions. Meeting this challenge requires identifying the basic relations between humans, machines and nature in order to reveal the cyber-physical-social principles.

Product Details

ISBN-13:	9789811373107
Publisher:	Springer Nature Singapore
Publication date:	11/13/2020
Series:	SpringerBriefs in Computer Science
Edition description:	1st ed. 2020
Pages:	351
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Hai Zhuge is an ACM (Association of Computer Machinery) Distinguished Scientist and a Fellow of the British Computer Society. He has made a systematic contribution to semantics and knowledge modelling through fundamental research on the Semantic Link Network and the Resource Space Model based on multi-dimensional methodology. He is leading research on cyber-physical-social intelligence using methodological, theoretical and technical innovations. As an ACM Distinguished Speaker, he has delivered 20 keynotes at international conferences and invited lectures at universities in various countries. As a Professor, he is head of an international research network consisting of the Guangzhou University, the Key Laboratory of Intelligent Information Processing at the Institute of Computing Technology in Chinese Academy of Sciences, the University of Chinese Academy of Sciences, and the System Analytics Research Institute at Aston University. He was a Distinguished Visiting Fellow of the Royal Academy of Engineering. He is the author of four monographs: Cyber-Physical-Social Intelligence on Human-Machine-Nature Symbiosis (Springer, 2019), Multi-Dimensional Summarization in Cyber-Physical Society (Morgan Kaufmann, 20016), The Knowledge Grid: Toward Cyber-Physical Society (World Scientific, 2012), and The Web Resource Space Model (Springer, 2008). He is currently as an Associate Editor of IEEE Intelligent Systems.

Table of Contents

1 Introduction
1.1 Cyber-Physical Society
1.2 Data
1.2.1 General definition of data
1.2.2 Data for computing
1.2.3 The philosophy of between material and mind
1.2.4 Views on Big Data
1.2.5 Big networks of data and humans
1.2.6 The shift of science paradigm
1.2.7 The fourth industrial revolution
1.2.8 Observed system, data, information, knowledge, humans and machines1.2.9 Problems
2 Discovering Implicit Semantic Link in Cyber-Physical Society
2.1 Semantic Link
2.2 A brief history of Semantic Link Network
2.3 Knowledge flowing through semantic links
2.4 Modeling with local views and global views
2.5 The Big Gap
2.6 The Levels of Representation
2.7 The semantic link networks that enable intelligence
3 Dimensions on Data
3.1 Different views of Dimension
3.2 Dimensions on Big Data
3.3 Multi-dimensional category space
3.4 Complex multi-dimensional space and the tasks of analysis
4 Multi-Dimensional Analytics
4.1 Data operation dimension
4.2 System behavior dimension
4.3 Value dimension4.4 Time dimension
4.5 Human dimension
4.6 Strategic planning on multiple dimensions  an example
5 Unconventional Mapping from Data Space into Knowledge Space
5.1 Mapping Representations into Wikipedia
5.2 Mapping data space into knowledge space with cognition
5.3 Mapping data into human-level concepts
5.4 Mapping from representation into knowledge through complex modeling
5.5 From correlation to knowledge
5.6 Human representation and machine representation
5.7 Knowledge flow through cognitive systems
6 Cyber-Physical-Social Infrastructure
6.1 The development of Cyber-Infrastructure
6.2 Big gaps between humans and machines
6.3 Incorporating cognitive architecture into cyber-infrastructure
7 Communities of Cognition and Practice
8 New Paradigm of Science
8.1 The evolving paradigm of science
8.2 Science process with data, concept, motivation, thinking, knowledge and interaction
9 The Nature of Big Data Computing
9.1 The computing nature
9.2 Problem-driven, data-driven, and data-based problem driven
9.3 Beyond Turing test
9.4 Analogical mapping
9.5 Toward an open interactive computing
10 Mapping through Social Space
11 The Emergence of Cyber-Physical-Social Intelligence
11.1 Fundamental problem
11.2 Methodology
11.3 Understanding: extensible mapping from reality into mental space
11.4 Cyber-Physical-Social-Mental Computing
11.5 Laws of the complex system
12 Conclusion
13 References