II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Twintelligent Gaiable Knowledge

C. Earthica Learns as a Symbiotic Person/Planet, Collaborative Ecosmo Sapience

Zhong, Nong, et al, eds. Web Intelligence Meets Brain Informatics. Berlin: Springer, 2007. With Weibo Gong above, considerations of the intensifying convergence between neuroscience and the worldwide computer web, which, as we realize how truly like a brain it is, such attributes as listed below ought to be further enhanced.

Brain Informatics (BI) is an emerging interdisciplinary field to study human information processing mechanism systematically from both macro and micro points of view by cooperatively using experimental, computational, cognitive neuroscience and advanced WI (Web Intelligence) centric information technology. It attempts to understand human intelligence in depth, towards a holistic view at a long-term, global vision to understand the principles and mechanisms of human information processing system, with respect to functions from perception to thinking, such as multi-perception, attention, memory, language, computation, heuristic search, reasoning, planning, decision-making, problem-solving, learning, discovery and creativity. (3)

Zhuge, Hai. Discovery of Knowledge Flow in Science. Communications of the ACM. 49/5, 2006. Insightful ways to implement the worldwide e-science knowledge grid so as to enhance its productivity and in turn improve our societies. One then wonders if such “autonomous” cerebral activity on a planetary scale might achieve its own salutary learning capacity.

The knowledge flow network implicit in the citation network consists of knowledge flows between nodes (scientists) that process knowledge, including reasoning, fusing, generalizing, inventing, and problem solving, by authors and co-authors. (103) Exploring the universe and human society are great challenges of 21st century science. This article explores the dynamic nature of knowledge, the power to promote and influence the development of human society, and the future interconnection environment. It describes an important approach to automatically discovering knowledge flow networks within scientific documents and activities. Such networks embody an autonomous knowledge grid, which supports individual and cooperative scientific research, helps investigate the evolution of knowledge and disciplines, and assists in planning for scientific research development. (107)

Zhuge, Hai. Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure. arXiv:1507.06500. A leading Chinese computer scientist, see bio below, continues his contributions to the salutary advancement of planetary and personal Internet design, capability, and access. Of interest are intimations of a New Paradigm of Science by way of these instant, collaborative worldwide endeavors, an e-science which is really an emergent human sapiensphere. See also his latest November 2016 book Multi-Dimensional Summarization in Cyber-Physical Society from Morgan-Kaufmann Publishers. Some years ago Professor Zhuge and I had a good exchanged emails which led to my review of his earlier work The Knowledge Grid (search) for a Chinese journal. See also The Complex Link at 1805.00434 for a later edition.

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing. (Abstract)

Hai Zhuge has appointments at Nanjing University of Posts and Telecommunications, Institute of Computing Technology in Chinese Academy of Sciences, and Aston University. Professor Zhuge is a recognized pioneer and leader Cyber-Physical Society research and Knowledge Grid research. His research in this area has received over 4,000 citations. He is a Distinguished Scientist of ACM, and a Fellow of British Computer Society, an Associate Editor of IEEE Intelligent Systems and Steering the International Conference on Semantics, Knowledge and Grids. He previously authored The Knowledge Grid -- Toward Cyber-Physical Society 2E, World Scientific Publishing Co (2012).

Zhuge, Hai. The Knowledge Grid. Singapore: World Scientific, 2004. The author is a professor of computer science at the Chinese Academy of Sciences and founder of the Chinese Knowledge Grid Research Group. This frontier volume explores the epistemologies, ontologies, semantics, systems principles, scale-free network properties, and so on of national and worldwide knowledge resources. By Knowledge Grid is meant an intelligent and sustainable interconnected network that allows and empowers people and their computers to access, publish, share and manage information resources. (An introductory article by Zhuge is The Future Interconnection Environment in Computer, April 2005.) A good example is Zhuge's work on creating a virtual model of the recent SARS epidemic, reported in the Planetary Physiology section. An extended review of this important work is posted in the Recent Writings section.

Intelligence, Grid, peer-to-peer and environment represent humanity's four aspirations for the future working and living environment. The intelligence reflects humanity's pursuit of recognizing themselves and the society. The Grid reflects humanity's pursuit of optimization and system. The peer-to-peer reflects humanity's pursuit of freedom and equality. The environment reflects humanity's pursuit of understanding of nature and its harmony. (27)

Zhuge, Hai and Xiaoqing Shi. Fighting Epidemics in the Information and Knowledge Age. Computer. October, 2003. A practical example of applying intelligent information processing to combat outbreaks such as the SARS virus.

Zhuge, Hai and Xiaoqing Shi. Toward the Eco-Grid: A Harmoniously Evolved Interconnection Environment. Communications of the ACM. 47/9, 2004. Further insights on recasting the Internet into a more organic, evolutionary and dynamically responsive noosphere.

An Eco-Grid is an open worldwide interconnection environment reflecting the characteristics of natural ecological environments. (80)

Zonker, Johannes, et al.. Insights into drivers of mobility and cultural dynamics of African hunter–gatherers over the past 120 000 years. Royal Society Open Science. November, 2023. While the presence and benefits for any group of an accumulating social lore has been known for some time, this current entry by JZ and Nataša Djurdjevac Conrad, Zuse Institute, Berlin, and Cecilia Padilla-Iglesias, University of Zurich can provide the first agent-based, networked, mathematic quantification, which also includes a mobility factor. The paper begins by laying out this scientific phase and then applies it to an actual spatial/temporal tribal exemplar. A third section concludes as a proven fact, that the long span of human history is indeed distinguished and motivated by an expansive. salutary knowledge repository. As the basis of this resource website, along with concepts such as a major individuality transition and global brain knowsphere, a worldwise Earthumanity cumulative culture could be another occasion.

• Humans have a unique capacity to innovate, transmit and rely on a complex, cumulative culture for survival. While prior work has popof the sum entirety ulations with regard to persistence, diversity and information, they have not yet explained their occurrence and distribution over an evolutionary trajectory. Here, we develop a spatial-temporal agent-based model to include environmentally driven changes in the size and dynamics of hunter–gatherer groups as they may affect the form, transmission and accumulation of a relative knowledge content. We validate our model using empirical data from Central Africa spanning 120 000 years. Our work can therefore offer important insights into the role of a foraging lifestyle on the evolution of cumulative culture. (Abstract)
•
Despite being less genetically diverse than all our Great Ape relatives, humans are able to inhabit every terrestrial habitat of the planet. This unique adaptive ability has been largely explained by our capacity to rely on cumulative culture for survival. Culture is a second inheritance system that parallels and interacts with the genetic system, generating most of human population diversity. Cultural variation and innovations accumulate in populations throughout time, allowing for complex cultural adaptations to evolve. (1)

Zurn, Perry and Danielle Bassett. Network Architectures Supporting Learnability. Philosophical Transactions of the Royal Society B. February, 2020. In this special Unifying the Essential Concepts of Biological Networks issue, American University, Washington and University of Pennsylvania neuroscientists enter an innovative survey which joins a universe context from its physical, energetic basis with our manifest human neural net phase so as to trace a central essence and pathway of intelligent personal and societal learning and active knowledge. The paper cites the self-similarity of nested hierarchies, modularity, scalar transitions, shared information, metabolism, and more by which to achieve better represented models of this animate evolution. At each instance and stage, the relational, communicative topologies as they join pieces (particles, neurons, creatures) are seen to have a primary significance.

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the informational structure of the knowledge network and the architecture of a computational brain that encodes and processes it. That is, learning is reliant on integrated networks at both epistemic and computational levels, or the conceptual and neural. Here we discuss emerging work on network constraints on the learnability of relational knowledge, and statistical physics principles of thermodynamics and information theory to offer an explanatory model. We highlight similarities between the learnability of relational networks and the physical constraints on the development of interconnected patterns in neural systems, both leading to hierarchically modular networks. Finally, we broach a unified approach to hierarchies and levels in biological networks by proposing epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. (Abstract excerpt)

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