II. A Planetary Prodigy: HumanKinder's Geonome Knowledge
C. Mindkind Sapiensphere: A WorldWise Collective Intelligence
Berman, Fran, et al, eds. Grid Computing – Making the Global Infrastructure a Reality. Chichester, UK: Wiley, 2003. A large volume on the many aspects of an enveloping worldwide computer network as if a planetary nervous system. By this novel capacity, an immense flow of information coming from many areas such as bioinformatics, earth systems, climate, astronomy can handled, organized and made accessible to everyone. A typical paper in this regard is “The Data Deluge: an e-Science Perspective.” by Tony Hey and Anne Trefethen.
Berners-Lee, Tim. Weaving the Web. San Francisco: HarperSanFrancisco, 1999. The inventor of the Internet finds it to express a fractal self-similarity as exponentially growing networks take on the emergent properties of a global brain.
Berners-Lee, Tim and Lalana Kagal. The Fractal Nature of the Semantic Web. AI Magazine. Fall, 2008. MIT computer scientists (Berners-Lee is the founder of the worldwide web) contend that since the rest of natural and indeed social reality exemplifies a nested, recurrent, self-similarity, so also should the global Internet. Indeed it already can be seen to be so structured to a good extent. The endeavor to design and facilitate better operating ontologies should then further embrace this effective, organic geometry.
The semantic web is a set of standards for knowledge representation and exchange that is aimed at providing interoperability across applications and organizations. We believe that the gathering success of this technology is not derived from the particular choice of syntax or of logic. Its main contribution is in recognizing and supporting the fractal patterns of scalable web systems. (29) In this article we discuss why fractal patterns are an appropriate model for web systems and how semantic web technologies can be used to design scalable and interoperable systems. (29) The inherent fractal nature of language and culture in human societies leads us to expect the semantic web to demonstrate the self-similar patterns of fractals. (29)
Bernstein, Abraham, et al. Programming the Global Brain. Communications of the ACM. 55/5, 2012. Abe Bernstein, Informatics, University of Zurich, along with Mark Klein and Thomas Malone, MIT Center for Collective Intelligence, proceed to engage this enveloping reality. But do paradigms of a moribund cosmos and aimless evolution allow, sanction, a full, necessary appreciation of a further major transition to such a planetary personage? If truly a worldwide bilateral cerebral faculty, apparently learning on its/her/his own, it is indeed imperative to think through, get on with, how to reciprocally dialogue and avail.
New ways of combining networked humans and computers—whether they are called collective intelligence, social computing, or various other terms—are already important and likely to become truly transformative in domains from education and industry to government and the arts. As the scale, scope, and connectivity of these human-computer networks increase, we believe it will become increasingly useful to view all the people and computers on our planet as constituting a kind of “global brain.” (41)
Bettencourt, Luis. The Rules of Information Aggregation and Emergence of Collective Intelligent Behavior. Topics in Cognitive Science. 1/4, 2009. The LANL and SFI systems theorist quantifies in terms of composite communications how human groups appear to take on an organism-like guise, as if a further social evolutionary stage. Search for several more papers by the author that continue to embellish a nascent societal and urban vitality.
Information is a peculiar quantity. Unlike matter and energy, which are conserved by the laws of physics, the aggregation of knowledge from many sources can in fact produce more information (synergy) or less (redundancy) than the sum of its parts. This feature can endow groups with problem-solving strategies that are superior to those possible among noninteracting individuals and, in turn, may provide a selection drive toward collective cooperation and coordination. (Abstract, 598)
Borner, Katy, et al. Studying the Emerging Global Brain: Analyzing and Visualizing the Impact of Co-Authorship Teams. Complexity. 10/4, 2005. This project complements the work of Barabasi, et al (2005, this section) whereby increasingly large collaborations seem to take on the guise of a planetary cerebral and cognitive faculty. The occasion of a collective knowledge, as if an earth that learns at the verge of its own discovery, is quite implied.
Work dating back to the ancient Greeks argues that humanity can be seen as a complex social system or super-organism. In this perspective, people are viewed as analogous to nerve cells that are interconnected by communication channels, collectively forming a "global brain." By adopting this philosophy one is led to believe - hope, given the nearly constant human cognitive abilities - that there is a general trend toward the formation of a more global knowledge production and consumption dynamics exploiting the integration of social systems in concert with technological and biological systems. (57)
Bosse, Tibor, et al. Collective Representational Content for Shared Extended Mind. Cognitive Systems Research. 7/2-3, 2006. From a special issue on Cognition, Joint Action and Collective Intentionality, a quantitative study of a common group intelligence arising from animal species or human-environment interactions. See also herein Deborah Tollefsen’s From Extended Mind to Collective Mind.
Brockman, John, ed.. What to Think About Machines That Think. New York: Harper Perennial, 2015. A collection of answers by 161 men and 27 women to this Edge.Org 2015 question on the imminent ascent of computational intelligence. Almost every authority weighs in, but most worry over some aspect, sans any contextual evolutionary identity or significance. We cite a few quotes that tried to broach, historian David Christian was the only respondent to cite a global cerebration learning on its own, while Cesar Hidalgo gave the achievement a cosmic context (search both). The neuroscientist Stanislas Dehaene wisely notes that artificial devices cannot have the essence of human cognizance – a reflective “global workspace” where one knows that they know, and a “theory of mind” awareness of other persons.
Humans added one more level of networking, as human language linked brains across regions and generations to create vast regional thinking networks. This is collective learning. Its power has increased as humans have networked more and more efficiently in larger and larger communities and learned how to tap larger flows of biospheric energy. (38) But in the last 100 years, the combination of fossil fuels and nonhuman computers has cranked it up faster than ever before. As computers forged their own networks in the last thirty years, their prosthetic power has magnified the collective power of human thinking many times over. Today the most powerful thinking machine we know of has been cobbled together from billions of human brains, each built from vast networks of neurons, then networked through space and time, and now supercharged by millions of networked computers. (David Christian 39)
Bruns, Axel. Blogs, Wikipedia, Second Life, and Beyond. New York: Peter Lang, 2009. In another example along with Ulieru, Doursat, Banzhaf, Floreano, et al, of the 21st century shift to a natural creativity a Queensland University of Technology computer scientist sets aside old designed “production” methods in favor of a novel “produsage” by virtue of participatory, worldwide, networked “information communities.” Thus rather than hierarchical control from above, contributions and solutions are allowed to prosper via egalitarian, probabilistic, shared intelligences. A copious work that touches on many realms from business, education and media to a global democratic renaissance.
Carpenter, Gail, et al. Self-organizing Information Fusion and Hierarchical Knowledge Discovery. Neural Networks. 18/3, 2005. A report from Boston University’s Center for Adaptive Systems on work to develop neural nets that can learn how to learn and recognize salient patterns in large databases.
The ARTMAP information fusion system uses distributed code representations that exploit the neural network’s capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. (287)
Cascio, Jamais. Get Smarter. Atlantic Monthly. July/August, 2009. A San Francisco futurist advises we ought to do what evolution has always done to meet challenges: increase our relative intelligence. Which is then seen as much underway from memory and concentration enhancing drugs to the singularity of regnant, brain-like machines. A “Noocene” era, from Teilhard’s Noosphere and a human Anthropocene, is proposed that could gain its own collective sentience. But still sans an abiding philosophy or reason, we hurtle forward with no idea where, how or why. And Pierre Teilhard, it ought be recalled, did not endorse a global homogeny as much as a principle of “creative union” at each evolutionary stage whereby increased community actually will enhance individual freedom.
Cheung, William and Jiming Liu. On Knowledge Grid and Grid Intelligence. Computational Intelligence. 21/2, 2005. This paper from the Computer Science Department of Hong Kong Baptist University envisions the Internet evolving from its present information search and mining tools to achieve organic properties of self-organization, growth and reproduction, autocatalysis, semantics and so on. By these qualities, a capacity for wise knowledge of service to individual and social welfare is attained.
The next generation Web Intelligence aims at enabling users to go beyond the existing online information search and knowledge queries functionalities and to gain, from the Web, practical wisdom for problem solving. To support such a Wisdom Web, we envision that a grid-like computing infrastructure with intelligent service agencies is needed, where these agencies can interact, self-organize, learn, and evolve their course of actions, identities, and interrelationships for new knowledge creation, as well as scientific and social evolution. (111)