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

Olsen, Gary, et al, eds. Scientific Collaboration on the Internet. Cambridge: MIT Press, 2008. Originating from the University of Michigan School of Information, an attempt to scope out science’s shift to such a worldwide electronic domain. Six parts with many authors discuss the 'collaboratory' vision, distributed research, the physical sciences, biological and health sciences, earth and environmental studies, and the developing world.

Omicini, Andrea, et al. The Multidisciplinary Patterns of Interaction from Sciences to Computer Science. Goldin, Dina, et al, eds. Interactive Computation. Berlin: Springer, 2006. A universality of self-organization via entities-in-relation gives rise to a globally informative intelligence. Such a dynamically creative nature can then provide an exemplary model for computational systems.

No matter if we are modeling the behavior of a human organization, the life of an intricate ecosystem, or the dynamics of a huge market-place, we can expect to find some repeated patterns, some shared schemes, some common laws that makes all these systems look similar when observed at the right level of abstraction. (396)

Palermos, S. Orestis. The Dynamics of Group Cognition. Minds & Machines. 26/4, 2016. A University of Edinburgh scholar who works at the intersection of epistemology, philosophy of mind, and cognitive science avails dynamic systems theory, as per the Abstract, to learn how human assemblies can actually take on a mind of their own. By these insights, in support of this website, an evolutionary emergence of local and global communicative groupings seem to initiate a new phase of collective knowledge above and beyond individual opinion, which we desperately need. The author is also a member of the “Extended Knowledge Project” at Edinburgh, see second quote.

The aim of this paper is to demonstrate that the postulation of irreducible, distributed cognitive systems (group minds) is necessary for the successful explanatory practice of cognitive science and sociology. Towards this end, and with an eye specifically on the phenomenon of distributed cognition, the debate over reductionism versus emergence is examined from the perspective of Dynamical Systems Theory (DST). Firstly, DST is particularly popular amongst cognitive scientists who work on modelling collective behaviors. Secondly, DST can deliver two distinct arguments in support of the claim that the presence of mutual interactions between group members necessitates the postulation of the corresponding group entity. Thirdly, DST can also provide a succinct understanding of the way group entities exert downward causation on their individual members. The outcome is a naturalist account of the emergent, and thereby irreducible, nature of distributed cognitive systems that avoids the reductionists’ threat of epiphenomenalism, while being well in line with materialism. (Abstract)

Extended Knowledge is a three-year project within Edinburgh’s department of Philosophy that studies the ramifications of the extended and distributed cognition hypotheses for epistemology: If cognition can extend beyond our brains and bodies to the artefacts we employ or–further–be distributed among several different individuals at the same time, then how (and to what extent) should we reconceive our thinking about knowledge and other related cognitive processes, such as learning and justification, which have traditionally been approached against an ‘intracranial’, individualist background? (Website excerpt)

Park, Kihong and Walter Willinger, eds. The Internet as a Large-Scale Complex System. Oxford: Oxford University Press, 2005. Proceedings of a Santa Fe Institute conference to consider the network properties and power-law connectivities of the worldwide web. For example, Computation in the Wild by Stephanie Forrest, et al and The Bio-Networking Architecture by Tatsuya Suda, et al describe natural evolutionary qualities which can serve as a model for its increasing viability and cognition. However Willinger, et al have recently taken issue with the wholesale apply of 'scale-free networks' in this regard in "Mathematics and the Internet" in Notices of the AMS for May 2009.

The Bio-Networking Architecture is inspired by the observation that the biological world has already developed mechanisms that are necessary for future network requirements such as self-organization, scalability, adaptation and evolution, security, and survivability. (Suda 251)

Pentland, Alex. On the Collective Nature of Human Intelligence. Adaptive Behavior. 15/2, 2007. An MIT Media Lab professor contends that individuals do not exist in lone isolation, but are enmeshed and engaged in myriad fluid social networks. He goes on to detail novel technologies that could enhance such distributed cognition.

Pfeifer, Rolf and John Bongard. How the Body Shapes the Way We Think. Cambridge: MIT Press, 2007. Actually a significant contribution to the field of artificial intelligence with regard to the design of robots based on evolutionary principles of self-organization, variation and selection. Sure, the brain can influence and control its soma but this vehicle will in turn in momentary, ontological and phylogenetic time frames constrain and enable reflective and anticipatory thought. As such complex systems are allowed to evolve, a responsive order can be seen to emerge. As agents thusly interact they serve to generate an emergent modular-based collective intelligence from cells to societies.

Pink, Daniel. The Book Stops Here. Wired. March, 2005. With an engagingly edgy style, the story of Wikipedia, the free self-organizing, repairing, policing, online encyclopedia that anyone can contribute to or edit. Which surely looks like a global cerebral learning enterprise. How can we then imagine all these efforts achieving their own composite, salutary knowledge?

Provencal, Yvon. The Mind of Society. New York: Gordon & Breach, 1998. An innovative study based on the work of Marvin Minsky and Pierre Teilhard de Chardin of human society as a superorganism with its own physiology, cerebration and self-consciousness. In this view, a close agreement is recorded in Piaget’s nomenclature between a child’s cognitive experience and the education of humanity.

The conceptual correspondence between cerebral mechanisms of perception and the collective mechanisms of scientific discovery can be seen in striking detail. (40) The analogical method used here requires that we consider global human society, which is undergoing rapid change, as a self-organizing structure similar to a young child’s brain. (49)

Qian, Ning and Richard Lipkin. A Learning Style Theory of Understanding Autistic Behaviors. Frontiers in Human Neuroscience. 5/Art. 77, 2011. By a similar approach to Ari Rosenberg (2015, below), a Columbia University neuroscientist and a biophysicist open with a listing of some 18 features from prior studies such as words without grammar, or trees but no forest. As the Abstract explains, a common denominator may be a particulate, “lookup table” focus, absent abilities to join or “interpolate” into patterns and regularities. Which again appears as a (male) left brain emphasis without a holistic right complement. As noted in Rosenberg, these traits or deficits could easily be seen to apply to national societies beset by internecine, senseless violence sans any common, familial relation. Autistic America cannot perceive that the two political parties, bent on destroying each other, are reciprocal archetypes.

Understanding autism’s ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup table (LUT) learning, which aims to store experiences precisely, to interpolation (INT) learning, which focuses on extracting underlying statistical structure (regularities) from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low- and high-dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization. The LUT style poorly compresses information, resulting in inefficiency, sensory overload, restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction, impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. (Abstract)

Unlike previous efforts that focus on small subsets of autistic behaviors, we propose a theory that appears to account for a wide range of autistic behaviors. We hypothesize that autistic brains are biased toward LUT learning, which aims to store training examples precisely without extracting their underlying statistical structure or regularities, whereas typical brains are biased toward INT learning, which does not insist on representing training examples precisely but focuses instead on discovering their underlying regularities for generalization. (12)

Rasmussen, Steen, et al. Collective Intelligence of the Artificial Life Community. Artificial Life. 9/2, 2003. A report on an exercise at their August 2000 conference to collectively access the status and future of this endeavor.

Human society evolved from small, separated hunting tribes to a huge, globally integrated society….When the full diversity of the society’s intellectual dynamics is combined with the Internet’s ability to quickly and accurately link information, large groups can quickly and efficiently pool their resources and coherently analyze complexes that were very difficult to cope with in other ways. (209)

Reagle, Joseph. Good Faith Collaboration: The Culture of Wikipedia. Cambridge: MIT Press, 2011. The author is a Berkman Center for Internet and Society, Harvard University, postdoctoral fellow, well placed to wax on this blooming appearance of a self-organizing worldwide repository informed by any millions of neuron-like contributors. A notable early chapter is The Pursuit of the Universal Encyclopedia which retraces an historical passion to gather in one place, in some venue, the totality of human knowledge. In so doing, a continuity can be discerned from 1930s H. G. Wells to 2000s Jimmy Wales, of an enveloping “World Brain” that may just now attain a consummate, palliative wisdom.

Ridley, Matt. The Rational Optimist. New York: HarperCollins, 2010. From a 21st century vantage, a British science writer looks back over the millennia to discern a broad, pervasive improvement in the human condition. While admitting its tragic trajectory, this real advance is in need of exposition today to counter an apocalyptic gloom in the media. Fraught as we are with weapons, stupidity, and greed, by many measures, a consummate horizon ought to be appreciated that bodes for a earthwide eden, if we so choose. Ridley goes on to attribute this progression and vista to a ramifying, collective brain that overcomes individual animus in favor of social well being. (A similar view would be Nonzero by Robert Wright).

[Prev Pages]   Previous   | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21  Next  [More Pages]