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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Organic Universe
Earth Life Emerge
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II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

C. Earth Learns: Interactive Person/Planet, Self-Organizing, Daily Collaboratiions

Michelucci, Pietro. Human Computation and Convergence. arXiv:1503.05959. A chapter for the forthcoming Handbook of Science and Technology Convergence (Springer, 2016) by the director of the Human Computation Institute (Washington, DC), editor of the Handbook of Human Computation (Springer, 2014), with a doctorate in Cognitive Science from Indiana University. Search Dirk Helbing for a similar European take. These movements are generally meant to enhance person-device, computer plus, capabilities, as if complementary right and left brain, so that their implementation can help solve ultra complex social and environmental problems for a better, safer, sustainable world.

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis. (Abstract)

Millhouse, Tyler, et al. Frontiers in Collective Intelligence: A Workshop Report. arXiv:2112.06864. TM, Melanie Mitchell, Santa Fe Institute and Melanie Moses University of New Mexico survey papers by scholars such as Jessica Flack, Jeff Hawkins, and Cleotilde Gonzalez. The meeting motive was a growing scientific sense that life’s evolution is actually most involved with ramifying cerebral cognitive stirrings, learning, proactive behaviors. A recurrent theme was a notice that living systems at every scale and instance, as they evolve, survive, and advance, as a rule proceed to form into intelligent communal groupings.

In August of 2021, the Santa Fe Institute hosted a workshop on collective intelligence as part of its Foundations of Intelligence project. This project seeks to advance the field of artificial intelligence by promoting interdisciplinary research on the nature of intelligence. The workshop brought together computer scientists, biologists, philosophers, social scientists, and others to share their insights about how intelligence can emerge from interactions among multiple agents--whether those agents be machines, animals, or human beings. In this report, we summarize each of the talks and the subsequent discussions. We also draw out a number of key themes and identify important frontiers for future research.

Miorandi, Daniele, et al. Social Collective Intelligence. Berlin: Springer, 2014. The subtitle for this eclectic collection is Combining the Powers of Humans and Machines to Build a Smarter Society. Typical chapters are A Taxonomic Framework for Social Machines, Interface Design in Massive Open Online Courses, Computational Epidemiology, and Social Collective Awareness in Socio-Technical Urban Superorganisms.

The book focuses on Social Collective Intelligence, a term used to denote a class of socio-technical systems that combine, in a coordinated way, the strengths of humans, machines and collectives in terms of competences, knowledge and problem solving capabilities with the communication, computing and storage capabilities of advanced ICT.

Momennejad, Ida. Collective Minds: Social Network Topology Shapes Collective Cognition. Philosophical Transactions of the Royal Society B. December, 2021. In this special Emergence of Collective Knowledge and Cumulative Culture issue (see Cisek), a Microsoft Research, NYC senior scientist posts an advanced deep learning study to date of our intricate human connectivities which are then found to be distinguished by fluid web structures. These generic neural modes then serve to inform and spread as a community-wide resource. These cerebral geometries proceed to endow a working degree of collaborative intelligence. As a result, it is concluded that our local and global sapience does indeed appear to possess a true mind of its own, along with a worldwise knowledge repository. In turn for this issue, a relative capacity is notably present throughout life’s long animal stirrings. See Social Network Structure Shapes Innovation By Eleni Nisioti, et al at arXiv:2206.05060 for more by Ida M. and friends.

Human cognition is not solitary, but shaped by collective learning and memory. Unlike swarms or herds, human social networks have diverse topologies so to fit modes of collective cognition and behaviour. Here, we review research that combines network structure with psychological and neural modelling to understand how dynamic social networks shape collective cognition. First, we review graph-theoretical approaches to behavioural experiments on collective memory, belief propagation and problem solving. Then we discuss neuroimaging studies showing that human brains encode the topology of one's close and wider social network with similar neural patterns to each other. Combining network science with cognitive, neural and computational approaches well informs how social structures shape collective cognition. (Abstract)


I study how we build models of the world and use them in memory & planning. To do so, I build and test neurally plausible algorithms for learning the structure of the environment. My work shows that multi-scale predictive map representations updated via memory replay support abstraction & hierarchical planning. My approach combines behavioral experiments, fMRI, & electrophysiology with reinforcement learning, neural networks, & machine learning. (Ida M.)

Mones, Enys, et al. Universal Hierarchical Behavior of Citation Networks. Journal of Statistical Mechanics. P05023, 2014. Budapest systems biophysicists with Tamas Vicsek describe how this “accumulation of knowledge” is another exemplar of nature’s ubiquitous dynamic structuring into complex, nested, invariant nets. One might imagine, by an affinity with cerebral connectomes, that a similar worldwide brain, a noosphere, is in formation. And compare with Modeling Leadership Hierarchy in Multilevel Animal Societies by Ozogany (search) and Vicsek for a similar instance.

Many of the essential features of the evolution of scientific research are imprinted in the structure of citation networks. Connections in these networks imply information about the transfer of knowledge among papers, or in other words, edges describe the impact of papers on other publications. This inherent meaning of the edges infers that citation networks can exhibit hierarchical features, that is typical of networks based on decision-making. In this paper, we investigate the hierarchical structure of citation networks consisting of papers in the same field. We find that the majority of the networks follow a universal trend towards a highly hierarchical state, and i) the various fields display differences only concerning their phase in life (distance from the "birth" of a field) or ii) the characteristic time according to which they are approaching the stationary state. (Abstract)

Morales, Alfredo, et al. Global Patterns of Synchronization in Human Communications. Journal of the Royal Society Interface. Online March, 2017. New England Complex Systems Institute (Morales, Vaibhav Vavilala, Yaneer Bar-Yam) and Universidad Politecnica de Madrid (Rosa Benito) systems scientists use the latest cyber-analysis techniques to distill intrinsic complex coordinations by way of pervasive social media. The second quote is from a NECSI email about a talk in Boston with cites this work.

Social media are transforming global communication and coordination and provide unprecedented opportunities for studying socio-technical domains. Here we study global dynamical patterns of communication on Twitter across many scales. Underlying the observed patterns is both the diurnal rotation of the earth, day and night, and the synchrony required for contingency of actions between individuals. We find that urban areas show a cyclic contraction and expansion that resembles heartbeats linked to social rather than natural cycles. Different urban areas have characteristic signatures of daily collective activities. We show that the differences detected are consistent with a new emergent global synchrony that couples behavior in distant regions across the world. Although local synchrony is the major force that shapes the collective behavior in cities, a larger-scale synchronization is beginning to occur. (Abstract)

On March 30, 2017 there will be a special presentation by senior postdoctoral researcher Alfredo J. Morales. Titled "The Science of Human Collective Behavior Using Twitter," it will cover structural and dynamical patterns of social systems revealed through the analysis of data from social media like Twitter. Morales uses complex systems science to retrieve important information from Big Data produced by a globally-tweeting civilization. Tweets can reveal unstructured patterns of social behavior across multiple scales, ranging from the daily routines of individuals up to the collective pulse of activities within cities and global networks of communication and synchronicities that can span hemispheres. (NECSI)

Muller-Scholer, Christian, et al, eds. Organic and Pervasive Computing. Berlin: Springer, 2004. Computer and system architectures are becoming more animate in kind as they gain capabilities of adaptive self-organization.

Organic Computing investigates the design and implementation of self-managing systems that are self-configuring, self-optimizing, self-healing, self-protecting, context aware and anticipatory. (Preface)

Muthukrishna, Michael and Joseph Henrich. Innovation in the Collective Brain. Philosophical Transactions of the Royal Society B. Vol.371/Iss.1690, 2016. Harvard University, Human Evolutionary Biology scholars (MM now at London School of Economics) advance a strong argument that human small and larger assemblies and collaborations could actually attain their own intelligent, information processing creativity. While this ought to be obvious (search David Christian and Matt Ridley, e.g.), our cultural mindset of no abiding, procreative reality by itself blocks this view. This sourcebook website quite simply based on this premise that a nascent humankinder is now learning and discovering on her/his sapiensphere own. See also Henrich’s 2016 work The Secret of Our Success for much more.

Innovation is often assumed to be the work of a talented few, whose products are passed on to the masses. Here, we argue that innovations are instead an emergent property of our species' cultural learning abilities, applied within our societies and social networks. Our societies and social networks act as collective brains. We outline how many human brains, which evolved primarily for the acquisition of culture, together beget a collective brain. Within these collective brains, the three main sources of innovation are serendipity, recombination and incremental improvement. We argue that rates of innovation are heavily influenced by (i) sociality, (ii) transmission fidelity, and (iii) cultural variance. For example, we provide preliminary evidence that transmission efficiency is affected by sociality—languages with more speakers are more efficient. (Abstract)

Newman, Harvey, et al. The UltraLight Project: The Network as an Integrated and Managed Resource for Data-Intensive Science. Computing in Science & Engineering. November/December, 2005. The international high energy physics community is assembling an interlinked computational grid to process and share vast data-sets. With similar efforts in other fields such as climate change, as Albert Barabasi, et al (Science. 308/639, 2005) observes, the human scientific endeavor is in transition to a new global cerebral capacity.

Nolte, David. Mind at Light Speed: A New Kind of Intelligence. New York: The Free Press, 2001. The future of optical, holographic, and quantum computers and worldwide communications presage an immense expansion of human mental abilities.

Norgaard, Richard. Learning and Knowing Collectively. Ecological Economics. 49/2, 2004. The University of California at Berkeley environmental scientist contends that the challenges of climate change and many other systemic issues will require a new mode of intentional, global collaboration. The many disciplines and specialties can no longer proceed in isolation. A common epistemology, language and modeling approach is vital so we can learn and act in effective concert.

Norgaard, Richard and Paul Baer. Collectively Seeing Complex Systems. BioScience. 55/11, 2005. In a Special Roundtable Section with several articles, Norgaard, University of California at Berkeley and Baer, Stanford University, continue their efforts, in collaboration with the Millennium Ecosystem Assessment team, to implement a new understanding of science and policy as a collective seeing, learning, and judgment, a distributed cognitive network. Only by such interdisciplinary social syntheses can ultra complex issues as global climate change be addressed.

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