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

Marijuan, Pedro, et al. Scientomics: An Emergent Perspective in Knowledge Organization. Knowledge Organization. 39/3, 2012. Aragon Institute of Health Science, Zaragoza, Spain, bioinformation researchers continue their reconception of cells and societies as most distinguished by a genetic-like communicative and informational quality that is universally recapitulated in each and every evolutionary and organismic instance.

In one of the most important conceptual changes of our times, biology has definitely abandoned its mechanistic hardcore and is advancing “fast and furious” along the informational dimension. Biology has really become an information science; and, as such, it is also inspiring new ways of thinking and new kinds of knowledge paradigms beyond those discussed during past decades. In this regard, a new “bioinformational” approach to the inter-multi-disciplinary relationships among the sciences will beproposed herein: scientomics. Biologically inspired, scientomics contemplates the multifarious interactions between scientific disciplines from the “knowledge recombination” vantage point. In their historical expansion, the sciences would have recapitulated upon collective cognitive dynamics already realized along the evolutionary expansion of living systems, mostly by means of domain recombination processes within cellular genomes, but also occurring neurally inside the “cerebral workspace” of human brains and advanced mammals. Scientomics, understood as a new research field in the domain of knowledge organization, would capture the ongoing processes of scientific expansion and recombination by means of genomic inspired software like in the new field of culturomics. (Abstract)

Marsh, Leslie. Introduction to the Special Issue: “Extended Mind”. Cognitive Systems Research. 11/4, 2010. Articles on this school of thought initiated by Andy Clark and David Chalmers in the late 1990s which is presently spawning much interest that human cognitive faculties go much beyond brains alone, and reach out into bodily, societal, artifactual, and environmental domains.

Marsili, Matteo and Yasser Roudi.. Quantifying Relevance in Learning and Inference. Physics Reports. Volume 963, 2022. We cite this entry by Abdus Salam International Centre for Theoretical Physics, Trieste, Italy and Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology physicists because it is mainly about an apparent physical propensity to seek and gain more relative, functional knowledge, whose frontier is our human sapience. In a philosophia mind, one might view a learning universe which needs to achieve its own self-observance, description and record. As the quotes allude, this ultimatel endeavor, aka bit to it information, can be seen as another instance of an inherent self-organized critical process. See also Abstraction requires breadth: a renormalisation group approach by C. Caputo, E. Seiffert and M. Marsili at 2407.01656 for more.

Learning is a distinctive feature of intelligent behaviour. Here we review recent progress on based on the notion of "relevance" which quantifies the amount of information that a dataset or representation contains. This allows us to define maximally informative samples and optimal learning machines where both exhibit critical features. This identifies samples obeying Zipf's law as the most compressed loss-less representations that are optimal in the sense of maximal relevance. (Excerpt)

Maximal relevance and criticality in efficient coding and statistical learning The above discussion implies that when samples are generated to be maximally informative, they should exhibit statistical criticality. Natural and artificial learning systems offer a further test for the criticality hypothesis. That the brain operates in a critical state has been advanced by many authors. The observation that neural networks have enhanced computational efficiency at the edge of chaos is well known. (20, 21)

Massari, Giovanni, et al. Intelligence of Small Groups. arXiv:1909.11051. Six researchers based at the University of North Texas including Bruce West offer a technical inquiry by way of many-body physics and network neuroscience to consider whether human interactive meetings could achieve a collective acumen of their own. By this approach, self-organizing processes are seen to reach phase transitions which reside at a critical poise. As a further measure, in such communicative settings 150 people seem to be an optimum size, which is then noted to confirm Robin Dunbar’s famous number.

Mayer-Kress, Gottfried and Cathleen Barczys. The Global Brain as an Emergent Structure from the Worldwide Computing Network. The Information Society. 11/1, 1995. From a neuroscience perspective, the Internet seems to be developing and functioning in a similar way as a human brain.

Invoking concepts from complexity science, we can view both the cognitive abilities of the biological brain and the problem-solving capabilities of the Global Brain as other levels of capability that emerge from this interconnected system once the system is sufficiently complex. (5) Thus a Global Brain derived from a complex information and communications network composed of people and computers would be able to sense and respond to the world outside that network as well as within that network, with abilities that would be analogous to our brain’s abilities but would surpass them. (8)

Menary, Richard, ed. The Extended Mind. Cambridge: MIT Press, 2010. An effort to gather between covers this academic surmise that mental activities somehow draw upon and exist in somatic, environmental and communal realms beyond the cerebral brain.

Michael, Miller. Cloud Computing. Indianapolis: Que Publishing, 2009. The welling revolution from desktop PCs to remote, vastly interlinked servers, as if “clouds” of computation, will much change the industry. Here is one of the first volumes to explain the lineaments and values of such peer-to-peer, distributed collaboration.

Michel, Jean-Baptiste, et al. Quantitative Analysis of Culture Using Millions of Digitized Books. Science. 331/176, 2011. An interdisciplinary Harvard University team including Martin Nowak, Erez Liberman Aiden, Steven Pinker, and Yuan Kui Shen scope out a project to read all the books at once, as if a collective corpus of worldwide humankind. And it should be noticed that the authors coin an “-omics” term - does this imply it is all somehow “genetic” in instructive kind?

We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities. (Abstract)

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.