II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

2. Collective Local/ Global Brain Intelligences

Garcia, David, et al. The Psychology of Collectives. Perspectives on Psychological Science. December, 2023. Henrik Olsson, University of Konstanz and DG, Mirta Galesic, Complexity Science Hub, Vienna introduce a special issue on this current subject. Typical papers are The Emerging Science of Interacting Minds by Thalia Wheatley, et al, Group formation and the evolution of human social organization by C. DeDreu, et al, The spread of beliefs in modularized communities by R. Goldstone, et al, and Polarization and the psychology of collectives by S. Levin and E. Weber.

Ha, David and Yujin Tang. Collective Intelligence for Deep Learning. Collective Intelligence. September, 2022. Google Brain, Tokyo software engineers provide a timely, wide-ranging survey from nature’s persistent tendency for animal groupings to become smarter through communicative interactions. In respect, the paper is a good example of 2022 worldwise abilities, as not much earlier, to quantify and recognize how significantly prevalent this vital learning process actually is. Bu our frontier phase, as if a spiral ascent to an Earthumanity involves deep neural nets, AI methods, agent behaviors, pattern notice, altogether a planetary learning endeavor.

In this review, we will provide a historical context of neural network research’s involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its capabilities. We hope this review can serve as a bridge between the complex systems and deep learning communities.

Levy, Pierre. Semantic Computing with IEML. Collective Intelligence. 2/4, 2023. As per the bio below, the author has been a visionary advocate since the 1990s of an emergent planetary brain-like Internet faculty, its composite knowledge content and now herein viable ways to achieve an effective linguistic discourse. Into these 2020s, as the quotes say, he has composed a relative lingua franca suitable for our active Earthuman cross- conversations and postings. To his correct credit, Pierre Levy has stayed on message over 30 years with these latest cyber-literacy articulations.

This paper presents IEML, Information Economy MetaLanguage, a constructed language with the expressive power of a natural, computable language and computable semantics. In order to compose this formation in a mathematical way, including its paradigmatic dimension, I have coded linguistic semantics with IEML. This article introduces its 3000-word dictionary, formal grammar, and its integrated tools for building semantic graphs. In regard, IEML could become a vector for a fluid calculation and communication of meaning and advance the progress of collective intelligence, artificial intelligence, and digital humanities. (Abstract)

How can we enhance collective intelligence at a time when it emerges largely from a process of cross-communication? To address, I approached it from a librarian’s perspective, considering how to organize memory in a manner that would maximize three key factors: the possibilities for original data description, effective translation and conveyance, and automatic information processing. To make progress, I required a next-generation documentary language designed to function as a metadata system for the global digital repository. (1)

Conclusion Independently from a recent generation of neuro-symbolic AI models, IEML is pioneering a new kind of semantic literacy that supports several research directions. One concerns the design of information systems in which the use of data banks by this mathematical language would allow a large number of analytic (arising from the construction of the concepts themselves), and synthetic a priori (arising from the relations between concepts) truths. An illumination of the cognitive processes supported by a common informative semantics could well aid progress towards a more reflexive collective intelligence. (25)

Pierre Lévy is a French philosopher, cultural theorist and media scholar who was a professor at the University of Ottawa. He specializes in the understanding of the cognitive implications of digital technologies and human collective intelligence. He introduced the phrase in his 1994 book Collective Intelligence: Mankind's Emerging World in Cyberspace.

McClleland, James. Capturing Advanced Human Cognitive Abilities with Deep Neural Networks. Trends in Cognitive Sciences. 26/12, 2022. . At 73 years. the pioneer cofounder with David Rumelhart of 1980s connectionism continues his project by integrations with computational AI methods. A big difference will be a more “goal directed” orientation going forward. We add that by this vista, science can be seen to spiral from individuals to a 2020s global sapiensphere going on its cognizant self. As AI and CI may join forces, might a proper Earthificial Intelligence be appropriate, as a prior section seeks to do?

How can artificial neural networks capture the advanced cognitive abilities of pioneering scientists? I suggest they must learn to exploit human-invented tools of thought and human-like ways of using them, and must engage in explicit goal-directed problem solving as exemplified in the activities of scientists and mathematicians and taught in advanced educational settings.

McMillen, Patrick and Michael Levin. Collective intelligence: A unifying concept for integrating biology across scales and substrates. Communications Biology. 7/378, 2024. Tufts University social scholars (search ML) begin by noting that a robust propensity across life’s widely stratified evolution to form recurrent viable creaturely groupings with their own cognitive capabilities has by now been well verified. A conclusion is then stated that a natural universality of nested collaborative assemblies at every level and phase of fauna and fauna has been established. See also How Is Flocking Like Computing? By Stephen Strogatz and Iain Cousin in Quanta. (March 29, 2024) for another thorough affirmation. As these studies converge and reinforce they presage a real discovery of an independent, me + We = US, family-like universality. With this in place, the authors propose that this iconic finding can serve as a guide for novel intentional and synthetic coherences.

A defining feature of biology is the use of a multiscale architecture ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. However, biology is not only nested structurally, but also functionally such as physiological, morphological, and behavioral state spaces. Percolating from one level to a higher organization requires multiple components to work together. Here we survey scales to show the ability of cellular material to make decisions that implement homeodynamic cooperation with collective intelligence at the cell, tissue, and whole-organism levels. We then briefly outline the implications of this approach for regenerative medicine and synthetic bioengineering. (Excerpt)
.Alan Turing was prescient in studying both intelligence and the chemical basis of self-organization for they have much in common with a non-neural collective intelligence of morphogenesis. Neuroscience can benefit from a glimpse into e the evolutionary past of the brain’s remarkable capabilities, while developmental biology and bioengineering can borrow the practical and conceptual tools of neuroscience which is likely to be about much more basic principlesthan the function of classical neurons. Taken together, collective intelligence is an extremely exciting and interdisciplinary emerging field that spans from the most fundamental philosophical problems of the parts-whole relationship to advancing fundamental and applied discovery in a number of important subfields. (12)

Mengers, Vito, et al. Leveraging Uncertainty in Collective Opinion Dynamics with Heterogeneity. arXiv:2402.03354. We note this entry by Technische Universit at Berlin, Humboldt Universit and University of Konstanz system scholars including Pawel Romanczuk in this section for its broad theoretic recognition of how prevalent a consistent tendency to move toward and form viable groupings across natural and social occasions actually is

Natural and artificial collectives exhibit complex, heterogeneous behaviors across its dimensions. We investigate two effects of such collective opinion dynamics: the agents' prior information and network neighbors. To study these, we introduce uncertainty as an additional aspect.. By quantifying this for each agent, we can adaptively weigh their individual against social information. These opportunities for improved performance and observability suggest the importance of uncertainty both for the study of natural and the design of artificial heterogeneous systems. (Excerpt)

Individuals in collectives are exposed to a flow of incoming information from their neighbors, be it in a school of fish, a network of sensors, a swarm of robots, or a group of humans. Models of opinion dynamics not only suggest a mechanism for how individuals incorporate this information but also provide a way to design equivalent artificial systems. The inter-individual variations of agents can manifest in behavioral traits, position in the network, information access, or self-confidence. (1)

Mulgan, Geoff. Big Mind. How Collective Intelligence Can Change Our World. Princeton: Princeton University Press, 2018. A University College London professor of social innovation provides an early survey of the advent and avail of this so far unappreciated cognitive process.

A new field of collective intelligence has emerged in recent years, prompted by digital technologies that make it possible to think at large scale. This "bigger mind"―human and machine capabilities working together―could potentially solve the great challenges of our time. Gathering insights from the latest work on data, web platforms, and artificial intelligence, Big Mind reveals how the power of collective intelligence could help organizations and societies to survive and thrive.

Rabb, Nathaniel and Steven Sloman. Radical Collective Intelligence and the Reimagining of Cognitive Science.. Topics in Cognitive Science.. 16/2, 2024. As the quote says, MIT and Brown University introduce a special issue along with a novel perspective for the endeavor. Among the papers we note What Makes Us Smart? by Joseph Henrich and Michael Muthukrishna (see below) and The Wisdom of the Crowd is not a Forgone Conclusion. Effects of Self-Selection on (Collaborative) Knowledge Construction by Marie-Christin Krebs, et al.

Our special issue How Minds Work: The Collective in the Individual proposes a “radical CI” as a new paradigm for for this emergent collaborative facility. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. This concept clarifies how the volume's contributions either rethink long-studied cognitive processes (memory, metacognition, reasoning) or contemplate how radical CI can arise.

Human creativity does not solely rely on our individual cognitive abilities, but instead emerges from the recombination of ideas, practices, and approaches that result from social interactions and idea exchanges in large, diverse populations. These population-level processes, which operate over generations, influence not only on our tools, technologies, and languages, but also key aspects of our culturally-evolved cognition, such as our epistemologies and ontologies. (JH & MM)

Rajaram, Supama. Collective Memory and the Individual Mind.. Trends in Cognitive Sciences. 26/12, 2022. A SUNY Stony Brook psychologist provides a good contrast between the once and future quarter century spans as broadly aligned with distinctly personal and global phases of our ascendant cerebral Earthumanity.

How does social transmission of information shape individual and collective memory? Taking a cognitive-experimental perspective, I propose three critical research themes to tackle in the next 25 years: the dynamic reciprocity of influence between the individual and the collective; changes in the individual and collective memory structures; and the impact of culture. (Abstract)

Moving forward: the next 25 years: Looking ahead to the big questions for memory scientists to tackle in the next 25 years, I call attention to three research themes: (i) a reciprocal and iterative analysis to understand how the individual and the collective influence each other; (ii) a study of changes in the structure of memory at the individual and collective levels; and (iii) a study of the influences of culture in which the individual is embedded..

Conclusion Beyond studying the individual and the collective as separate levels of analysis, the time is ripe to investigate the reciprocal and iterative process between the collective and the individual. Myriad factors can determine the influence each can have on the other; for example, the characteristics of the individual in a network, and the properties of the full network. From a cognitive perspective, memory organization and culture are two such key sources of influence, each worthy of study.

See, Judi, et al. People are Like Plutonium. Collective Intelligence. 2.2, 2023. In this new journal, Sandia National Labs and Idea Connection Systems, Rochester scholars cast an innovative vista by which to suggest a deep affinity between a widest span of persons and physics. Going forward so it seems, a grand scientific affirmation and re-marriage of macro and micro ecosms can at last be affirmed and verified. Thus this once and future invariant feature at the heart of wisdom can become a luminous, truth we so need.

An analogy is drawn between the study of human behavior and of the element plutonium to demonstrate that soft and hard sciences are more similar than different. The studies of human behavior and plutonium follow a common research cycle akin to Thomas Kuhn’s paradigm changes which evinces that the thought processes and methodologies for success are congruent in these far removed realms. The primary implication from this analogy is that scientists in all disciplines could well buffer the distinction between soft (human) and hard (universe) phases. Focusing on similarities rather than differences among researchers from disparate disciplines would serve as a vital way to enhance collective intelligence.

Stepney, Susan. Computing with Open Dynamical Systems. ieeexplore.ieee.org/document/9475943.. A presentation by the University of York computation theorist (search) at a 2021 IEEE Conference in the IEEEXplore journal is available at this address. Its intent is to show how natural complexities can also be appreciated to perform as information processors

Computation is often thought of as a branch of discrete mathematics, using the Turing versions. That model works well for conventional applications such as word processing and database transactions. But much of the world's computer power resides in embedded devices, sensing and controlling complex physical processes. Other computational approaches might be better suited to such as a form of complex dynamical systems. One particular view is reservoir computing which can apply to different material substrates and integrate sensing and computing in a single physical package. (Excerpt)

Reservoir computing is a framework derived from recurrent neural network theory that maps input signals into high dimension computational spaces through the dynamics of a fixed, non-linear system called a reservoir. (Wikipedia and Recent Advances in Physical Reservoir Computing by Gouhei Tanake, et al in Neural Networks (115/7, 2019).

Thieu, Thoa and Roderick Melnik. Social Human Collective Decision-making and Applications with Brain Network Models. arXiv:2307.05731. As an awareness of the actual process and value of cooperative cognition grows, Wilfrid Laurier University, Canada system theorists (search RM) describe their study of way to enhance diverse, public engagements.

In this chapter, we consider probabilistic drift-diffusion models and Bayesian inferences to better assist this title issue. We explain the models and representative numerical examples. We also give a review of recent developments in human collective decision-making and its applications with brain network research such as the role of neuromodulation, reinforcement learning in decision-making processes. Finally, we call attention to open problems, and promising approaches iincluding those arising from nonequilibrium considerations. (Excerpt)

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