II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Twintelligent Gaiable Knowledge

2. Collective Local/ Global Brain Intelligences

Flack, Jessica, et al. Editorial to the Inaugural Issue. Collective Intelligence. August, 2022. JF, SFI, Panos Ipeirotis, NYU, Thomas Malone, MIT Geoff Mulgan, UCL and Scott Page, U. Michigan introduce this mid 2022 periodical co-published by SAGE and ACM Digital. It’s origins stem from Santa Fe Institute seminars with J. Flack, David Krakauer, others to a point that a dedicated journal became merited to convey and study the significant presence of this personal to planetary communicative, knowledge gain process.

See among initial articles Collective Intelligence for Deep Learning by David Ha and Yujin Tang (see review), Self-Organization in Online Collaborative Work Settings by Joanna Lykourentzou, et al, A descriptive analysis of collective intelligence publications since 2000 by Berditchevskaia, Aleks, et al and Collective Intelligence as a Public Good by Naomi Leonard and Simon Levin.

Collective behavior is a universal property of biological, social, and many engineered systems. However, the study of collective intelligence—roughly, the production of adaptive, wise, or clever structures and behaviors by groups—remains nascent. Despite that, it is growing in various disciplines, from biology and psychology to computer science and economics, management, and political science to mathematics, complexity science, and neuroscience. With the launch of Collective Intelligence, we aim to create a publication that transcends disciplines, methodologies, and traditional formats. We hope to help discover principles that can be useful to both basic and applied science and encourage the emergence of a unified discipline of study. (Abstract)

We can find collective intelligence in any system in which entities collectively, but not necessarily cooperatively, act in ways that seem intelligent. Often — but not always — the group’s intelligence is greater than the intelligence of individual entities in the collective. These entities can be molecules, cells, biological organisms, computers, organizations, software components, or machine learning systems. They may perform tasks such as identifying phenomena, making predictions, solving problems, or taking actions (1)

Friston, Karl, et al. Designing ecosystems of intelligence from first principles. Collective Intelligence. January, 2024. As the AI frontier opens wide, twenty neuroscholars from the UK, USA, Canada, Germany, Australia and the Netherlands coauthor a proposed approach and plan at this outset to respectfully orient, guide and enhance a safe, viabe way forward. Their endeavor concurs with Pierre Levy’s paper Semantic Computing with IEML herein (2/4, 2023) to build in dedicated programs for this purpose.

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade. Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making in which people are integral participants by way of a shared intelligence. This vision is premised on active inference which is a means of adaptive behavior that can be read as a physics of intelligence, self-organization, and as self-evidencing. We consider communication protocols needed to enable such a knowledge ecosystem. (Excerpt)

Active inference offers a formal definition of intelligence for AI research that entails the beliefs of agents and groups which allows us to write down the self-organizing over several scales. The result is AI that “scales up” the way nature does: by aggregating individual minds and their contextual knowledge into “nested intelligences”. (2) Once intelligence at each scale supervenes on, or emerges from, simpler phases, the multi-scale view of natural acumen implies a recursive structure in which the same functional motif recurs in ramified forms via more complex agents. (3)

Conclusion: Our proposal for stages of development for active inference as an artificial intelligence technology The aim of this white paper is a vision of research and development in the field of artificial intelligence for the next decade. We have proposed active inference as a technology uniquely suited to the collaborative design of an ecosystem of natural and synthetic sense-making, in which humans are integral participants by way of shared intelligence. The Bayesian mechanics that follows led us to define intelligence as the accumulation of evidence for an agent’s generative model of their sensed world—also known as self-evidencing (search Hohwy}. (12)

Galesic, Mirta, et al. Beyond Collective Intelligence: Collective Adaptation.. Journal of the Royal Society: Interface. March, 2023. Twenty senior biobehaviorists from the USA, Austria, Denmark, Germany, the UK including Dora Biro, Robert Goldstone and Alex Mesoudi identify and explain how all manner of animal groupings across life’s long evolution occur due a deep propensity to not only become smarter, but to enhance this fitness by such communal unities. These latest, salient findings are then braced by vivid graphics and 300 references.

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.

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