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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

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

2. Collective Local/ Global Brain Intelligences

In November 2022, we introduce this new section which could not have been added sooner. Two prime, content-based occasions are the online presence of a dedicated Collective Intelligence journal, see J. Flack herein, and a 25th Anniversary: Looking Forward special issue of Trends in Cognitive Science (26/12, December 2022) with many general CI entries, see M. Botvinick below.

The occasion has a timely relevance because our website, whose working premise from the early 2000s has been that a further emergent planetary phase of composite, collaborative studies could be seen as learning on her/his own self.. Some names could be a prior Noosphere, a Knowsphere, maybe a Yesphere. After two intense decades, it is increasingly evident that our Earthumanity has attained an enveloping mental cognizance. A prime consequence is a vast accumulated elibrary information repository that is mostly accessible to everyone. As these present years are beset with plagues, war crimes, weather crises, social chaos, a tragic litany, peoples in pain and despair need to appreciate a concurrent advent of such a palliative, salutary, ordained resource.

Baltzerson, Rolf. Cultural-Historical Perspectives on Collective Intelligence. Oxford University Press, 2022.

Botvinick, Matthew. Realizing the Promise of AI: A New Challenge for Cognitive Science. Trends in Cognitive Sciences. 26/12, 2022.

Centola, Damon. The Network Science of Collective Intelligence.. Trends in Cognitive Sciences. 26/11`2022,

Duarte, Denise, et al. Representing Collective Thinking through Cognitive Networks. Journal of Complex Networks. 10/6, 2022.

Flack, Jessica, et al. Editorial to the Inaugural Issue. Collective Intelligence. August, 2022.

Ha, David and Yujin Tang. Collective Intelligence for Deep Learning. Collective Intelligence. September, 2022.

Millhouse, Tyler, et al. Frontiers in Collective Intelligence: A Workshop Report. arXiv:2112.06864.

Rajaram, Supama. Collective Memory and the Individual Mind. Trends in Cognitive Sciences. 26/12, 2022.

Stepney, Susan. Computing with Open Dynamical Systems. ieeexplore.ieee.org/document/9475943.


Allen, Benjamin, et al. Natural Selection for Collective Action. arXiv:2302.14700. For the record, eight Emmanuel College biomathematicians suggest ways that life’s evolution is innately supportive of a steady scale of beneficial cooperatives.

Collective action as the combined behaviors of multiple individuals occurs across living beings. Knowledge of how and why it evolves has a prime value for behavioral ecology, multicellularity, and human society but is hard to model due to nonlinear fitness effects along with spatial, group, and/or family units. Here, we derive a simple condition for collective action as favored by natural selection with a group influence on each individual weighted by the relatedness between them. (Excerpt)

Arola-Fernandez, Luis and Lucas Lacasa.. An effective theory of collective deep learning. arXiv:2310.12802. Instituto de Fısica Interdisciplinar y Sistemas Complejos IFISC, Mallorca computational physicists add a further dimension to the burgeoning AI intensity, whence the neural net activities can be seen to have a group-like collectivity. Once again this common phenomena recurs everywhere.

Unraveling the emergence of collective learning in artificial neural network systems points to broad implications for machine learning, neuroscience, and societies. Here we introduce a minimal model that condenses several recent algorithms via a competition between two modes: the local learning parameters of each neural net unit, and a diffusive coupling among units that homogenizes the parameters of the ensemble. This framework predicts disorder-order-disorder phase transitions that reveal the onset of a collective learning phase. Our work begins to establish the basic physics of collective learning. (Excerpt)

In a nutshell, this work offers a mathematical foundation for collective learning in natural and artificial systems. Our perspective enriches deep learning theories and statistical physics approaches with regard to interactive brains, and makes a first step towards a next-generation model to validate emergent collective learning in many-agent populations. (5)

Baltzerson, Rolf. Cultural-Historical Perspectives on Collective Intelligence. Oxford University Press, 2022. An Oslo Metropolitan University College historian writes a first thorough, book-length treatment as a quantified recognition grows about how vital and persistent this cognitive facility is across all manner of animal, and human groupings. Chapters run from Crowdsourcing and Open Online Knowledge Sharing to Collaborative Problem Solving and COVID as a Wicked Problem. It closes with The Intelligent Society to consider and recommend that an intentional enhance of the realization that public assemblies of any size and case do actually possess such a potential ability. The full PDF text is available as Open Access on Cambridge Core web page.

Throughout our evolution, our most extraordinary ability as humans is to collaborate with each other. Our history is much about how we gradually learned to solve problems together in larger groups. We first lived in caves in small numbers, then peoples went on to form villages, which with time, grew into kingdoms and nations. Today millions of us find fresh ways of solving problems in large distributed groups in a global online setting. In regard, platforms and projects allow open online knowledge sharing, e.g. Wikipedia) and so on. There is also a growing awareness that complex problems like climate change or COVID-19, require innovative worldwise approaches that build on the combined scientific and political efforts of individuals and teams all over the globe. (1)

Another strand of CI research considers different types of self-organization. An overall macro stage describes the Internet as a self-organizing super-intelligence that unites all human sapience into a network of information and communication. CI emerges from the myriad interactions between humans and computers in a vast online communication network. This global brain is immensely complex and self-organizing without any centralized control, and emerges as an adaptive complex system. (8)

Botvinick, Matthew. Realizing the Promise of AI: A New Challenge for Cognitive Science. Trends in Cognitive Sciences. 26/12, 2022. A Deep Mind, London computational neuroscientist writes a lead paper for a 25th Anniversary Series: Looking Forward special issue whose 25 authoritative, diverse entries review past years as a way to preview and enhance a new quarter century of interdisciplinary insightful advance. A main endeavor should be a humane, beneficial syntheses of this planetary collective intelligence CI phase with deep neural AI learnings.

See, for example, Advanced Human Cognition Abilities with Deep Neural Networks by James McClelland, Predictive Architecture of the Mind and Brain by Floris de Lange, et al, The Computational Society by Nick Chater, What would Make Cognitive Science more Useful? by Neil Lewis, Collective Memory and the Individual Mind by Suparna Ragaran (see review) , and The Complex Brain: Connectivity, Dynamics, Information by Olaf Sporns.

Rapid progress in artificial intelligence (AI) places a new spotlight on a long-standing question: how can we best develop AI to maximize its benefits to humanity? Answering this question in a satisfying and timely way represents an exciting challenge not only for AI research but also for all member disciplines of cognitive science.

How do individual human minds create languages, legal systems, scientific theories, and technologies? From a cognitive science viewpoint, such collective phenomena may be considered a type of distributed computation in which human minds together solve computational problems beyond any individual. This viewpoint may also shift our perspective on individual minds. (Nick Chater)

Predictive processing has become an influential framework in cognitive neuroscience. However, it often lacks specificity and direct empirical support. How can we probe the nature and limits of the predictive brain? We highlight the potential of recent advances in artificial intelligence (AI) for providing a richer and more computationally explicit test of this theory of cortical function. (Floris de Lange)

Most would agree, the brain is complex. But, beyond metaphor, does the brain’s complexity demand a paradigm shift in how we study its structure and function? I argue that complexity manifests in three domains – connectivity, dynamics, and information – and that unlocking their interactions will greatly advance our understanding of brain and cognition. (Olaf Sporns)

Cao, Hung, et al. Fostering new vertical and horizontal IoT applications with intelligence everywhere. Collective Intelligence. 2/4, 2023. Analytics Everywhere Lab, University of New Brunswick, Fredericton, Canada, Asia University, Taichung, Taiwan describe another clever endeavor in this issue, as the Abstract cites, to advantage this nascent occasion of an open worldwise library-like facility. See also Collective Intelligence Using 5G: Concepts, Applications, and Challenges by Arun, Narayanan et al in IEEE Access (July 2022).

Intelligence Everywhere is predicated on the integration of Internet of Things (IoT) networks as they convey a vast amount of data streams through many computing resources which rely on distributed machine learning models. The result is an interconnected and collective intelligent ecosystem where devices, services, and users work together. This paper discusses state-of-the-art research and the principles of our Intelligence Everywhere framework for enhancing IoT applications such as Digital Health, Infrastructure, and Transportation/Mobility.. Finally, this paper provides comprehensive insights into the challenges and opportunities for harnessing collective knowledge which can foster to optimum processes and better overall collaboration across different IoT sectors. (Excerpt)

Collective intelligence is a form of decision-making where intelligent, distributed human and software agents, situated in a networked communication system, receive information and feedback from their immediate environment and other agents and make decisions collectively to perform tasks that, together, achieve a common desirable outcome. (Definition from Narayanan)

Casadei, Roberto. Artificial Collective Intelligence Engineering: A Survey of Concepts and Perspectives. Artificial Life. 29/4, 2025. In a Special Issue on Lifelike Computing Systems, a University of Bologna surveys an array vital aspects by which nature’s steady propensity to become smarter, learned, solve problems, find better ways, by forming conversational groupings.

Collectiveness is an important property of many natural and artificial systems. By way of a large number of individuals, it is possible to produce intelligent collective. Indeed, collective intelligence is often a design goal of engineered computation motivated by technoscientific trends like the Internet of Things, swarm robotics, and crowd computing. The challenge is to identify, place in a common structure, and connect the different areas and methods through intelligent collectives. This article covers preliminary notions, fundamental concepts, identifies opportunities for researchers on artificial and computational collective intelligence engineering.

Centola, Damon. The Network Science of Collective Intelligence.. Trends in Cognitive Sciences. 26/11`2022, . With an emphasis on problem solving and crowd wisdom, a University of Pennsylvania social scientist surveys earlier versions in several fields such as climate studies, management, elections, and so on. But as they try to become efficient, it is proposed that as active connectivities form cooperative interrelations they result in network topologies. The paper goes on to illustrate how such a structured appreciation can serve better group cohesion and performance. That is to say, an awareness of these innate dynamic features is a vital step. Our intent in this new section is to help identify and enhance this novel facility and knowledge resource just among us all.

In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively. I conclude by highlighting current theoretical problems at the forefront of research on networked collective intelligence, as well as vital public policy challenges that require new research efforts. (Abstract)

The essential puzzle of collective intelligence is whether the collective judgment from a group of people will outperform a smart individual reasoning alone. Recent computational and experimental studies have led to breakthroughs in two of the primary fields of networked collective intelligence: collective problem-solvingand the wisdom of the crowd. Collective problem-solving typically addresses the optimal design for communication networks within organizations. The key network property governing problem-solving outcomes is informational efficiency. (Highlight)

This review offers a new perspective showing how network science may provide a unifying framework for research on collective intelligence. I focus on recent insights from two key fields of empirical study spearheading new approaches to the network dynamics of group performance, namely: collective problem-solving and the wisdom of the crowd. Here, I present a synthesis of this work that broaches a generalized understanding of how social networks influence collective intelligence.. (1)

Christian, David. Future Studies. Little, Brown, 2022. The Macquarie University, Australia big historian notably founded this widest integral field in the early 2000s. After many popular writings and collegial projects (search) this new work turns about to look ahead, imagine and survey further trajectories of life’s entire evolutionary course, as we Earthlings may be able to track and trace. True to style, sections run from how microbes seem to sense and plan, onto to life’s emergent, communal, cognizant organisms, and to our sentient, curious selves as we may begin to scan and consider near and far astronomic complexities.

But as his prior works have emphasized, a special emphasis is the constant occasion of a collective learning process in all manner of groupings by which they sustain, prevail and ascend. See Optimizing the Location of the Colony of Foragers with Collective Learning by Sanchayan Bhowal, et al (arXiv:2211.02424) from the references. We would then highlight the cultural importance of this natural tendency and extend it to our EarthSmart Learns moment as another way to appreciate how our planetary prodigy can gain vital knowledge on her/his own.

The future is uncertain, possibly dangerous, maybe wonderful. We cope with this uncertainty by telling stories about the future. This book is thus about looking ahead as a sort of a User’s Guide. We all need such a guide because the future is where we will spend the rest of our lives. David Christian, historian and author of Origin Story, is renowned for pioneering the emerging discipline of Big History, which surveys the whole of the past. But with this volume, he casts his expansive vision forward to introduce whatever near and farther realms might be imagined from the individual to the cosmological. (Excerpt)

The Social and Cultural Differences of Language and Collective Learning: Along with those enhanced skills our lineage got an unexpected evolutionary bonus. Larger brains enabled an even more transformative change: collective learning. Many species have some form of culture because they can share information. But humans can uniquely transfer detailed knowledge across scales and generations to an extent that an accumulated store is achieved. That is what I mean by this phrase, and ability which has taken over the whole world. As collective repositories grew our lifeways, thought processes and technologies have profoundly changed. (122-123)

Duarte, Denise, et al. Representing Collective Thinking through Cognitive Networks. Journal of Complex Networks. 10/6, 2022. We cite this December article by University of Sao Paulo scholars (see bio’s below for global postings) as an example of this actual “thinking like a planet” emergent expansion of Wuman-Earth cognizance. Another indication is a proper advent of this novel 2022/2023 section, and CI journal,

This article presents a novel quantitative approach using network features to represent community collective thinking. We propose a new function, called the cognitive affinity coefficient that maps individual cognitive links within a graph structure. This function transforms the data generated by the words chosen for an individual regarding a specific subject into an appropriate relational object for analysing cognitive networks. We apply our methodology to novel data on evocations about river floods, which allowed us to find communities inside the network according to their thinking about this subject and identify the most active individuals inside each one and, therefore, explicit their collective thinking. (Abstract)

Denise Helena Silva Duarte: I am a Full Professor at the School of Architecture and Urbanism, and Head of the Graduate Programme and Environment and Energy Studies Lab at the University of Sao Paulo, Brazil.

Gilvan Guedes: I am a Demographer at the Federal University of Minas Gerais in health issues, and spatial population distribution. I mostly use statistical methods (simulation, regression based methods and network analysis) coupled with formal demography (life table models and decomposition studies).

Wesley Pereira: I have a D.Sc. in Computational Modeling (2019) from the National Laboratory of Scientific Computation, Brazil. I am presently a Research Associate in Mathematical and Statistical Sciences at the University of Colorado, Denver which involves Applied Mathematics, Scientific Software Development, and Large-Scale Numerical Algorithms.

Rodrigo Ribeiro, Ph.D: I am a Brazilian guy who enjoys to spend his time with his wife, friends and family. I have relatives and friends over the globe which makes me feel safe wherever I go. I am now a visiting assistant professor at University of Colorado Boulder in to research mathematics and probability.

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.

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