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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 1 through 15 of 42 found.


Our EarthVerse Edition: A 21st Century, Scientific, PhiloSophia, eLibrary of eCosmos Resource

The Genesis Vision > News

Daniels, Bryan, et al. Identifying a Developmental Transition in Honey Bees Using Gene Expression Data. bioRxiv, November 7, 2022. A latest paper by Arizona State University and Banner Health, Phoenix complexity theorists including Robert Page describes how dynamic genome studies now reveal critically poised bistable states even in this prescriptive phase. This –omic occurrence of self-organized criticalities can well establish nature’s 2020s universal preference for this optimum poise. See also Social Networks Predict the Life and Death of Honey Bees by Benjamin Wile, et al in Nature Communications 12/1, 2021 and Self-Organization and the Evolution of Division of Labor by R. Page and Sandra Mitchell in Apidologie (29/1, 1998).

In many organisms, interactions among genes lead to multiple functional states, while other interactions can transition into new modes, maybe by way of critical bifurcations in dynamical systems. Here, we develop a statistical theory to identify a bistability near a transition event from gene expression data. We apply the method to honey bees where a known developmental occurrence between bees performing tasks in the nest and leaving to forage. Our approach is able to predict the emergence of bistability and link it to genes involved in the behavioral transition. (Abstract excerpt)

Social insects represent well-known examples of adaptive collective systems, combining the efforts of many individual actors to produce robust and adaptive aggregate behavior. The allocation of tasks to individuals often displays a sophisticated organization that promotes collective success. This distributed coordination of effort is the result of a complicated process reaching from the level of gene regulation to social relations. (1) To summarize, the generality of this phenomenology suggests that such critical transitions may be a common mechanism within biology, making use of the emergent properties of strongly interacting dynamical networks to generate reproducible diversity. (14)

The Genesis Vision > News

Romanczuk, Pawel and Bryan Daniels. Phase Transitions and Criticality in the Collective Behavior of Animals. arXiv:2211.03879. Humboldt University and Arizona State University (see websites) post a chapter for the 2023 Volume VII of the World Scientific series Order, Disorder, and Criticality. An especial notice is that it is edited by Yuri Holovatch (search) at the Laboratory for Statistical Physics of Complex Systems (194.44.208.227/~hol/), National Academy of Science in Ukraine, see notes below. This subject entry has its own distinction as an early integral synthesis of 21st century nonlinear science which proceeds to join an older complex adaptive system format with newly-realized, consequent self-organized criticalities. After these novel appreciations are described as they exemplify across every natural and social domain, the paper goes on to trace their deep rootings in active statistical physics phenomena.

Collective behaviors exhibited by animal groups, such as fish schools, bird flocks, or insect swarms are valid examples of self-organization in biology. Concepts and methods from statistical physics have lately been used as a theoretic reason for such collective effects in living systems. In addition, it has been implied that animal groupings should operate close to a phase transition as a (pseudo-)critical point to optimize their capability for collective computation. In this chapter, we will discuss the current state of research on the "criticality hypothesis", along with how to measure distance from criticality. We highlight the emerging view that explores the benefits of living systems being able to tune to an optimal distance from criticality. (Abstract)

Collective behavior exhibited by large animal aggregations such as swarms of insects, schools of fish, and flocks of birds are ubiquitous examples of biological self-organization. Physicists now investigate parallels between large animal collectives and statistical phenomena where local interactions between simpler components can lead to adaptive macroscopic properties. This functional behavior relies on distributed information available to entities within complex biological systems such as proteins in cells, neurons in brains onto animal and human groups. (2)

As laid out in this chapter, phase transition and criticality theories are highly relevant for understanding the interplay of self-organization and active organism behaviors. The “criticality hypothesis", whence complex biological system seek to optimize their collective computation capabilities, can provide a unifying principle across life’s life’s nested scales. Our consideration aligns with recent calls within evolutionary biology and ecology for novel ideas that can be grounded in a theoretical physics perspective. A truly bidirectional exchange between physics and biology thus opens new avenues of research for better fundamental understandings. (21)

The first volume of Order, Disorder and Criticality was published by World Scientific in 2004 and, over time, it gave rise to this book series. Its chapter content originated from the Ising (Ernst 1900-1998) Lectures workshops that occurred annually in Lviv in the Ukraine. The volumes initially aimed to provide topical surveys related to phase transitions and criticality in theoretical studies. As they appeared, it grew to natural phenomena beyond statistical physics such as complex biological systems composed of many interacting components that display collective behavior above their individual parts. (Yuri Holovatch)

EarthKinder Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

A Learning Planet > The Spiral of Science

Huertan-Company, M. and F. Lanusse. The Dawes Review 10: The Impact of Deep Learning for the Analysis of Galaxy Surveys. arXiv:2210.01813. Universidad de La Laguna, Tenerife, Spain astronomers post an extensive review as these collaborative, computational studies presently spiral on up to an Earthropo sapience. Some 500 references indicate how much our intrinsic endeavors to quantify and describe a celestial spacescape have a planetary cast. See also Gravothermal Collapse of Self-Interacting Dark Matter Halos as the Origin of Black Holes in Milky Way Satellites by Tamar Meshveliani, et al (University of Iceland) at arXiv:2210.01817 as another transitional example.

As these data flows grow, here we review the main applications of deep learning for galaxy surveys so far. We report that the applications are becoming more diverse and deep learning is used for computer vision estimates of galaxy properties, along with cosmological models.. Some common challenges are cited before moving to the next phase of deployment in the processing of future surveys; e.g. uncertainty quantification, interpretability, data labeling, and so on as the endeavor shifts from training and simulations, so to become a common practice in astronomy. (Excerpt)

A Learning Planet > The Spiral of Science > deep

Smith, Michael J. and James Geach. Astronomia ex Machina: A History, Primer and Outlook on Neural Networks in Astronomy. Royal Society Open Science. November, 2022. University of Hertfordshire computer scientists post a detailed 21st century recount of this ascendant turn from local homo sapience when computers and internet websites came online in the early 2000s to a worldwise cerebral activity today. But this spiral anthropic to Earthuman stage proceeds by way of machine computations which can analyze vast cosmic data flows on their own. See also, for example, A Neural Network Subgrid Model of the Early Stages of Planet Formation by Thomas Pfeil, et al at arXiv:2211.04160.

In recent years, deep learning procedures have been taken up by many fields because it reduces the need for specialist knowledge and automating the process of knowledge discovery from data. This review describes how astronomy is similarly in the midst of a deep learning transformation. We trace astronomical connectionism from early multilayer perceptrons, through to recurrent neural networks, onto the current wave of self-supervised and unsupervised methods. We then preview a fourth phase of a “foundational” model by way of a symbiotic relationship between astro=science and connectionism. (Abstract excerpt)

A Learning Planet > Mindkind Knowledge

Robin, Amanda, et al.. Major Evolutionary Transitions and the Roles of Facilitation and Information in Ecosystem Transformations.. Frontiers in Ecology and Evolution. December, 2021. A contribution by UCLA and Stanford University biologists to a special Social Evolution and the Major Evolutionary Transition in the History of Life issue (see Peter Nonacs for review) which provides a rare, latest extension of this emergent scale onto its global fulfillment. Such a obvious but unfamiliar perception likely had to hold off until a 2020s retrospect to admit and appreciate this evident domain which has long been the basis for our EarthWise attribution. In regard, we offer an array of quotes.

Into the 21st century, the presence of “Major Evolutionary Transitions” (METs) with novel forms of organismal complexity, information and individuality have gained increasing notice among biologists. Into these 2020s, we introduce this special collection meant to gather many findings into an overdue full scale, explanatory recognition of life’s main ascendant course. We also seek to provide this evolutionary sequence within an ecological basis, aka Major System Transitions (MSTs). In regard, important morphological adaptations are noted that spread through populations because of direct-fitness advantages for individuals. We elucidate the role of information across five levels: (I) Encoded; (II) Epigenomic; (III) Learned; (IV) Inscribed; and (V) Dark, newly due to abiotic entities rather than organisms. Level IV is then seen to engender a worldwide human phase emergence. (Abstract excerpt)

The Levels of Information: Instructional: Information is transformed into physical, symbolic formats that have vast storage capacity. An instructional corpus can far exceed the combined encoded, epigenetic, learned and iconic content previously available to any single individual. Across the tree of life, only humans are known to have ever extensively created and used instructional information. Dark: Information produced by abiotic computer programs which are so complicated that biological organisms cannot replicate or derive. Examples are: internet search engines; global climate models; bioinformatic analyses of genetic data sets; neural network simulations and genetic algorithm models. The potential reach of this information may exceed that of the species that creates it, to the extent that it may become a new ‘living species’ in and of itself. (4)

The capacity for symbolic representation of language is critical for the emergence of technological innovations that expanded the realized niche for humans exponentially and paved the path to a global MST. We proliferated across every continent and environment on Earth while substantially impacting these ecosystems. One example of inscribed language producing global-altering information and technology is the very existence of the discipline of evolutionary science and the systematic study of life itself. Humans are uniquely able
to understand how evolution works. (15)

A Learning Planet > Mindkind Knowledge > CI

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)

A Learning Planet > Mindkind Knowledge > CI

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)

A Learning Planet > Mindkind Knowledge > CI

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)

A Learning Planet > Mindkind Knowledge > CI

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)

A Learning Planet > Mindkind Knowledge > CI

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)

A Learning Planet > Mindkind Knowledge > CI

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.

A Learning Planet > Mindkind Knowledge > CI

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.

A Learning Planet > Mindkind Knowledge > CI

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.

A Learning Planet > Mindkind Knowledge > CI

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.

A Learning Planet > Mindkind Knowledge > CI

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).

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