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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 82 found.


> Geonativity

Song, Tiancheng, et al. Unconventional Superconducting Quantum Criticality in Monolayer WTe2.. arXiv:2303.06540. Into the mid 2020s, fifteen Princeton University and National Institute for Materials Science and Nanoarchitectonics, Japan researchers proceed to find an inherent tendency for optimum critical behavior even in this substantial realm. See also Self-similarity of the third type in ultra relativistic blastwave by Tamar Faran, et al at arXiv:2402.07978 for another instance of deeply ingrained critical behavior.

The superconductor to metal transition in two dimensions (2D) provides a platform for study quantum phase transitions (QPTs) and critical phenomena but many questions remain. Extending Nernst experiments down to millikelvin temperatures, we identify a superconducting quantum critical point (QCP) in spin Hall insulator made of tungsten ditelluride (WTe2). These findings, which have no prior analogue, call for careful examinations of the mechanism of the QCP, including the possibility of a QPT between ordered phases in the monolayer. Our experiments open a new avenue for studying quantum critical matter. (Abstract)

Our Planatural Edition: A 21st Century PhiloSophia, Earthropo Ecosmic PediaVersion

The Genesis Vision > Historic Precedents

Robledo, Alberto and Carlos Velarde. rA Half-Century Research Footpath in Statistical Physics. arXiv:2401.06181. Universidad Nacional Autónoma de México physicists provide a unique retrospective of a combined sequence of achievements on a long winding walk toward a unified synthesis. The nonlinear dynamics section alone has a dozen sections which stand as a good review of the subject.

We give an account of condensed matter and complex system studies that span five decades by links to access abstracts and full texts of a select publications. The topics, techniques and outcomes reflect evolving interests of the community along with the use of analogies in distinctive ways. The studies have been grouped into thirty sets and these, in turn, placed into three collections according to the main approach: stochastic processes, density functional theory, and nonlinear dynamics. We refer to our main surmise: Athe validity of ordinary statistical mechanics and the pertinence of (Constantino) Tsallis statistics. (Excerpt)

The Genesis Vision > News

Kukarni, Suman, et al.. Information content of note transitions in the music of J. S. Bach. Physical Reviews Research. 6/013136, 2024. University of Pennsylvania systems scholars including Chris Lynn and Dani Bassett post an innovative appreciation that symphonic and melodious compositions are suffused with and arranged by multiplex networks. The paper reviews of a technical basis which is graphically illustrated. After many centuries the actual presence of natural rhythms is mathematically quantified and published in a Physics journal. See also Unsupervised cross-domain translation via deep learning and application to music-inspired protein designs by Markus Buehler in Patterns. (4/3, 2023) and Cells and sounds by Michael Spitzer in Progress in Biophysics and Molecular Biology (186, January 2024). If olny we could hear and listen to the song of the cell and of the ecosmos.



Music has a complex structure that expresses emotion and conveys information. Here we study a musical piece by way of networks formed by notes (nodes) and their transitions (edges). Thus we view compositions by J. S. Bach through the lens of network science, information theory, and statistical physics over a wide range of fugues and choral pieces. In turn, we consider human neural networks that enable efficient communication via heterogeneity and clustering. Taken together, our findings shed light on both Bach's work and further studies of complexities, creativity, and more. (Abstract excerpt)’

We hope that our framework inspires more exchanges between physics, cognitive science, and musicology. On a broader scale, our project investigates how information in complex systems is conceptually contained. To conclude, we highlight a number of exciting directions for future inquiry and outline ways in which our approach can be expanded upon and improved. (10) By providing an example of a comprehensive analysis of musical melodies, our version complements the rich study of language, music, and art as dynamic complex multiplex systems. Finally, a quantitative treatment of the patterns and motifs inspire analogies between music and other fields of science such as including understanding protein structures and designing organic materials. (12)

The Genesis Vision > News

Manrique, Pedro, et al. Non-equilibrium physics of multi-species assembly: From inhibition of fibrils in biomolecular condensates to growth of online distrust.. arXiv:2312.08609. George Washington University theorists including Neil Johnson (search PM, NJ) post an innovative, wide correspondence between biomolecules and sociopeople as they/we intersect, crosstalk and come together. See also Multi-Species Cohesion: Humans, machinery, AI and beyond by this group at arXiv:2401.17410. Once again a common affinity is evident across these widest reaches which then implies deeper a physical origin.

Self-assembly is a key process in living systems from the microscopic biological level (e.g. proteins into fibrils in a human cell) to the macroscopic societal level (e.g. humans into common-interest social media). The components in such systems) are highly diverse, and so are the self-assembled structures that they form. But there is no theory of how they arise from a multi-species pool. Here we provide a simple model which trades myriad chemical and human details for a transparent analysis, in good agreement. It reveals a new inhibitory role for biomolecular condensates against dangerous amyloid fibrils, as well as a kinetic reason so much distrust has now beset the internet. The nonlinear dependencies that we uncover suggest real-world control strategies to buffer and better these processes. (Excerpt).

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

A Learning Planet > The Spiral of Science

Ho, Matthew, et al. LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology. arXiv:2402.05137. We note this paper by fifteen coauthors with postings in France, Korea, the USA and UK to report and convey an advancing reciprocal synthesis of personal guidance and computational abilities as public scientific endeavor enter a collaborative Earthwise era.

This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline as a codebase for rapid, user-friendly, machine learning (ML) knowledge in astrophysics and cosmology. The program includes software for neural architectures, training schema, priors, and density estimators adaptable to any research workflow. We present real applications such as estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra, gravitational wave signals; and semi-analytic models of galaxy formation. We also include comparisons of other methods as well as discussions about the ML inference in astronomical sciences. (Excerpt)

A Learning Planet > Mindkind Knowledge > deep

Pierre-Yves Oudeyer. www.pyoudeyer.com.. . The French computational psychologist (search) is the director of the Flowers project-team at the Inria Center of University of Bordeaux. Current (March 2024) projects are now much involved with chatty AI features guided by insights gained from studies with children. A recent talk is Developmental AI: machines that learn like children and help children learn better. As the quotes say, another senior scholar finds evidence that both youngsters and large language modes use trail/error iterate methods in similar ways. See also Open-ended learning and development in machines and humans on the flowers.inria.fr. site.


Together with a great team, I study lifelong autonomous learning, and the self-organization of behavioural, cognitive and language structures at the frontiers of artificial intelligence and cognitive sciences. I use machines as tools to understand better how children learn and develop, and I study how one can build machines that learn autonomously like children, as well as integrate within human cultures, within the new field of developmental artificial intelligence. (P-Y O)

The Flowers project-team, at the University of Bordeaux and at Ensta ParisTech, studies versions of holistic individual development. These models can help us better understand how children learn, as well as to build machines that gain knowledge as children do, aka developmental artificial intelligence, with applications in educational technologies, automated discovery, robotics and human-computer interaction.

A Learning Planet > Mindkind Knowledge > deep

Frank, Michael. Baby steps in evaluating the capacities of large language models.. Nature Reviews Psychology. 2/6, 2023. A Stanford University child psychologist offers another recognition that an intrinsic affinity seems to be apparent between such ChatGPT resources and how children achieve literacy and factual comprehension. It is then recommended that an integrative accord between the two general approaches would be beneficial. See also Variability and Consistency in Early Language Learning: The Wordbank Project by MF and colleagues (MIT Press, 2021).

Large language models show remarkable capacities, but it is unclear what abstractions support their behaviour. Methods from developmental psychology can help researchers to understand the representations used by these models, complementing standard computational approaches — and perhaps leading to insights about the nature of mind.

A Learning Planet > Mindkind Knowledge > deep

Levi, DeHaan. LLM as Child Analogy.. levidehaan.com.. This is a posting by a veteran cyber designer whose group projects can be found on the above site. A longer title is Theorem: Evolutionary Pathway of LLMs under the "LLM as Child Analogy" which may be reached by Google keywords. We cite some excerpts which are similar to Marina Pantcheva’s views herein.

LLMs: A machine learning model designed to process and generate human-like text based on statistical patterns in data. Real-time Adaptability: The ability to modify its behavior based on new information. Memory Retrieval: A system to store, recall, and utilize past interactions. Decision-making Algorithm: A set of rules to make choices. Creative Reasoning: The capability to generate original content but not confined to it.

Children possess real-time adaptability, have a memory retrieval system. develop decision-making abilities and the capability for creative reasoning. If LLMs are to evolve along the lines of children, then the first logical step would be to implement real-time learning algorithms, moving from static to dynamic models. For LLMs to be more analogous to children, they would need the ability to generate new, original content, potentially through some form of creative reasoning. Achieving real-time adaptability would make LLMs dynamic learners, thereby aligning with human children.

A Learning Planet > Mindkind Knowledge > deep

Li, Qing, et al. Progress and Opportunities of Foundation Models in Bioinformatics. arXiv:2402.04286. Chinese University of Hong Kong and BioMap, Beijing computer scientists provide a wide-ranging perspective on this mid 2020s synthesis of a Bioinformatic approach, whose journal goes back to 1985, and these novel AI neural net, large language models as they become amenable.

Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI) and the adoption of foundation models (FMs). These AI techniques have addressed prior issues in bioinformatics such as scarce annotations and of data noise. FMs are adept at handling large-scale, unlabeled data, which has allowed them to achieve notable results in downstream validation tasks. The primary goal of this survey is to conduct a systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed. Finally, we outline potential development paths and strategies for FMs in future biological research. (Excerpt)

A Learning Planet > Mindkind Knowledge > deep

Pantcheva, Marina. How do LLMs and humans differ in the way they learn and use language.. rws.com/blog/large-language-models-humans.. A Senior Group Manager at RWS (see below) with a PhD in Theoretical Linguistics addresses this aspect with a list of several ways by which youngsters become talkative and informed. She then makes note of a general affinity between these personal learning methods and the algorithmic, iterative processes that form LLMs content and capabilities.

The question of how children learn language is central to modern linguistics. Numerous contributions have sought to explain this process, here are a few:

Social interactionist theory suggests that feedback and corrections play a pivotal role in language acquisition along with dialogue between the child and the linguistic adults.
Behaviorist theory posits that children learn language by mimicking those around them and positive reinforcement for their endeavors.

Statistical learning theory proposes that children use the natural statistical properties of language to deduce its deep structure such as sound patterns, words, and grammar.
Universal grammar theory argues for the existence of constraints on what human language can look like. In essence, children possess an innate biological component that enables their rapid development of language. (MP)

Genuine Intelligence (GI). Generative AI and Large Language Models are redefining the boundaries of language and content transformation. GI is not just about AI and people working together, it composes a symbiotic blend of AI's computational capacity with human insight and creativity. RWS is a global company based in the UK for transforming content through translation, localization and AI technology blended with human expertise.

A Learning Planet > Mindkind Knowledge > deep

Ruggeri, Azzurra, et al. Preschoolers search longer when there is more information to be gained. Developmental Science. 27/1, 2024. Senior psychologists AR, MPI Human Development, Oana Stanciu, Central European University, Madeline Pelz, MIT, Alison Gopnik, UC Berkeley and Eric Schulz, MPI Biological Cybernetics provide new insights into how children proactively seek and acquire knowledge and then recommend that the process would serve Large Language Models if it was written into its algorithms

What drives children to explore and learn when external rewards are uncertain or absent? We tested whether information gain itself acts as an internal reward and suffices to motivate children's actions. We measured 24–56-month-olds' behavior in a game where they had to search for an object with uncertainty about which specific object was hidden. We found that children were more persistent in their search when there was higher ambiguity and more information to be gained. Our results highlight the importance of artificial intelligence research to invest in curiosity-driven algorithms. (Abstract)

All in all, these findings consolidate our understandings of children’s motivation to learn and explore, and have strong implications for developmental psychology and artificial intelligence. The results are consistent with a theory of children’s exploration and learning driven by uncertainty reduction. From an artificial intelligence view, they lend further support to the idea that to build computational machines that learn like children, one should build curiosity-based systems and design algorithms motivated by the underlying expected IG (Intelligence gain) of their actions. (6)

A Learning Planet > Mindkind Knowledge > deep

Stevenson, Claire, et al. Do large language models solve verbal analogies like children do?. arXiv:2310.20384. University of Amsterdam psychologists including Ekaterina Shutova cite another present recognition of a basic correspondence, in this title case, of how youngsters draw on commonalities and associations between items or situations and what it seems these AI chatBot procedures arealso trying to do.


Analogy-making lies at the heart of human cognition. Adults solve analogies such as horse to stable and chicken to coop. In contrast, children use association, and answer egg. This paper investigates whether large language models (LLMs) can solve verbal analogies in A:B::C form, similar to what children do. We use analogies from an online learning environment, where 14,002 7-12 year-olds from the Netherlands solved 622 analogies in Dutch. We conclude that the LLMs we tested indeed tend to solve verbal analogies by association like children do. (Excerpt)

An important take-away from our study is that LLMs may solve analogies as well as 11 year-olds, but to ascertain whether this reasoning is emerging in these systems we need to know the mechanisms by which they obtain these comparisons. Our findings point towards associative processes in play, perhaps similar to those in children. (11)

A Learning Planet > Mindkind Knowledge > CI

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)

A Learning Planet > Mindkind Knowledge > CI

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)

Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

Animate Cosmos

Chon-Torres, Ovtavio, et al. Astrobiocentrism: reflections on challenges in the transition to a vision of life and humanity in space. \. International Journal of Astrobiology. February, 2024. Universidad de Lima, ICTP, Trieste, Italy, Lund University, Umeå University, Sweden. King’s College London, CSIC-UCM, Madrid, Universitat Bern, Bern and Bethany College, KS, USA astroscholars including Julian Chela-Flores and David Dunér introduce an engaging, mid 2020s, appreciation of a life-friendly, conducive ecosmos either by an evolutionary genesis on Earth-like analogs, or by human expansion into and colonization of the nearer and further galactic ezpanse.

Astrobiocentrism is a vision that places us in a confirmation of life in the universe, either as a second genesis or as an expansion of humanity in space. Unlike biocentrism or ecocentrism, the astrobiocentric view is not limited to the Earth-centric perspective for it incorporates a multi-, inter- and transdisciplinary understanding. Therefore, the aim of this paper is to be a reflection on the astrobiocentric issues related to the challenges and problems of the discovery of life in the universe. Here we explore some aspects of the transition from biogeocentrism, astrobio-semiotics, homo mensura, moral community, planetary sustainability and astrotheology perspectives.

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