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

Ellis, John, et al. Ellis, John, et al. Do we Owe our Existence to Gravitational Waves?. arXiv:2402.03593. John Ellis, Kings College London, Brian Fields, Center for Advanced Studies of the Universe, University of Illinois, and Rebecca Surman, University of Notre Dame engage a novel imagination, akin to Simon Conway Morris, which is not constrained by the vested verdict that no other reality exists on its own. Rather by way of an integral vista, certain celestial elements and conditions can be seen to have a necessary relation to our considerate presence, This is not “anthropic” as much as an ability to perceive independent phenomena without which human beings could not be here. Our point is that just a mindfulness to allow the very possibility, which has been excluded for the past male century, can reveal an innately animate genesis.

Two heavy elements essential to human biology are thought to have been produced by the astrophysical r-process, which occurs in neutron-rich environments: iodine is a constituent of thyroid hormones that affect many physiological processes, and bromine is essential for tissue development. Collisions of neutron stars (kilonovae) have been identified as sources of r-process elements including tellurium, which is adjacent to iodine in the periodic table, and the lanthanides. Neutron-star collisions arise from energy loss due to gravitational-wave emission, leading us to suggest that they have played a key role in enabling human life by producing these certain elements. (Abstract)

In nuclear astrophysics, the rapid neutron-capture process, also known as the r-process, is a set of nuclear reactions that is responsible for the creation of approximately half of the atomic nuclei heavier than iron, the "heavy elements"

I am fascinated by the "inner space/outer space" connections that link the science at the smallest and largest scales. My research focusses on highest-energy sites in such as the big bang, exploding stars (supernovae), and particles in space (cosmic rays). Understanding these processes and their interplay allows us to trace the history of matter over a temporal expanse. (Brian Fields)

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

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.

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.

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.

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.

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)

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)

Wang, Yu, et al.. Can AI Understand Our Universe? Test of Fine-Tuning GPT by Astrophysical Data. arXiv:2404.10019. Eight computational astroscientists posted Italy, Armenia, China and Iran evaluate and compare guided applications of these current AI frontiers to data-intensive celestial studies. In their careful procedure they do find a valuable enhancement to an extent to define a new spiral phase of planetary human-computer teamwork.

As astro-researchers, we are curious about if scientific data can be correctly analyzed by large language models (LLMs) and yield accurate physics. In this article, we fine-tune the generative pre-trained transformer (GPT) model from the observations of galaxies, quasars, stars, gamma-ray bursts (GRBs), and the black holes (BHs), whereby the our model is able to classify astrophysical phenomena, distinguish between two types of GRBs, deduce the redshift of quasars, and estimate BH parameters. With the volume of multidisciplinary data and the advancement of AI technology, we look forward to a deeper comprehensive understanding of our universe. This article also shares some interesting thoughts on data collection and AI design. Using the approach of understanding the universe as a guideline, we propose a method of series expansion for AI, suggesting ways to train and control AI that is smarter than humans. (Abstract)

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

Hamilton, Chris and Jean-Baptiste Fouvry.. Kinetic Theory of Stellar Systems. arXiv:2402.13322. IAS, Princeton and Sorbonne University astrophysicists contribute a 66 page, 182 reference Tutorial as a latest statement of the dynamic sunny stars. Nine segments such as Orbits in mean field potentials well covers the technical content.

Stellar systems - star clusters, galaxies, dark matter haloes, and so on - are ubiquitous characters in the evolutionary tale of our Universe. This tutorial article is an introduction to the collective dynamical evolution of the very large numbers of stars and/or other self-gravitating objects that comprise such systems, i.e. their kinetic theory.

Saarloos, Win van, et al. Soft Matter: Concepts, Phenomena, and Applications. Princeton: Princeton University Press, 2024. Wim van Saarloos is professor emeritus of theoretical physics at the Lorentz Institute at Leiden University, Vincenzo Vitelli is professor of physics at the University of Chicago and Zorana Zeravcic is professor of physics in the Gulliver Laboratory at ESPCI Paris. In regard they contribute the first book treatment of this animate subject, hardly a decade old. A chapter on Active Matter is included along Non-Equilibrium Pattern Formation, Elasticity, Designing Matter and so on. Altogether one more perspective upon a natural dynamic liveliness due to common codings gains a broad and deep expression.

Soft matter science is an interdisciplinary field at the interface of physics, biology, chemistry, engineering, and materials science. It encompasses colloids, polymers, and liquid crystals as well as rapidly emerging topics such as metamaterials, memory formation and learning in matter, bioactive systems, and artificial life.. The presentation integrates statistical mechanics, dynamical systems, and hydrodynamic approaches with conservation laws and broken symmetries as guiding principles along with computational and machine learning advances.

Ambjørn, Jan and Y. Watabiki.. Is the present acceleration of the Universe caused by merging with other universes? Journal of Cosmology and Astroparticle Physics. 12/11, December, 2023. In these years, Niels Bohr Institute, Copenhagen and Tokyo Institute of Technology (search JA) theorists are able to extend their mathematical conjectures to the farthest reaches of spacetime so to quantify, explain and wonder about these farthest multiversal realms.

"The main finding of our work is that the accelerated expansion of our universe, caused by the mysterious dark energy, might have a simple intuitive explanation, the merging with so-called baby universes, and that a model for this might fit the data better than the standard cosmological model," Jan Ambjørn, told LiveScience. The researchers' calculations showed that merging with other universes should increase the volume of our universe, which could b horizon.e perceived by our instruments as an expansion of the universe. (JA to LiveScience)

Wang, Jai, et al. Interstellar formation of glyceric acid, the simplest sugar.. Science Advances. March 24, 2024. University of Hawaii and University of Mississippi (Ryan Fortenberry) cite their sweet detection of this organic mainstay compound. As the quotes imply, once again nature’s astrochemistry seems to possess an innate spontaneity to form just what life needs for the long cellular ovogenesis to our late retrospect description.

Glyceric acid [HOCH2CH(OH)COOH] is a key molecule in biochemical metabolic processes such as glycolysis. Although linked to the origins of life and identified in carbonaceous meteorites, the mechanisms of its formation have remained elusive. Here, we report the first abiotic synthesis of racemic glyceric acid via the radical-radical reaction of the hydroxycarbonyl radical with 1,2-dihydroxyethyl radical in low-temperature carbon dioxide and ethylene glycol ices. This work reveals the key pathways for glyceric acid synthesis through nonequilibrium reactions from profuse precursor molecules, advancing our fundamental knowledge of the formation of key biorelevant organics—sugar acids—in deep space. (Abstract)

Here, we demonstrate the very first abiotic synthesis of 1 in low-temperature (5 K) carbon dioxide and ethylene glycol (HOCH2CH2OH, 16) ice mixtures. This was accomplished via the barrierless radical- radical eaction of the hydroxycarbonyl (HOĊO, 11) with the 1,2-dihydroxyethyl (HOĊHCH2OH, 17) radicals (Figs. 1 and 2). These model ices were exposed to energetic electrons mimicking secondary electrons generated in the track of galactic cosmic rays (GCRs) pene-trating ices in cold molecular clouds aged a few million years. (1)

Ziurys, Lucy. Prebiotic Astrochemistry from Astronomical Observations and Laboratory Spectroscopy. Annual Review of Physical Chemistry. Volume 75, 2024. As the quote notes, a senior University of Arizona bioastronomer contends that the profuse ISM population of appropriate biomolecule precursors found so far must have made a vital contribution to the origin and occurrence of nascent Earth life and evolution. See also RNA-catalyzed evolution of catalytic RNA by Nikolaos Papastavrou, et al in PNAS (121/11, 2024) and Complex organic molecules uncover deeply embedded precursors of hot cores by Laure Bouscasse, et al at arXiv:2403.05237 for more evidence of a natural life-bearing spontaneity. Altogether these findings suggest that our worldwise scientific quest may have at last reached an actual realization of a phenomenal ecosmic fertility which proceeds with its own procreative development.

The discovery of more than 200 gas-phase chemical compounds in interstellar space has led to the speculation that this nonterrestrial synthesis may play a role in the origin of life. Interstellar chemistry produces a wide range of organic molecules in dense clouds such as NH2COCH3, CH3OCH3, CH3COOCH3, and CH2(OH)CHO. Elusive phosphorus has now been found in molecular milieu and the sites of star formation. The presence of fertile interstellar starting material, as well as the link to planetary bodies such as meteorites and comets, suggests that astrochemical processes set a prebiotic foundation. (Abstract)

Coelho, Liqia, et al. Purple is the new green: biopigments and spectra of Earth-like purple worlds.. arXiv:2404.10105. Cornell University and University of Minnesota astroscientists including Lisa Kaltenegger (See also her new book Alien Earths: The New Science of Planet Hunting in the Cosmos) expand and extol the frontiers of exoplanet searches as they go forward with new telescopes and instruments. They scope and inspire a grand quest for other neighbors, which requires a widest allowance for what forms and features may be possible.

With more than 5500 detected exoplanets, the search for life is entering a new era. Using life on Earth as our guide, we look beyond green landscapes to expand our ability to detect signs of surface life on other worlds. While oxygenic photosynthesis gives rise to green landscapes, bacteriochlorophyll-based anoxygenic phototrophs can also color their habitats. Here, we characterize the reflectance spectra of purple sulfur and purple non-sulfur bacteria from a variety of anoxic and oxic environments. Our biological pigment data base for purple bacteria and the high-resolution spectra of Earth-like planets, including ocean worlds, snowball planets, and frozen worlds are available online, providing a tool for modellers and observers to train retrieval algorithms, optimize search strategies, and inform models.

Herbort, Oliver, et al. Habitability constraints by nutrient availability in atmospheres of rocky exoplanets. arXiv:2404.04029. University of Vienna, Austrian Academy of Sciences, and Marble Space Institute of Science, USA astroresearchers provide one more finely cast factor as just the right bioatomic mix in a watery solvent and moist air so as to engender relative organic combinations.

Life as we know it requires liquid water and nutrients, which are mainly based on the elements C, H, N, O, P, and S and trace metal micronutrients. In this paper we introduce a framework of nutrient availability levels based on the presence of water condensates and the chemical state of the CHNOPS elements.. The atmospheric model is a bottom-to-top equilibrium chemistry phase which includes the atmosphere-crust interaction and the element depletion due to the formation of clouds. While the reduced forms of CNS are present at the water cloud base for most atmospheric compositions, P and metals are lacking. (excerpt)

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