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

B. The Spiral of Science: Manican to American to Earthicana Phases

Lavin, Alexander, et al. Simulation Intelligence: Towards a New Generation of Scientific Methods. arXiv:2112.03235. In December 2021, some 23 leading authorities including Hector Zenil, David Krakauer and Kyle Cranmer came together to get a read and bead on an apparent major emergent transition of our historic learning process from a homo and anthropo endeavor to a current global Earthropocene sapience. The result is a 100 page document with 700 references. Its outline runs from Modules (Multi-Physics and Scale Models), Engines (Probabilistic Programs), Frontiers (Open-Ended Optimization) to Intelligent Simulation (Integrations) so as to scope out this Turing Turn to such worldwise abilities. The incent is to allow this advance but get in front of as an Earthuman endeavor that peoples can plan and program, while these computations may proceed on their own. (But still can 21 men and 2 women (Anima Anandkuman, Adi Hanuka) be able to imagine, seek and admit an actual discovery of a greater genesis, of which they are vital participants?)

The original "Seven Motifs" endeavor set forth essential methods for the field of scientific computing, where a motif is an algorithm for a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence" for the development and integration for a merger of scientific computing, scientific simulation, and artificial intelligence, (SI) for short. In regard, the motifs are interconnected and interdependent just as the layers of an operating system. We thus propose (1) Multi-physics and multi-scale models; (2) Surrogate emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; and (9) Machine programming. (Abstract excerpt)

Le-May Sheffield, Suzanne. Women and Science. Santa Barbara, CA: ABC-CLIO, 2004. A survey of the relation of women with scientific pursuits throughout history. Before the 16th and 17th scientific revolution, an organic cosmos reigned which by its nature could be encountered as well by women as men. As the rage to quantify took over, this became an exclusive male endeavor. But as a result the universe became mechanical and impersonal in kind. Only just now are women beginning to regain rightful respect and inclusion. As a reflection, we note how these two optional persuasions of machine or organism can take upon a gender basis, and could be integrated in a bicameral humankind.

In general, medieval scholars perceived the world as a living organism, nature as female, and male and female as two complementary parts of a greater whole. The spiritual realm was as equally available to study as the physical world and the two were inseparable from one another. (1-2)

LeGoff, Jacques. Intellectuals in the Middle Ages. Cambridge, MA: Blackwell, 1993. An essay on the early 13th to 15th century dawning humanist realization that our natural reality is amenable to rational study, indeed is made intelligible because macro universe and micro human hold a mirror to each other. But centuries later, this venerable, valid relation is lost, the quest abandoned before a sterile multiverse.

Leung, Henry and Jo Bovy. Towards an astronomical foundation model for stars with a Transformer-based model. arXiv:2308.10944. As the quote notes, University of Toronto astronomers describe an integral method by which to readily join and enhance vast data surveys as a large language document (parsing the parsecs?). Here more evidence of an historic turn to a global phase which could be seen as learning on her/his own. And once more an allusion to a natural textuality is suggested.

Rapid strides are currently being made in the field of artificial intelligence using Transformer-based models like Large Language Models (LLMs). The potential of these methods for creating a single, large, versatile model in astronomy has not yet been explored. In this work, we propose a framework for data-driven astronomy that uses the same core techniques and architecture as used by LLMs. Using a variety of observations and labels of stars as an example, we build a Transformer-based model and train it in a self-supervised manner with cross-survey data sets to perform a variety of inference tasks.

Levit, Georgy, et al. Alternative Evolutionary Theories: A Historical Survey. Journal of Bioeconomics. 10/1, 2008. The neoDarwinism that reigns especially in this 150th anniversary year was for many years but one version among a number of options. This cogent collation reviews the occasion and passage of Orthogenesis, Mutationism, Scientific Creationism, Lamarchism, Old-Darwinism, Idealistic Morphology, Saltationism, and Biosphere Theories (V. Vernadsky). But a sense of something lost and missed by today’s narrow view of life due to chance mutations and selection is also conveyed.

Li, Xiaomei, et al. A Conceptual Framework for Human-AI Collaborative Genome Annotation.. arXiv:2503.23691. This entry by seven Agriculture & Food, CSIRO, NSW, Australia and University of Leeds, UK researchers is another good instance of a dedicated reciprocity between individual persons and Earthwise computational neural facilities. In regard, though this best balance requires that a proper infrastructure be specifically put in place.

Genome annotation is essential for understanding the functional elements within them. While automated methods can process much genomic data, they have trouble predicting gene structures and functions. Consequently, expert curation remains crucial and shows as it integrates human expertise with AI capabilities to improve both the accuracy and efficiency. However, manual curation process is difficult to scale for large datasets. Here we propose a Human-AI Collaborative Genome Annotation (HAICoGA) method to enhance the synergistic partnership between humans and AI. (Excerpt)

Lin, Yi and Shoucheng OuYang. Irregularities and Prediction of Major Disasters. New York: Taylor & Francis/Auerbach Publications, 2010. A volume in the Systems Evaluation, Prediction and Decision-Making Series, with a cover image of Yin and Yang superimposed on a landform map. Chinese scientists with international educations and appointments survey the field of nonlinear dynamics and its application to sudden global and local atmospheric and geological events. But a chapter “Evolution Science” is of special note for it draws a unique contrast of Western and Eastern modes of science, from their own perspective. In this view, as often cited, the West historically emphasizes quantitative “numbers,” a discrete calculus and partition of disciplines. An Asian approach tends to nature and society as a unified, organic whole, whereof integral “events” are primary. By this distinction the Book of Changes is seen to offer an evolutionary theory long before us late comers. (And this may simply explain why women may not be enthused male math and physics, for they are impoverished and incomplete.)

Cultural differences are created by those existing in the geographical conditions and environments so that people employ different methods to solve their individual problems. Different methods of knowing and explorations naturally create different spheres of livelihoods and regional streams of consciousness. No doubt, in the learning atmosphere that not all things can numbers describe, in the Eastern philosophies, the Chinese would not have made laws of nature to depend solely on quantities. However, not solely depending on quantities does not mean that China has never had schools of learning. That is why the Chinese established their theory of yin and yang and that of five elements – the theory about states and mutual reactions of materials, which is a system of logical transformations of structural characteristics of materials. (357)

We should be clear that the Chinese phrase “universe” contains different meanings from the English words cosmos or universe. The Chinese “universe” (yu shou) means space (yu) and time (shou) together while the English cosmos and universe only mean space. The first character “yu” specifies materials occupying all imaginable fields in all directions; the second character “shou” stands for “coming from the past and heading into the future.” (365-366)

Liu, Jiazhen, et al. Correlated Impact Dynamics in Science. arXiv:2303.03646. University of Miami physicists including Chaoming Song move on to discern how an intrinsic presence of complex network structures can serve to organize and advance worldwide research studies as they now ascend and advance to a worldwise pursuit.

Science progresses by building upon previous discoveries. It is commonly believed that the impact of scientific papers, as measured by citations, is positively correlated with the impact of past discoveries built upon. However, analyzing over 30 million papers and nearly a billion citations across multiple disciplines, we find that there is a long-term positive citation correlation, but a negative short-term correlation. We demonstrate that the key to resolving this paradox lies in a new concept, called "capacity", which captures the amount of originality remaining for a paper. We find there is an intimate link between capacity and impact dynamics that appears universal across the diverse fields we studied. The uncovered capacity measure not only explains the correlated impact dynamics across the sciences but also improves our understanding and predictions of high-impact discoveries. (Abstract)

Liu, Wenyuan, et al. Predicting the Evolution of Physics Research from a Complex Network Perspective. Entropy. 21/12, 2019. We cite this entry by Nanyang Technological University, Singapore and Wroclaw University of Science, Poland computational intelligence researchers because it considers that the process of cumulative scientific inquiry and knowledge, which presently proceeds on a global scale, can be seen to exhibit and be treatable by the same dynamic multiplex networks as everywhere else. Here then is another way and reason to appreciate a planetary learning achievement due to humankinder altogether as it goes forth by itself. See also Knowledge Evolution in Physics Research: An Analysis of Bibliographic Coupling Networks by Wenyuan Liu, et al in PLOS One (September 2017). All of which quite accords with the intent of this Annotated Anthology website.

The advancement of science, as outlined by Popper and Kuhn, is largely qualitative, but with bibliometric data, it is possible to develop a quantitative picture of scientific progress. In this paper, we address this problem of quantitative knowledge evolution by analyzing the APS data sets from 1981 to 2010. We build the bibliographic coupling and co-citation networks to detect topical clusters (TCs), measure the similarity of TCs, and visualize the results as alluvial diagrams. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Our results represent the first step from a descriptive understanding of the science of science (SciSci), towards one that is ultimately prescriptive. (Abstract excerpt)

Liu, Zizhou, et al. The Mind in the Machine: A Survey of Incorporating Psychological Theories in LLMs. arXiv:2505.00003. Columbia University, Barnard College and Cambridge University infoscholars contribute to a current endeavors trying to get a better bead/read on where profligate ChatBot literacies and responses are coming from and going to. This Spiral turning (Turing) section is concerned with validating their personal Earthuman venue as an interactive, reciprocal sciencesphere, while the Earthificial module below is about their cognitive essence and content. See also Nature's Insight: A Comprehensive Analysis of Agentic Reasoning by Zinan Liu, et al at arXiv:2505.05515.

Psychological insights have long shaped Natural language processing by way of attention mechanisms, reinforcement learning, and Theory of Mind. behavior, and interaction. This paper reviews how psychological theories can help inform LLMs stages including pre-training, evaluation and application. Our survey integrates insights from cognitive, behavioral, social, personality psychology, and psycholinguistics. (Abstract)

Lloyd, Geoffrey and Nathan Sivin. The Way and the Word. New Haven: Yale University Press, 2002. The grand history of science takes two distinct paths from Greece and China with their emphasis on either analytical rationality, logos, or interrelational context, the Tao.

Lucio-Arias, Diana and Loet Leydesdorff. The Dynamics of Exchanges and References among Scientific Texts, and the Autopoiesis of Discursive Knowledge. Journal of Informetrics. 3/3, 2009. University of Amsterdam communication researchers propose a “literary model” whereof articles, books, and websites represent node-like entities of the global Internet. This approach then fosters the recognition in this domain of collective human learning of the same self-organizing autopioetic living systems found everywhere else across nature and society.

In the case of the science system, the selection mechanisms operate differently from markets or non-market exchange mechanisms. In this paper, we contribute to the theme of “a science of science” by using Maturana & Varela’s theory of autopoiesis or self-organization, and Luhmann’s theory of social systems for the specification of how different selection mechanisms are constructed from and feed back upon the process of scientific publishing in recursive loops. The “literary model” of science enables us to trace publications and their dynamics, and therefore operationalize these evolutionary theories. Both the networks (at each moment of time) and the self-referential loops (over time) can be expected to operate as distributions: uncertainty in these distributions can be measured. (261-262)

Discursive knowledge is generated by exchanges of codified elements among texts (e.g., specific arguments and references) in a next-order dynamics. Socio-cognitive regimes emerge at the supra-individual level from (i) streams of publications, (ii) their reflexive decompositions and reconstructions in discursive exchanges, and (iii) the consequent dynamics in their positions in the network of communications. (264-265)

By using the literary model for developing indicators, one obtains fruitful heuristics. The focus is no longer on historical cases and single events, but on distributions in sampled document sets. The distributions enable us to test observations against hypotheses. We proceed in the following sections by elaborating on (a) how the basic mechanism of growth and change are generated by the continuous streams of publications; (b) how intellectual structures emerge as specialties and self-organize, (c) how this self-organization can be operationalized to measure reduction of uncertainty in systems of scientific communication using configurational information; and (d) how the science system interacts with other social systems. (263)

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