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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

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

Krenn, Mario, et al. SELFIES: A Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry. arXiv:1905.13741. MK is now with coauthor Alan Aspuru-Guzik’s University of Toronto group. The Semantic Network machine learning approach he developed in Vienna with Anton Zeilinger (search) is employed along with graphic plots so as to distill themes, paths, and advances as the field of chemical research proceeds as a worldwide endeavor. The presence of a global activity going on by itself is quite evident, which is an historic shift beyond individuals and teams.

Graphs are ideal representations of complex, relational information. Their applications span diverse areas of science and engineering. Recently, many of these examples turned into the spotlight as applications of machine learning (ML). While much progress has been achieved in the generation of valid graphs for domain- and model-specific applications, a general approach has not been demonstrated. Here, we present a sequence-based, robust representation of semantically constrained graphs, which we call SELFIES (SELF-referencIng Embedded Strings), based on a Chomsky type-2 grammar, augmented with two self-referencing functions. SELFIES are not limited to the structures of small molecules, and we show how to apply them to two other examples from the sciences: representations of DNA and interaction graphs for quantum mechanical experiments. (Abstract excerpt)

Kuhn, Thomas. The Structure of Scientific Revolutions. Chicago: University of Chicago Press, 1970. The classic book that identified how a reigning “paradigm” or worldview characterizes an intellectual age which then governs its research protocol and often the resultant society. Kuhn observed that when two comprehensive systems of thought vie due to findings that cannot be forced into the older model and when senior scientists take opposite sides, a radical “paradigm shift” is imminent. This situation surely fits our moment when the mechanical, expiring cosmos is being superseded by an organically self-organizing genesis.

Kuhn, Tobias, et al. Inheritance Patterns in Citation Networks Reveal Scientific Memes. Physical Review X. 4/041036, 2014. With Matjaz Perc and Dirk Helbing, Swiss and Slovenian scientists extol how publications such as the Web of Science and Physical Reviews adhere to similar topologies as genomes.

Kumar Pan, Raj, et al. The Evolution of Interdisciplinary in Physics Research. arXiv:1206.0108. Online August 2012. We note because Kumar Pan, Kimmo Kaski, and Jari Saramaki, Aalto University School of Science, Finland, and Sitabhra Sinha, Institute of Mathematical Sciences, CIT Campus, India, systems scientists seem to articulate the same dynamic modular networks for this evolving cross-connection in physics that are being found, almost word for word, in descriptions of how an individual human brain develops knowledgable cognitive faculties. Compare, e.g., with Bassett, Danielle, et al. “Dynamic Reconfiguration of Human Brain Networks During Learning,” (PNAS, 2011) in Systems Neuroscience.

In this paper, we focus on the dynamics and emergence of connections between the various subfields of physics, and perform a longitudinal analysis of the evolution of physics from 1985 till 2009. Our results are based on a study of the papers appearing in the Physical Review series of journals published by the American Physical Society during this period, with their Physics and Astronomy Classification Scheme (PACS) numbers indicating the subfields of physics to which they belong. If a paper is listed under two different PACS codes, the two corresponding subfields are considered to be connected by the paper. In this manner we construct a set of annual snapshots of the networks of subfields in physics that are connected through all papers that have been published in each year, and study the evolution of these networks at multiple structural scales. In this way, we can focus on the big picture of the evolution of physics in terms of changes in the nature of connections between its subfields, instead of the microscopic level that is considered by the widely studied collaboration or citation networks. We show that the network of the subfields of physics is becoming increasingly connected over time, both in terms of link density and the numbers of papers joining different subfields. (1)

Latour, Bruno. A Plea for Earthly Sciences. http://www.bruno-latour.fr/articles/article/102-BSA-GB.pdf. The 2007 keynote address to the British Sociological Association by the French sociologist of science and polemic author, which cites James Lovelock’s The Revenge of Gaia to say that at this terminal time the science war arguments need be set aside before an integral vista that can foster a biosphere viability. Together with Science and Technology Studies scholars Michel Callon and John Law, Latour has also conceived an “Actor-Network Theory” (Google) with a “material-semiotic” basis that involves agental elements and conceptual interrelations so as to form a coherent whole. Which again seems to evoke ubiquitous complex adaptive systems, although one can find no reference in their writings.

But, as Jacqueline Holzer (herein) notes, in late post/modernity an academics do persist in their struggle to admit ‘something out there’ with its own self-existence, as a ‘real ground’ upon which to build theories. Latour indeed worries in his final paragraph over “a world entirely devoid of meaning.” We might propose that a sentient shift to a truly planetary domain of knowledge via a cerebral humanity, an accompanying noosphere, is imperative. See also Bruno's 2009 apropos essay "Will Non-Humans be Saved? An Argument in Ecotheology" in the Journal of the Royal Anthropological Institute (15/3).

Laughlin, Robert and David Pines. The Theory of Everything. Proceedings of the National Academy of Sciences. 97/1, 2000. Rather than continue to search for a bottom, elemental level in physics, a new century of synthesis ought to be based on the emergent hierarchy of nature.

….we are now witnessing a transition from the science of the past, so intimately linked to reductionism, to the study of complex adaptive matter, firmly based in experiment, with its hope of providing a jumping-off point for new discoveries, new concepts, and new wisdom. (30)

Laughlin, Robert., et al. The Middle Way. Proceedings of the National Academy of Sciences. 97/1, 2000. Theoretical principles inform atomic and cosmic domains but the mesoscopic realm of life is so far bereft of lawful behavior. This may be remedied by applying theories of self-organization first found in the small and large infinities.

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.

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