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

Khoury, Justin. Dark Matter Superfluidity. arXiv:2109.10928. We cite this entry by a University of Pennsylvania astrophysicist as a current example of collective Earthuman abilities to plumb and explore any depth and reach of cosmological phenomena. One wonders what kind of extant reality, as may become evident to us, proceeds to evolve its own facility of self-revelation and description. For whatever reason is this universal learning process going on. See also Boyle, Latham and Neil Turok. Two-Sheeted Universe, Analyticity and the Arrow of Time by Latham Boyle and Neil Turok at 2109.06204 and The Universe as a Driven Quantum System by Jose Vieira at 2109.01660 for other such scientific studies as they spiral to this global genius.

In these lectures I describe a theory of dark matter superfluidity developed in the last few years. The dark matter particles are axion-like, with masses of order eV. They Bose-Einstein condense into a superfluid phase in the central regions of galaxy halos. The superfluid phonon excitations in turn couple to baryons and mediate a long-range force (beyond Newtonian gravity). Thus the dark matter and modified gravity phenomena represent different phases of a single underlying substance, unified through the well-studied physics of superfluidity. (Abstract)

Kim, Edward and Robert Brunner. Star-Galaxy Classification Using Deep Convolutional Neural Networks. arXiv:1608.04369. We cite this entry by a University of Illinois physicist and an astronomer to show how an international collaborative community is uses cerebral methods to analyze vast amounts of data. In regard, one might assume a nascent global brain learning on its own. See also Deep Recurrent Neural Networks for Supernovae Classification by Tom Charnock and Adam Moss at 1606.07442 for another example.

Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine to automatically learn the features directly from data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep convolutional neural networks (ConvNets) directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep neural networks require very little, manual feature engineering. (Abstract)

Kostic, Andrija, et al. Machine-driven Searches for Cosmological Physics. Astronomy & Astrophysics. July 5, 2021. We cite this entry by MPI Astrophysics, Stockholm University, Niels Bohr Institute, and Sorbonne University researchers as a current example of how these novel computational facilities are opening a new empowerment by which our Earthkind survey can describe and quantify, as we seem unbeknownst, made and meant to carry forth. In regard, our home planet seems to be lately graced with a collaborative noosphere able to learn, know and discover on her/his own.

We present maps revealing the expected information content of large-scale structures concerning cosmological physics. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. This achievement has become feasible through the recent development of a causal inference method that is based on the physics of cosmic structure formation. The results presented in this work elucidate the inhomogeneous distribution of cosmological information in the Universe. . This study paves a new way forward to perform efficient searches for the fundamental physics of the Universe, where search strategies are become refined with new cosmological data sets within an active learning framework. (Abstract excerpt)

Krenn, Mario and Anton Zeilinger. Predicting Research Trends with Semantic and Neural Networks with an Application in Quantum Physics. arXiv:1906.06843. University of Vienna, Center for Quantum Science and Technology physicists MK, now a postdoc (search) at the University of Toronto, and AZ, an esteemed theorist since the 1970s (see Wikipedia and his website), apply their polymath acumen to this subject field. Circa 2019, it becomes evident that global scientific endeavors are going on by own their worldwide selves, independent of individual contributors. A Semantic Network software program is proposed by which to data mine the vast resultant literature such as this eprint site. An actual cerebral process (sapiensphere) coming to her/his own knowledge remains to be seen. With daily threats of a climate, and/or nuclear Armageddon, an agreed advent of a planetary phase of salutary edification, a natural discovery in our midst, is vitally necessary. See also Quantum Teleportation in High Dimensions by the authors and Chinese colleagues at 1906.09697.

The growing number of publications in all scientific disciplines can no longer be comprehended by a single human person. As a consequence, researchers have to specialize in sub-disciplines, which makes it challenging to uncover connections beyond the own field of research. In regard, access to structured knowledge from a large document corpus could help advance the frontiers of science. Here we demonstrate a method to build a semantic network from scientific literature, which we call SemNet. We use SemNet to predict future trends and to inspire new seeds of ideas in science. In SemNet, scientific knowledge is represented as an evolving node/link network using the content of 750,000 scientific papers published since 1919. Finally, we consider possible future developments and implications of our findings. (Abstract excerpt)

krenn, Mario, et al. Artificial Intelligence and Machine Learning for Quantum Technologies. Physical Review A.. 107/010101, 2023. MPI Science of Light physicists present an illustrated review to date of these Earthhuman and computational capabilities going forward which seem to so readily empower this new planetary phase.

In recent years the dramatic progress in machine learning has begun to impact many areas of science and technology. In our perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples of how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. (Abstract)

This perspective article will be concerned with shining a spotlight on how techniques
of classical machine learning (ML) and artificial intelligence (AI) hold great promise for improving quantum technologies in the future. A wide range of ideas have been developed at this interface between the two fields during the past five years; see Fig. 1. Whether one tries to understand a quantum state through measurements, discover optimal feedback strategies or quantum protocols, or design new experiments, machine learning can yield efficient solutions, optimized performance and, in the best cases, even new insights. (1)

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.

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