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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

Draelos, Timothy, et al. Neurogenesis Deep Learning. arXiv:1612.03770. We note this posting by Sandia National Laboratory computational neuroscientists as another report about how neural, machine, algorithmic, computational, and probabilistic procedures are being applied from cosmology to chemistry to social media. See also for example Deep Learning with Dynamic Computation Graphs at arXiv:1702.02181.

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. (Abstract)

Dworkin, Jordan, et al. The Emergent Integrated Network Structure of Scientific Research. PLoS One. 14/4, 2019. A guiding premise for this website is a worldwide intellectual endeavor which is lately gaining revolutionary knowledge by its own sapient self. Its mission is to gather, report and document copious findings from cosmos to creativity. Here University of Pennsylvania neuroresearchers JD, Russ Shinohara, and Danielle Bassett indeed perceive an independent global learning process via many cumulative personal contributions. From their network neuroscience expertise, the dynamic process may appear to take on a cerebral topology. In regard, the prescient noosphere of Vladimir Vernadsky, Pierre Teilhard, and others in the last century seems at last in full manifestation.

Scientific research is often seen as individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex system of natural computation, in which information is obtained, generated, and disseminated more effectively than by isolated individuals. But the structure of this integrated, innovative landscape of scientific ideas is not well understood. Here we use network science to map the landscape of interconnected topics covered in the multidisciplinary journal Proceedings of the National Academy of Sciences since 2000. In regard, nodes represent topics of study and edges give the degree to which they occur in the same papers. The network displays small-world architecture, with dense connectivity within scientific clusters and sparse connectivity between clusters. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight. (Abstract excerpt)

Eisler, Riane. Toward an Empathic Science. Chaisson, Eric and T.-C. Kim, eds. The Thirteenth Labor.. Amsterdam: Gordon and Breach, 1999. Eisler recommends an educational approach that is not skewed to male warfare and control but founded upon humanistic and communal values.

Elliott, Kevin, et al. Conceptions of Good Science in Our Data-Rich World. BioScience. 66/10, 2016. In this lead journal of the American Institute of Biological Sciences, Michigan State University naturalists and philosophers consider a revised methodology suitable for a 21st century proliferation of informational bytes which require ways to constrain, organize them so as to convey significant findings.

Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin's research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms. (Abstract)

Fernandez, Elisco. Taking the Relational Turn. Biosemiotics. 3/2, 2010. A succinct study of this epochal, welling shift. The vested “nominalist” view that only objects exist, implicit for the past centuries of science, is now in eclipse by a reassembly of nature in many areas (albeit necessary to first find all the pieces) into viable, dynamic systems. Such interrelated networks are then seen to be suffused by an informational essence as they constantly communicate. In support, prescient precursors of the Relational Biology of Robert Rosen, the Relational Quantum Mechanics of Carlo Rovelli, and Charles Peirce’s semiotic philosophy are enlisted.

A cluster of similar trends emerging in separate fields of science and philosophy points to new opportunities to apply biosemiotic ideas as tools for conceptual integration in theoretical biology. I characterize these developments as the outcome of a “relational turn” in these disciplines. They signal a shift of attention away from objects and things and towards relational structures and processes.

Relational developments in biology bear a telling similarity to a parallel relational turn presently manifest in the philosophy of science, rooted in the philosophy of physics and mathematics and in different varieties of structural and informational realism. The recognition of the relational nature of reality within these disciplines entails a tacit repudiation of nominalistic biases in science that have hindered the reception of semiotic conceptions in biology.

Ferris, Timothy. The Science of Liberty: Democracy, Reason, and the Laws of Nature. New York: HarperCollins, 2010. The science journalist here astutely draws parallels between the experimental pursuit of natural and technical knowledge and a social milieu that permits and endorses such free pursuit. The term “liberal,” which Ferris often employs, is equated in this case with a popular openness to creative inquiry. (Other broad uses, such as by FDR and JFK, would include social justice or tolerant welfare, now under virulent attack from the right.) The result is a cogent chronicle of the past centuries of scientific advance, seen to go hand in hand with progressive democratic societies. I found his chapter on Academic Antiscience about the postmodern ridicule of such endeavors to be most lucid amongst this murky episode. The next chapter, One World, goes on to offer a succinct review of Islamic theologies. But, as a spate of new books, e.g. Sean M. Carroll, Chris Impey, and Marcelo Gleiser, take as a default and I wonder if clear thinker TF realizes this, the whole project is undercut, as per the quote, by the acceptance, even acquiescence, of a pointless, moribund nature, a random physical cosmos with no place for people or knowledge.

So to wonder, “Why am I here?” is to ask the wrong question. Nothing requires that you or I exist, or that the human species exist; it’s just that so long as there is life on Earth some creatures will exist, and you and I happen, at present, to be among them. Evolution reveals that human got here the way everything else got here, through a long historical process of accident and selection. (263)

Floris Cohen, H. The Scientific Revolution. Chicago: University of Chicago Press, 1994. A treatise that integrates many sources to equate science with a precise “mathematization” whence numbers predominate over words in the natural text, which then resulted in a machine model. By the 18th century, researchers had lowered their expectations from overarching explanations to collecting voluminous minutiae of data.

Fortunato, Santo, et al. Science of Science. Science. 359/1007, 2018. A 14 member team including Katy Borner, Dirk Helbing, Filippo Radicchi and Albert-Laszlo Barabasi provide a strong statement to date about how this international collaborative endeavor can be well characterized by the same dynamic, self-organizing complex network system theories as everywhere else. Akin to cerebral cognition on a group and global scale (while not overtly said), this approach and identity can help identify better methods and techniques, aid project design, and hasten discovery. And here is an affirmation of our website premise, whence an emergent humankinder is to be appreciated as coming to her/his own revolutionary knowledge.

The increasing availability of digital data on scholarly inputs and outputs—from research funding, productivity, and collaboration to paper citations and scientist mobility—offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems.

Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. (First Page)

Franceschet, Massimo. The Large-Scale Structure of Journal Citation Networks. Journal of the American Society for Information Science and Technology. Online February, 2012. Within the burst of such noosphere studies reported herein, in another example, a University of Udine, Italy, mathematician proceeds to reconceive the field of bibliometrics by way of integral network dynamics. Three levels of interest are then node, group, and network. And in all these contributions, beyond abstract terms, the formation of an enveloping worldwide brain by the same stratified neural network geometries seems undeniable, waiting to be realized, and availed.

Frangsmyr, Tore, et al, eds. The Quantifying Spirit in the 18th Century. Berkeley: University of California Press, 1990. On the proliferation of instrumentation that served to identify and measure everything from chemicals to clouds.

Gates, Alexander, et al. Nature’s Reach: Narrow Work has Broad Impact. Nature. 575/32, 2019. As a contribution to this premier scientific journal’s 150th anniversary, Northeastern University network theorists including Albert-Laszlo Barabasi quantify and explain the long course from individual rudiments to today’s global, multiple coauthor, research teams. See the Science of Science article by Santo Fortunato, et al (search) from this group as a 2018 technical document. As we near 2020, how might it dawn (as time runs out) that this dynamic, instant, worldwide, emergent endeavor is presently gaining knowledge on her/his own.

Gau, Remi, et al. Brainhack: Developing a Culture of Open, Inclusive, Community-driven Neuroscience. Neuron. 109/11, 2021. As an example of how real and responsive the nascent global knowledge repository actually is, just three words – brainhack, Gau and Neuron – could instantly access the full article. Herein some fifty researchers across Europe, Australia and the USA describe an active medium for on-going discussions with regard to our collective project to retrospectively quantify and learn all about the homo sapiens cerebral faculty from which it all arose. At what point, by what vista, as this site tries to broach, could we altogether witness, and achieve an ecosmic self-discovery, as if a second singularity, so as for EarthKinder to begin a new self-cocreation?

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

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