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

Tiropanis, Thanassis, et al. Network Science, Web Science, and Internet Science. Communications of the ACM. 58/8, 2015. University of Southampton, Cambridge, Northwestern and Yale computer engineers including Wendy Hall first describe the generic presence and properties of scale-free networks as they are lately being found across every natural and social realm. The computer Internet and worldwide web, defined below, are similarly seen to exhibit these intrinsic, vivifying topologies. Although not noted, we add that since dynamic neural nets are often their archetypal instance, this global informative activity might readily be appreciated as a cerebral sapiensphere.

The observation of patterns that characterize networks, from biological to technological and social, and the impact of the Web and the Internet on society and business have motivated interdisciplinary research to advance our understanding of these systems. Their study has been the subject of Network Science research for a number of years. However, more recently we have witnessed the emergence of two new interdisciplinary areas: Web Science and Internet Science. (Abstract)

The Internet is a massive network of networks, a networking infrastructure. It connects millions of computers together globally, forming a network in which any computer can communicate with any other computer as long as they are both connected to the Internet. Information that travels over the Internet does so via a variety of languages known as protocols. The World Wide Web, or simply Web, is a way of accessing information over the medium of the Internet. It is an information-sharing model that is built on top of the Internet. The Web uses the HTTP protocol, only one of the languages spoken over the Internet, to transmit data. Web services, which use HTTP to allow applications to communicate in order to exchange business logic, use the Web to share information. (Webopedia)

Trotta, Roberto. Bayesian Methods in Cosmology. arXiv:1701.01467. An Imperial College London, Centre for Inference and Cosmology, Data Science Institute, researcher writes an 86 page tutorial on how to resolve data avalanches and measurement uncertainties by use of this iterative method to reach a good enough analysis and answer. One might describe it as a series of improved guesses that draw on prior experiences and results. See also, for example, Bayesian Mass Estimates of the Milky Way by Gwendolyn Eadie, et al (1609.06304).

These notes aim at presenting an overview of Bayesian statistics, the underlying concepts and application methodology that will be useful to astronomers seeking to analyse and interpret a wide variety of data about the Universe. The level starts from elementary notions, without assuming any previous knowledge of statistical methods, and then progresses to more advanced, research-level topics. After an introduction to the importance of statistical inference for the physical sciences, elementary notions of probability theory and inference are introduced and explained. Bayesian methods are then presented, starting from the meaning of Bayes Theorem and its use as inferential engine, including a discussion on priors and posterior distributions. Numerical methods for generating samples from arbitrary posteriors (including Markov Chain Monte Carlo and Nested Sampling) are then covered. The last section deals with the topic of Bayesian model selection and how it is used to assess the performance of models, and contrasts it with the classical p-value approach. (Abstract)

Truong, Timothy and Tristan Bepler. PoET: A Generative Model of Protein Families as Sequences of Sequences. arXiv:2306.06156. New York Structural Biology Center researchers (search BP) contribute an exemplary review of how current deep computational learning advances can serve to well parse both vital proteins and linguistic prose. In mid 2023, this historic spiral ascent is gaining a wide utility and benefit, see The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence at 2307.07522 for a latest global overview.

For more topical cases see RITA: A Study on Scaling up Generative Protein Sequence Models at 2205.05789, Large Language Models Generate Functional Protein Sequences in Nature Biotechnology (January 2023), AmadeusGPT: A Natural Language Interface for Interactive Animal Behavior (2307.04858) and Generative Language Models on Nucleotide Sequences of Human Genes (2307.10634).

Generative protein language models are a natural way to design biomolecules with new desired functions. However, current versions are difficult to produce, or need be trained on a multiple sequence alignment (MSA). To address this, we propose Protein Evolutionary Transformer (PoET), an autoregressive model of whole protein families that can generate sets of related proteins as sequences-of-sequences across millions of natural protein clusters. But PoET can be a retrieval-augmented model to score modifications on any protein family, and generalize even for small families. This is enabled by a unique Transformer layer; we model tokens sequentially within sequences while attending between sequences order invariantly, allowing PoET to scale to context lengths beyond those used during training. (Excerpt)

Proteins are large, complex molecules that play many critical roles in the body. They do most of the work in cells and are required for the structure, function, and regulation of the tissues and organs. Proteins are made up of hundreds or thousands of smaller units called amino acids. The sequence of amino acids determines each protein’s unique 3-dimensional structure and its specific function. (MedlinePlus)

Wagner, Caroline. The New Invisible College. Washington, DC: Brookings Institution Press, 2008. A senior analyst at the Center for International Science and Technology Policy, George Washington University, proposes that scientific research ought to be presently seen to proceed as a worldwide collaborative endeavor, which could be known by the book’s title. What is especial here is how the author’s informs this approach via nonlinear systems and dynamic network theory. As an example, individual researchers in a field such as seismology are shown to constantly interact and organize themselves as they explore and fill that subject landscape. Curiously Dr. Wagner makes little notice of its facilitation by online publication, data sharing, blogging, topical Wikis, and the like. But a step not taken anywhere is to realize that if these same characteristics of a thinking brain are found to equally grace a nascent global and local experience, then humankind might be imagined to possess a cerebral capacity just beginning to learn and know on its own. See Whitefield herein for a similar posting.

Chapter 4 uses quantitative data to establish that global science does indeed operate like a network and that this network is growing at a spectacular rate. The chapter also explains how the network expands by focusing on the motivations that drive the individuals who constitute it. It shows that the pattern of collaboration in a wide range of disciplines follows the scale-free distribution that is characteristic of complex adaptive systems and explorers the simple rules that generate such complexity. (10)

Wang, Daifeng, et al. Temporal Dynamics of Collaborative Networks in Large Scientific Consortia. Trends in Genetics. 32/5, 2016. As the title cites, Yale University Program in Computational Biology and Bioinformatics, including Mark Gerstein, quantify the presence of exemplary programs which underlie and guide even this realm of natural and social research itself.

The emergence of collective creative enterprise such as large scientific consortia is a unique feature in modern scientific research. We analyzed the temporal co-authorship network structures of ENCODE and modENCODE consortia. Our analysis revealed that the consortium members work closely as a community whereas non-members collaborate in the scale of a few laboratories. We also identified a few brokers playing an important role to facilitate collaborations with outside researchers. (Abstract)

Wang, Hanchen, et al.. Scientific discovery in the age of artificial intelligence. Nature. August 2, 2023. 28 information scholars from Stanford. MIT, Cornell, Georgia Tech, University of Illinois and beyond led by Marinka Zitnik (Harvard) including Yousha Bengio enter a major statement and survey of this historic turn and advance. At its early stage, various deep neural net capabilities, ideally under human guidance, can handle vast data flows, be trained to come up with new theories, facilitate collective intelligence and so on. See also The Rise of Open Science: Tracking the Evolution and Perceived Value of Data and Methods Link-Sharing Practice by Hancheng Cao, et al at arXiv:2310.03193 for another perspective and public methods.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not otherwise possible. Here we examine recent progress in generative AI such as design small-molecule drugs and proteins, analyze diverse data modalities, including images and sequences. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously. (Excerpt)

Webster, Charles. The Great Instauration. London: Duckworth, 1975. A classic work on the intellectual milieu of Francis Bacon in the mid 17th century whence it was thought the new scientific method through its discoveries and technologies, when brought into accord with ancient wisdom, would restore a lost Atlantean or Edenic age.

Wei, Dai-Jan, et al. Box-covering Algorithm for Fractal Dimension of Weighted Networks. Nature Scientific Reports. 3/3049, 2013. In our worldwide purview, we note this sample contribution, among a dozen posted each day by this British journal, to illustrate the scope and reach of global research in our electronic noosphere. Specialists in bioinformatics, engineering, knowledge discovery, and computation from Shanghai Jio Tong University to Vanderbilt University describe a common system topology which holds from mathematics and bacteria to airline flight patterns. Ever again an independent universality is implied from this exemplary presence everywhere.

Box-covering algorithm is a widely used method to measure the fractal dimension of complex networks. Existing researches mainly deal with the fractal dimension of unweighted networks. Here, the classical box covering algorithm is modified to deal with the fractal dimension of weighted networks. Box size length is obtained by accumulating the distance between two nodes connected directly and graph-coloring algorithm is based on the node strength. The proposed method is applied to calculate the fractal dimensions of the “Sierpinski” weighted fractal networks, the E.coli network, the Scientific collaboration network, the C.elegans network and the USAir97 network. Our results show that the proposed method is efficient when dealing with the fractal dimension problem of complex networks. (Abstract)

Whitesides, George. Reinventing Chemistry. Angewandte Chemistry International Edition. 54/3196, 2016. For an issue upon the 150th anniversary of BASF, the European chemical company, the veteran Harvard University chemist first reviews 20th century research methods. With the advent of revolutionary technologies and communications on a global scale, a grand new vista has opened by way of computation, information, energies, catalysis, nonlinear dissipative systems, and more. Both palliative mediations and creative enhancements promise a better life and bioplanet.

Wilson, Edward O. Consilience. New York: Knopf, 1998. The Harvard University entomologist and conservationist argues for a reunification of knowledge to complete the Enlightenment agenda. But the way it is to be achieved is by a materialist reduction to atomic levels.

…an organism is a machine.…the universe was not made with us in mind. (43) The central idea of the consilience world view is that all tangible phenomena, from the birth of stars to the workings of social institutions, are based on material processes that are ultimately reducible, however long and tortuous the sequence, to the laws of physics. (266)

Wise, M. Norton, ed. Growing Explanations: Historical Perspectives on Recent Science. Durham, NC: Duke University Press, 2004. An eclectic but timely volume that expresses how the various complexity sciences represent a profound shift in emphasis from physical reduction to emergent self-organization. For example, Alfred Tauber notes that immunology is moving from a self-other view to antibodies as fluid and distributed in a postmodern sense. Richard Doyle, Stefan Helmreich and Claus Emmeche go on to consider the digital simulations of artificial life. But a step not yet taken is to realize these novel approaches imply a quite different, organically developing universe.

Put in the broadest terms, “growing explanations” refers to what may be a sea change in the character of much scientific explanation. Over the past forty years, the hierarchy of the natural sciences has been inverted, putting biology rather than physics at the top, and with this inversion emphasis has shifted from analysis to synthesis. In place of the drive to reduce phenomena from higher-order organization to lower-lying elements as the highest goal of explanation, we see a new focus on understanding how elementary objects get built up – or better, are “grown up” – into complex ones…. (1) Instead of particles all the way down, it would be dynamics all the way up. (19)

Woese, Carl. A New Biology for a New Century. Microbiology and Molecular Biology Reviews. 68/2, 2004. The University of Illinois evolutionary theorist (1928-2012) finds biological science to have reached an epochal turning point and paradigm shift. The necessary 20th century approach of identifying the molecular, genetic and microbial components has fulfilled its task. But this results in an incomplete, mechanical view of nature. To move forward, a diametric integral vista is called for whereby life’s innate emergence is expressed by the new sciences of self-organizing complexity. And it is just this revolution that Natural Genesis is trying to report and convey. Woese’s important paper is also noted in Part V, A Quickening Evolution, and Part VI, Microbial Colonies.

The molecular cup is now empty. The time has come to replace the purely reductionist “eyes-down” molecular perspective with a new and genuinely holistic, “eyes-up,” view of the living world, one whose primary focus is on evolution, emergence, and biology’s innate complexity. (175) And it is becoming increasingly clear that to understand living systems in any deep sense, we must come to see them not materialistically, as machines, but as (stable) complex, dynamic organization. (176)

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