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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
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IV. Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems

2. Biteracy: Natural Algorithmic Computation

Feinerman, Ofer and Amos Korman. Theoretical Distributed Computing Meets Biology. Hota, Chittaranjan and Pradip Srimani, eds. Distributed Computing and Internet Technology. Berlin: Springer, 2013. Weizmann Institute and University of Paris computer scientists who explore “complex physical and biological systems” provide an introductory review of this promising get together. Akin to life’s roots reaching deeper into matter while this substrate become more fertile, as computational processes are informed by natural inspirations, so evolutionary studies are coming to realize that some software-like iterative process is at original work.

Fekete, Sandor, et al. Algorithmic Foundations of Programmable Matter. Bulletin of the European Association for Theoretical Computer Science EATCS. 122/June, 2017. A summary of a Dagstuhl Seminar with this title held in Wadern, Germany. Some papers are Claytronics, Self-Organizing Particle Systems, Algorithmic Design, Dynamic Networks, Amoebots, and Programmable Living Matter.

A summary of a Dagstuhl Seminar with this title held in Wadern, Germany. Some papers are Claytronics, Self-Organizing Particle Systems, Algorithmic Design, Dynamic Networks, Amoebots, and Programmable Living Matter.

Fernandez, Jose and Francisco Vico. AI Methods in Algorithmic Composition. Journal of Artificial Intelligence Research. Volume 48, 2013. This entry by University of Malaga, Spain computer scientists is cited in A. Wagner’s Life Finds a Way (2019) to show how evolution seems guided by source programs which can be modeled by artificial neural networks. By such perceptions, the natural presence of iterative cellular automata and self-similar patterns can be noticed. Its mathematical form and flow also appear as a musical or written composition. In regard, are we coming upon an proactive ecosmos which is composing itself by way of sapient species as our global own? Please visit F. Vico’s website to read about his “Melomics” or genetics of melody project.

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence. (Abstract)

The purpose of this survey is to review and bring together existing research on a specific style of Computational Creativity: algorithmic composition. Interpreted literally, algorithmic composition is a self-explanatory term: the use of algorithms to compose music. (1)

Ferrante, Eliseo, et al. Evolution of Self-Organized Task Specialization in Robot Swarms. PLoS Computational Biology. August, 2915. We enter this paper by a team of Belgian and Turkish researchers for both its notice of how nature is found to employ a spontaneous division of labor everywhere, and as science become cognizant of such a universal quality, we peoples can intentionally carry it forward unto a better world and creation.

Many biological systems execute tasks by dividing them into finer sub-tasks first. This is seen for example in the advanced division of labor of social insects like ants, bees or termites. One of the unsolved mysteries in biology is how a blind process of Darwinian selection could have led to such highly complex forms of sociality. To answer this question, we used simulated teams of robots and artificially evolved them to achieve maximum performance in a foraging task. We find that, as in social insects, this favored controllers that caused the robots to display a self-organized division of labor in which the different robots automatically specialized into carrying out different subtasks in the group. Remarkably, such a division of labor could be achieved even if the robots were not told beforehand how the global task of retrieving items back to their base could best be divided into smaller subtasks. This is the first time that a self-organized division of labor mechanism could be evolved entirely de-novo. In addition, these findings shed significant new light on the question of how natural systems managed to evolve complex sociality and division of labor. (Author Summary)

Freitas, Diogo, et al. Particle Swarm Optimization: A Historical Review Up. Entropy. 22/3, 2020. University of Madeira, Portugal computer engineers survey many ways since the 1990s that this mathematic model of how natural evolutionary systems finesse and optimize iterative solutions has found practical utility. It is currently being joined with and enhanced by artificial neural networks for even more applications. Altogether the review implies and conveys the computational source that guides our life and community.

Exponential growth in data generation and big data science has created an imperative for low-power, high-density information storage. This need has motivated research into multi-level memory devices capable of storing multiple bits per device because their memory state is intrinsically analog. Furthermore, much of the data they will store, along with the subsequent operations, are analog-valued. However the current storage paradigm is quantized for use with digital systems. Here, we recast storage as a communication problem, which allows us to use ideas from analog coding and show that analog-valued emerging memory devices can achieve higher capacities.. (Abstract excerpt)

Gilpin, William. Cellular Automata as Convolutional Neural Networks. arXiv:1809.02942. The Stanford University physicist runs these computational programs in accord with dynamical systems theory to an extent that the results begin to look like cognitive architectures. See also his concurrent paper Cryptographic Hashing using Chaotic Hydrodynamics in the PNAS. (115/4869, 2018).

Gosciniak, Ireneusz. Semi-Multifractal Optimization Algorithm. Soft Computing. 23/5, 2019. A University of Silesia, Poland computer scientist illustrates that self-similar geometries can be seen to appear even in these software program iterations.

Observations on living organism systems are the inspiration for the creation of modern computational techniques. The article presents an algorithm implementing the division of a solution space in the optimization process. A method for the algorithm operation controlling shows the wide range of its use possibilities. The article presents properties of fractal dimensions of subareas created in the process of optimization. The paper also presents the possibilities of using this method to determine function extremes. The approach proposed in the paper gives more opportunities for its use. (Abstract)

Gregor, Karol and Frederic Besse.. Self-Organizing Intelligent Matter: A Blueprint for an AI Generating Algorithm. arXiv:2010.07627. DeepMind, UK computer theorists pick up on a 2019 paper by Jeff Clune (University of Wyoming) entitled Ai-generating Algorithms as an Alternate Paradigm for General Artificial Intelligence (1905.10985) about a better natural basis for evolutionary computation. The authors continue and enhance this method by way of further perceptions of life’s origin and complex, quickening course as a prime exemplar. In respect, this approach can provide another window upon of some manner of computational program and process at work in animate generation.

We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms. In this framework there is no explicit notion of an agent: instead there is an environment made of atomic elements. These elements contain neural operations and interact through exchanges of information and through physics-like rules contained in the environment. We discuss how an evolutionary process can lead to the emergence of different organisms made of many such atomic elements which can coexist and thrive in the environment. (Abstract)

An AI generating algorithm is a computational system that runs by itself without outside interventions and after a certain amount of time generates intelligence. Evolution on earth is the only known successful system thus far that we know of. (1) In this paper we proposed a framework for achieving intelligence by evolutionary process in an environment that is built out of interacting elements implementing computationally efficient and general learning. (9)

Hein, Andrew, et al. Natural Search Algorithms as a Bridge between Organisms, Evolution, and Ecology. Proceedings of the National Academy of Sciences. 113/9413, 2016. A team from Princeton and MIT, including Simon Levin, is in quest of commonalities between creaturely explorations of niche environments for optimum resources and reproduction. A synthesis of cellular and animal strategies is broached as a convergent dynamic state.

The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reproduce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology. (Abstract)

Hernandez-Orozco, Santiago, et al. Algorithmically Probable Mutations Reproduce Aspects of Evolution, such as Convergence Rate, Genetic Memory and Modularity. Royal Society Open Science. August, 2018. Algorithmic Dynamics Lab, SciLifeLab, Centre for Molecular Medicine, Stockholm computational scientists take up Gregory Chaitin’s polymath project (search) to quantify a deep mathematical source for life’s long evolution. Co-author Hector Zenil, with many colleagues including Chaitin, have pursued this task with technical finesse for some time (search here and arXiv eprints). As the quotes refer, this entry makes a strong case, which eludes an extended evolutionary synthesis (Laland), that selective effects alone are insufficient to adequately explain an oriented biological emergence from origins to us. This radical expansion is at odds with the vested Darwinian paradigm, but understandable (we add) if located within a procreative organic ecosmos as its consequent natural genetic code. Here the contrast is set between a “classical” randomness and a novel algorithmic, informational guidance. Similar work, such as by Sara Walker, Paul Davies and others, also explore ways to qualify and integrate this vital missing dimension. See also a concurrent paper An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems by Zenil and seven collaborators at arXiv:1709.05429. For more see Reprogramming Matter, Life, and Purpose by Zenil (2017) in Cosmocene Destiny.

Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random mutations. Here, we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations in various examples, particularly binary matrices, in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistically uniform but algorithmically uniform. We show that algorithmic distributions can evolve modularity and genetic memory by preservation of structures when they first occur sometimes leading to an accelerated production of diversity but also to population extinctions. These results validate some suggestions in the direction that computation may be an equally important driver of evolution. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to accelerate convergence of artificial evolutionary algorithms. (Abstract excerpts)

On the other hand, random mutation implies no evidence for a directing force and in artificial genetic algorithms, mutation has traditionally been uniform even if other strategies are subject to continuous investigation and have been introduced as a function of, for example, time or data size. More recently, it has been suggested that the deeply informational and computational nature of biological organisms makes them amenable to being studied or considered as computer programs following (algorithmic) random walks in software space, that is, the space of all possible—and valid—computer programs. Here, we numerically test this hypothesis and explore the consequences vis-a-vis our understanding of the biological aspects of life and natural evolution by natural selection, as well as for applications to optimization problems in areas such as evolutionary programming. (2)

We introduce a conceptual framework and an interventional calculus to steer, manipulate, and reconstruct the dynamics and generating mechanisms of dynamical systems from partial and disordered observations based on the algorithmic contribution of each of the systems elements to the whole by exploiting principles from the theory of computability and algorithmic randomness. This calculus entails finding and applying controlled interventions to an evolving object to estimate how its algorithmic information content is affected in terms of positive or negative shifts towards and away from randomness in connection to causation. The approach is an alternative to statistical approaches for inferring causal relationships and formulating theoretical expectations from perturbation analysis. We find that the algorithmic information landscape of a system runs parallel to its dynamic landscape, affording an avenue for moving systems on one plane so they may have controlled effects on the other plane. (1709.05429 Abstract)

Hillberry, Logan, et al. Entangled Quantum Cellular Automata (QCA), Physical Complexity, and Goldilocks Rules. arXiv:2005.1763. We cite this entry by a nine member team based at UT Austin, CalTech, and the University of Padova including Nicole Yunger Halpern as a current example of how disparate classical and quantum domains along with mathematic computations are joining up and cross-informing on the way to a phenomenal synthesis. The Abstract and quote convey a essential sense of the frontier project. So into the 2020s can we begin to realize (again) that an extant nature does have its own encoded reality and procreative purpose that we peoples can philosophize about? A good part of the project would be to translate the arcane terms into a human-familial image, which is what this site attempts to do.

Cellular automata are interacting classical bits that display diverse behaviors, from fractals to random-number generators to Turing-complete computation. We introduce entangled quantum cellular automata subject to Goldilocks rules, tradeoffs of the kind underpinning biological, social, and economic complexity. Tweaking digital and analog quantum-computing protocols generates persistent entropy fluctuations; robust dynamical features, including an entangled breather; and network structure and dynamics consistent with complexity. Present-day quantum platforms---Rydberg arrays, trapped ions, and superconducting qubits---can implement Goldilocks protocols, which generate quantum many-body states with rich entanglement and structure. Moreover, the complexity studies reported here underscore an emerging idea in many-body quantum physics: some systems fall outside the integrable/chaotic dichotomy. (Abstract)

We have discovered a physically potent feature of entangled quantum cellular automata: the emergence of complexity under Goldilocks rules. Goldilocks rules balance activity and inactivity. This tradeoff produces new, highly entangled, yet highly structured, quantum states. These states are persistently dynamic and neither uniform nor random. (14) Moreover, we have demonstrated that our QCA time-evolution protocols are implementable in extant digital and analog quantum computers. (14)

Hsu, Sheryl, et al. A Physarum-inpsired Approach to the Euclidean Steiner Tree Problem. Nature Scientific Reports. 22/14536, 2022. University of Chicago, Illinois researchers including Laura Schaposnik describe a latest instance of how the individual and colonial cognizance of slime-mold microbes can well serve to study and improve complex situations (the US highway is route an another case). See also the work of Tanya Latty, University of Sydney, which was profiled on the PBS NOVA show Secret Mind of Slime.

This paper presents a novel biologically-inspired, explore-and-fuse approach to a large array of problems. The inspiration comes from Physarum, a unicellular slime mold capable of solving complex situations. These characteristics of Physarum imply that many such organisms can explore the problem space in parallel, each individual gathering information and partial solutions. When the organisms meet, they fuse and share information, eventually forming one entity with a relative overview and find an overall solution. Here we develop the Physarum Steiner Algorithm which can find feasible ways to deal with Euclidean Steiner tree issues. (Abstract excerpt)

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