
IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic CodeScript Source2. Biteracy: Natural Algorithmic Computation Wibral, Michael, et al. Bits from Biology for Computational Intelligence. arXiv:1412.0291. If one may translate and gloss this paper by Wibral, Goethe University, with Joseph Lizier, University of Sydney, and Viola Priesemann, MPI Dynamics and SelfOrganization, a novel selfdeveloping, quickening cosmos seems suffused by natural information processing via evolved, genetic algorithms and multiplex networks. As Viola’s own research conveys (summary below, search Danielle Bassett also) human brains are an archetypal microcosm of this macrocosmic genesis. The paper appeared in Frontiers of Robotics and AI in 2015, and will be a chapter in From Matter to Life from Cambridge UP in 2017 Computational intelligence is broadly defined as biologicallyinspired computing. Usually, inspiration is drawn from neural systems. This article shows how to analyze neural systems using information theory to obtain constraints that help identify the algorithms run by such systems and the information they represent. Algorithms and representations identified informationtheoretically may then guide the design of biologically inspired computing systems (BICS). We show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely, or redundantly or synergistically together with others. (Abstract excerpts) Wolfram, Stephen. Logic, Explainability and the Future of Understanding. Complex Systems. 28/1, 2019. The polymath prodigy (bio below) now can provide a 40 page technical survey of the history, present and preview of philosophical knowledge by way of its computational basis. In this journal edited by Hector Zenil, see also On Patterns and Dynamics of Rule 22 Cellular Automaton by Genaro Martinez, et al (28/2). Stephen Wolfram is the creator of Mathematica, WolframAlpha and the Wolfram Language; the author of A New Kind of Science; and the founder CEO of Wolfram Research. Born in London in 1959, he was educated at Eton, Oxford and received his PhD in theoretical physics from Caltech at age 20. Wolfram's early scientific work was mainly in highenergy physics, quantum field theory and cosmology. Over the course of four decades, he has been responsible for many discoveries, inventions and innovations in computer science and beyond. (www.stephenwolfram.com) Wolpert, David, et al, eds. The Energetics of Computing in Life and Machines. Santa Fe: Santa Fe Institute Press, 2018. These highly technical proceedings from SFI seminars consider more efficient computational methods by way a better, deeper integration with vital principles and procedures. For example see Overview of Information Theory and Stochastic Thermodynamics of Computation by Wolpert (search), Information Processing in Chemical Systems by Peter Stadler, et al, and Automatically Reducing Energy consumption of software by Stephanie Forrest, et al. Why do computers use so much energy? What are the fundamental physical laws governing the relationship between the precise computation run by a system, whether artificial or natural, and how much energy that computation requires? Can we learn how to improve efficiency in computing by examining how biological computers manage to be so efficient? The time is ripe for a new synthesis of systems physics, computer science, and biochemistry. This volume integrates pure and applied concepts from these diverse fields, with the goal of cultivating a modern, nonequilibrium thermodynamics of computation. Woods, Damian, et al. Diverse and Robust Molecular Algorithms Using Reprogrammable DNA SelfAssembly. Nature. 567/366, 2019. Seven CalTech and Harvard bioinformatic researchers including Erik Winfree and David Doty (search each) advance understandings of how nature’s helical nucleotides can be availed for many more chemical, structural, data storage uses beyond replication. Who then are we cosmic curators to learn all about and intentionally take up life’s organic procreativity? Molecular biology provides a proofofprinciple that chemical systems can store and process information to direct molecular activities such as the fabrication of complex structures from molecular components. Mathematical tiling and statistical–mechanical models of molecular crystallization have shown that algorithmic behaviour can be embedded within molecular selfassembly processes by DNA nanotechnology. Here we report the design and validation of a DNA tile set that contains 355 singlestranded tiles and can be reprogrammed to implement a wide variety of 6bit algorithms. These findings suggest that molecular selfassembly could be a reliable algorithmic component within programmable chemical systems. (Abstract excerpt) Wu, Jun. The Beauty of Mathematics in Computer Science. Boca Raton: CRC Press, 2019. This popular text in Chinese by a Google Brain USA member (bio below) is here published in English. It’s main message is to show the deep affinities between algorithmic code, scriptural languages and innate mathematic principles The book covers many topics including Natural language processing, Speech recognition and machine translation, Statistical language modeling, Quantitive measurement of information, Pagerank for web search, Matrix operation and document classification, Mathematical background of big data, and Neural networks and Google’s deep learning.
Xu, Lei.
Further Advances on Bayesian YingYang Harmony Learning.
Applied Informatics.
Online June,
2016.
The Chinese University of Hong Kong chair of computer science provides a succinct update on his project since the 1990s to join traditional Chinese organic philosophy with Bayesian probabilities so to achieve a preferred way, via an intricate mathematics, to gather and realize natural knowledge. On the author’s CUHK website is a steady list of publications which explore and refine these unique insights. If one may distill, sentences such as Ying (feminine principle) is primary and comes first, while the Yang (male) is secondary and bases on the Ying express and qualify a dynamic reality with both an inner, constant, codelike source, and an overt, manifest animate structure. By whatever terms, its once and future essence is a universally evident gender complementarity, by creative turns Ying, Yang, and may we say Taome. Complementary composition of YingYang system: A system that survives or interacts with its world is able to be functionally divided into two different but complement parts. One is called Yang that inputs from its external world called Yang domain and transforms what gathered via a Yang pathway into an inner domain; while the other is Ying that consists of this inner domain called Ying domain and a Ying pathway. The Ying domain accumulates, integrates, digests, and condenses whatever came from Yang, and the Ying pathway selects among the Ying domain the best ones to produce the reconstructions back to the Yang domain. (321, 2010) Instead of simply regarding YingYang as two opposite parts, which was frequently misunderstood by westerns, the major natures of a YingYang pair are described by the following propositions: (1) Ying is primary, while Yang is secondary and comes from Ying, (2) Ying and Yang are not exclusive each other, though they were sometimes misunderstood by ones from a logical perspective. (321, 2010) Yang, Shengxiang and Xin Yao, eds. Evolutionary Computation for Dynamic Optimization Problems. Berlin: Springer, 2015. An edition amongst a burst of books which engage and implement the growing notice that nature’s cosmic to culture development involves a program source code which seems on a course to achieve its own intelligent selfrealization and intentional continuance. A typical chapter might be Memetic Algorithms for Dynamic Optimizations Problems. See also NatureInspired Metaheuristic Algorithms by XinShe Yang (Luniver Press, 2010). Yang, XinShe. NatureInspired Optimization Algorithms. Amsterdam: Elsevier, 2014. Reviewed more in Universal Darwinism, the work is a latest survey upon life’s evolutionary proclivity to search, test and reach goodenough solutions by way of such operations. Yang, XinShe. Socail Algorithms. arXiv:1805.05855. The Middlesex University, UK computational mathematician and author (search) posts a chapter for the 2020 edition of the Encyclopedia of Complexity and Systems Science (Meyers) which explains an array of features that distinguish this integral class of natural computations. See also Swarm Intelligence: Past, Present and Future by the author at 1804.07999.
Yang, XinShe and Joao Paulo Papa, eds. BioInspired Computation and Applications in Image Processing. Amsterdam: Elsevier, 2016. In this volume, the Middlesex University London mathematician and author is joined by a Sal Paulo State University computer scientist. As these biomimetic ways and means gain wide usage, a typical chapter is FineTuning Deep Belief Networks Using Cuckoo Search. In this volume, the Middlesex University London mathematician and author is joined by a Sal Paulo State University computer scientist. As these biomimetic ways and means gain wide usage, a typical chapter is FineTuning Deep Belief Networks Using Cuckoo Search. Yang, XinShe, et al, eds. Swarm Intelligence and BioInspired Computation. Amsterdam: Elsevier, 2013. An introductory text for spontaneous optimizations due to natural, organics agencies of stochastic, evolutionary, dynamic selfemergence. As noted in this section, the way social insects and animal groupings achieve this can be a good source and guide. Swarm Intelligence is the collective behavior of decentralized, selforganized systems, natural or artificial. SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, to a certain degree random interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples in natural systems of SI include ant colonies, bird flocking, animal herding, bacterial growth, fish schooling and microbial intelligence. (Wikipedia) Zarco, Mario and Tom Froese. SelfOptimization in ContinuousTime Recurrent Neural Networks. Frontiers in Robotics and AI. Online August, 2018. In an entry edited by Claudius Gros and reviewed by Richard Watson, Universidad Nacional Autónoma de México mathematicians glimpse an evidential presence even in cerebral cognitions of nature’s universal process of many relatively random states, in this case neurons, which iteratively evolve into viable “attractor configurations.” A recent advance in complex adaptive systems has revealed a new unsupervised learning technique called selfmodeling or selfoptimization. Basically, a complex network that can form an associative memory of the state configurations of the attractors on which it converges will optimize its structure: it will spontaneously generalize over these typically suboptimal attractors and thereby also reinforce more optimal attractors. This technique has been applied to social networks, gene regulatory networks, and neural networks, but its application to less restricted neural controllers, as typically used in evolutionary robotics, has not yet been attempted. Here we show for the first time that the selfoptimization process can be implemented in a continuoustime recurrent neural network with asymmetrical connections. (Abstract excerpts)
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