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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 1 through 15 of 49 found.


Earth Learn: A Planetary Prodigy Comes to Her/His Own Knowledge

A Learning Planet > Original Wisdom > The Book of Nature

Turker, Sabrina and Gesa Hartwigsen.. Exploring the Neurobiology of Reading through Non-invasive Brain Stimulation. Cerebral Cortex. 141/497, 2021. As the Abstract notes, MPI Human Cognition and Brain Sciences researchers uniquely attribute our human abilities to understand written texts to an interplay of dorsal and ventral streams which can attend to both more or less common vernacular. Once again, as the 2021 edification event becomes filled in at every instance, these archetypal, chimera-like complements are found to be in effect. And we ought to wonder about this nascent planetary prodigy whom altogether is proceeding to learn to read the natural ecosmomic scriptome.

Non-invasive brain stimulation (NIBS) has proved its worth as a modulatory tool for drawing causal inferences and exploring task-specific network interactions. Here we add a synthesis of reading-related studies based on 78 NIBS investigations of the causal involvement of brain regions, and then link these results to a neurobiological model of reading. Overall, the findings provide evidence for a dual-stream neurobiological model of reading in which a dorsal stream processes unfamiliar words and pseudowords, and a ventral stream deals with known words. In regard, we emphasize the need to investigate task-specific network interactions in future studies by combining NIBS with neuroimaging. (Abstract)

A Learning Planet > The Spiral of Science

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)

A Learning Planet > The Spiral of Science

Thiede, Luca, et al. Curiosity in Exploring Chemical Space: Intrinsic Rewards for Deep Molecular Reinforcement Learning. arXiv:2012.11293. University of Gottingen and University of Toronto computational chemists including Mario Krenn contribute to this scientific R & D frontier by scoping out ways that these AI methods can be composed, trained, and motivated so as to search more effectively for novel formulations on their own. See also Scientific Intuition Inspired by Machine Learning Generating Hypothesis by Pascal Friederich, et al (2010.14236) and Computational Theories of Curiosity-Driven Learning by Pierre Oudeyer (1802.10846). One then wonders if the entire ecosmic development might be viewed as a long, iterative, accumulated learning experience, as it may now reach a phase of potential self-recognition.

Computer-aided design of molecules has the potential to radically advance the field of drug and material discovery. These machine, reinforcement and deep learning approaches allow for molecular design without prior knowledge. In this study, we propose an algorithm to aid efficient candidate space exploration inspired by a concept known in the literature as curiosity. We show on three benchmarks that a curious agent finds better performing molecules. This indicates an exciting new research direction for reinforcement learning agents that can explore the chemical space out of their own motivation. (Abstract excerpt)

A Learning Planet > The Spiral of Science > deep

Anshu, Anurag, et al. Sample-efficient Learning of Interacting Quantum Systems.. Nature Physics. 17/8, 2021. We cite this entry by UC Berkeley, IBM Watson Research, RIKEN Center, Tokyo, and MIT researchers as an example of how AI studies are becoming amenable even to this deepest, foundational realm. Once again a grand ecosmic endeavor seems to be its own internal self-description, so that maybe whomever sapiensphere is able to do this can begin a new intentional creation from here.

Learning the Hamiltonian that describes interactions in both condensed-matter physics and the verification of quantum technologies is an important task. Previously, the best methods for quantum Hamiltonian learning with able performance required measurements that scaled exponentially with the number of particles. Here we prove that only a polynomial number of local measurements on the thermal state of a quantum system are necessary for accurately learning its Hamiltonian. The framework we introduce provides a theoretical foundation for applying machine learning techniques to achieve a long-sought goal in quantum statistical learning. (Abstract excerpt)

Hamiltonian function, also called Hamiltonian, is a mathematical definition introduced in 1835 by Sir William Rowan Hamilton to express the rate of change the condition of a dynamic physical system, such as a set of moving particles.

A Learning Planet > The Spiral of Science > deep

Spraque, Kyle, et al. Watch and Learn – A Generalized Approach for Transferrable Learning in Deep Neural Networks via Physical Principles. Machine Learning: Science and Technology.. 2/2, 2021. We enter a typical paper from this new Institute of Physics IOP journal so to report current research frontiers as AI neural net facilities join forces with systems physics and quantum organics. Here University of Ottawa, University of Waterloo, Canada, and Lawrence BNL theorists including Juan Carasquilla and Steve Whitelam discuss the natural affinities that these far removed realms seem to innately possess. See also Halverson, James, et al. Neural Networks and Quantum Field Theory by James Halverson, et al (2/3, 2021) and Natural Evolutionary Strategies for Variational Quantum Computation by Abhinav Anand, et al (2/4, 2021). Altogether, our phenomenal Earthuman abilities can begin a new era of participatory self-observance, description and discovery.

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Here we demonstrate an unsupervised learning approach augmented with physical principles that achieves transferrable content for problems in statistical physics across different regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to discern the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. This constitutes a fully transferrable physics-based learning in a generalizable approach. (Spraque Abstract)

We propose a theoretical understanding of neural networks in terms of Wilsonian effective field theory. The correspondence relies on the fact that many asymptotic neural networks are drawn from Gaussian processes (GPs), the analog of non-interacting field theories. Moving away from the asymptotic limit yields a non-Gaussian process (NGP) and corresponds to turning on particle interactions, allowing for the computation of correlation functions of neural network outputs with Feynman diagrams. General theoretical calculations are matched to neural network experiments in the simplest class of models allowing the correspondence. (Halverson Abstract excerpt)

A Learning Planet > Mindkind Knowledge

Arellanes, David. Composition Machines: Programming Self-Organizing Software Models. arXiv:2108.05402. Akin to Okyay Kaynak, et al herein, a Lancaster University, UK computer theorist considers how to achieve a computational spontaneity which could operate and advance on its intrinsic own.

We are entering a new era in which software systems are increasingly complex and extensive. But they are becoming more difficult to develop and empower. To address this, self-organizing software suites open a promising direction since they allow the bottom-up emergence of complex computational structures from simple rules. In this paper, we propose a composition method which facilitates their presence and operation. Our approach enables the occasion of multiple programs based on well-known rules from the realm of Boolean logic and elementary cellular automata. (Abstract)

A Learning Planet > Mindkind Knowledge

Graham, Daniel. An Internet in Your Head: A New Paradigm for How the Brain Works. New York: Columbia University Press, 2021. Our guiding premise since the early 2000s for this annotated anthology resource website has been that the newly enveloping worldwide Internet webwork, as it proceeds to form a global noosphere by way myriad human contributions, could well be seen to take on a major transition life and mind of its own. Into the 21st century, a further proposal is that this collective faculty is then proceeding to learn and gain knowledge by itself. However until this new work by a Hobart and William Smith Colleges neuroscientist, the plausible extrapolation was rarely considered. In regard, the author posts a strong and thorough comparison and continuity between our brains and this cerebral sensorium is at last fully explained. A veteran theorist, Dan Graham (search) was an editor for an issue of Network Neuroscience (4/4, 2021) and advised for this project by authorities such as Michael Gazzaniga, Gyorgy Buzsaki and Olaf Sporns.

As the quotes cite, it is argued that an older computer metaphor with byte-like nodes needs to be expanded by more emphasis on the many connective links in between. This webwork perspective can then provide a better, functional brain model along with being readily being applicable to the worldwide facility. This 2020s appreciation can thus give precedence to communicative routings of informative content, which is really what the brain is about. By this projected continuity, Graham is able to allow that this internet phase can rightly be seen as learning and coming to its own knowledge. In respect, an Earthuman collaborative neuroscience can begin to perceive and enhance the novel occasion of a palliative dispensation over this critically stressed bioworld.

In neuroscience, the metaphor of the brain as a computer has defined the field for much of the modern era. But as neuroscientists evaluate their assumptions about how brains work, we need a new metaphor to help us ask better questions. The computational neuroscientist Daniel Graham contends that the brain is not like a single computer ― it is a communication system like the internet graced with flexibility and reliability. The brain and the internet route signals which require protocols to direct messages. But we do not yet understand how the brain manages the dynamic flow of information across its entire network. The internet metaphor can help neuroscience unravel the brain’s connectivity by focusing on shared design principles and communication strategies. Highlighting similarities between brain connectivity and internet architecture can open new avenues of research and reveal the brain’s deepest secrets. (Publisher excerpt)

This chapter (5) introduces the workings of the internet and of communication systems more generally. The aim here is to describe the “physics” of the network – the overarching principles that form its conceptual superstructure. As we will see, it’s the internet’s general principles that make the system so powerful, and they are also relevant to the brain. (120)

I believe both approaches – rethinking existing knowledge and discovering new phenomena – are needed. There is more than enough evidence already that is consistent with a brain that performs sophisticated routing of messages and that resembles the internet. (237-238) If the brain is like the internet in important ways, is the internet then like the brain and possibly capable of consciousness? (258)

If the internet is conscious, it may be driven to creativity, much as we are. For humans, consciousness is the vehicle by which we generate new structures and ideas. Creativity relates to how we build up our understanding of the external world. From basic sensory processes upward, the world shapes our experience in fundamental and far-reaching ways. (266) The internet is also creative, and in a similar manner. It integrates and manages new components, along with the information those components generate and transmit. (267)

The internet’s ability to learn requires the existence of efficient real-time communication among millions of nodes, not just computations. This ability is supercharged by its capacity for graceful, creative and interoperable growth. Internet use continues to grow in large part because it learns so effectively and can integrate new forms of information across a rich variety of realms of knowledge. By learning and acting in this way, the internet resembles an adaptive biological entity, one potentially capable of consciousness. (268)

A Learning Planet > Mindkind Knowledge

Kaynak, Okyay, et al. Towards Symbiotic Autonomous Systems. Philosophical Transactions of the Royal Society A.. August, 2021. Bogazici University, Istanbul, Maladalen University, Sweden, and University of Science and Technology, Beijing engineers (surely a sapiensphere collaboration) introduce a special issue on present endeavors to achieve and enhance human - computer interface facilities and potentials. A common theme is that these Earthuman multiplex connectivities, by way of reciprocal interactions, will acquire an intelligent capability to learn and gain knowledge on their own. See the main paper by Yingxu Wang, et al herein for a copious explanation. And by a vista across centuries, this scientific journal founded by Isaac Newton can now report an historic spiral and ascent to a consummate worldwise phase, with a promise of a consummate discovery.

Starting in the last century, the widespread use of computers has changed the lifestyles of humankind. Since then, in Digital technology, the worldwide web, Internet of Things and artificial intelligence have led a growing interaction and empowerment among humans and technical devices. Looking ahead, this integration is tending to create symbiotic autonomous systems (SASs). What matters in the context of SASs is the degrees of autonomy they have, their capability to evolve (e.g. to learn and adapt), and their ability to interact with their environment, between themselves, and with ourselves. (Abstract excerpt)

A Learning Planet > Mindkind Knowledge

Wang, Yingxu, et al. On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems. Philosophical Transactions of the Royal Society A. August, 2021. In a special issue on this advance (see Kaynak), 15 multinational researchers (Y. Wang is a senior authority now based in Canada) scan the breadth and depth of this global frontier as multiplex Earthuman computational webworks increasingly form and take on a lively cerebral intelligence and informational content of their own.

Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting a self-organized collective intelligence enabled by coherent symbiosis of human-machine interactions. The emerging field of SAS has developed general AI technologies which function without human intervention and hybrid cognitive synergies between humans and intelligent machines. Here we look at a theoretical framework for SASs based on the latest advances in intelligence, cognition, computer, and system sciences which adopt bio-brain-social-inspired and autonomous behaviors. (Abstract abstract)

Symbiosis is a widely observable phenomenon in biological, mental, and social systems where mutual dependences exist among plants, animals, and human societies as a necessary condition for them to co-evolve. Symbiosis is particularly important to human societies because of the fundamental need for extending individuals’ physical, intellectual, and/or resource limits. Therefore, it becomes a fundamental principle of system science and the universal context of modern sciences and engineering. (3)

Ecosmos: A Procreative Organic Habitable UniVerse

Animate Cosmos > Quantum Cosmology

Brahma, Suddhasattwa, et al. Universal Signature of Quantum Entanglement Across Cosmological Distances. arXiv:2107.06910. We cite this entry by McGill University and University of Edinburgh physicists as one example among many as an indication of how our collaborative sapiensphere proceeds apace to quantify quantomic, atomic and ecosmomic realms across any depth and breadth. Into the 2020s quantum network systems are coming to pervade and distinguish an organic genesis.

universe originate from quantum fluctuations, most of the literature ignores the crucial role that entanglement between the modes of the fluctuating field plays in its observable predictions. In this paper, we import techniques from quantum information theory to reveal undiscovered predictions for inflation which, in turn, signals how quantum entanglement across cosmological scales can affect large structural formations. Our key insight is that observable long-wavelengths must be part of an open quantum system, so that the quantum fluctuations can decohere in the presence of an environment of short-wavelengs. (Abstract)

Animate Cosmos > Quantum Cosmology > Gaia

Irrgang, Christopher, et al. Towards Neural Earth System Modelling by Integrating Artificial Intelligence. Nature Machine Intelligence. August, 2021. Seven senior researchers posted in Germany, the UK, and the USA including Niklas Boers and Elizabeth Barnes scope out this meld and upgrade of Earth system science with deep learning frontier methods. By this union, might this Gaia bioworld be able attain a global brain facility which cam proceed to take over and sustain itself?

Earth system models (ESMs) can help quantify the physical, geologic state of our planet and predict how it might change under ongoing anthropogenic forcing. In recent years, artificial intelligence (AI) has been used to augment or even replace classical ESM tasks, raising hopes that AI could solve grand challenges of climate science. In this Perspective we survey the recent achievements and limitations of both process-based models and AI methods. We then propose a new approach in which deep neural networks and ESMs are integrated as learning, self-validating ESM–network hybrids. (Abstract excerpt)

Animate Cosmos > Quantum Cosmology > Gaia

Lyons, Timothy, et al. Oxygenation, Life and the Planetary System during Earth’s Middle History. Astrobiology. July 21, 2021. Six geoscientists from UC Riverside, Yale, China University of Geosciences and Georgia Tech advance understandings of how our habitable, self-sustaining bioworld could to exhibit some manner of an inherent biological development, maybe along a course to our retrospective.

The long history of life on Earth has unfolded as a cause-and-effect relationship with the evolving amount of oxygen in the oceans and atmosphere. An oxygen deficiency held over the first 2 billion years, yet evidence for biological O2 and local ocean enrichments appear before O2 in the atmosphere some 2.3 billion years ago. However, the relationship between complex life (eukaryotes, including animals) and later oxygenation is less clear. The apparent rise in O2 around 800 million years ago is coincident with major developments in complex life. This paper focuses on the geochemical records of Earth's middle history, roughly 1.8 to 0.5 billion years ago, so to explore an interactivity with biological evolution. A richer understanding of the interplay between coevolving life and Earth surface environments can provide a template for studies of sustained habitability on distant exoplanets. (Abstract excerpt)

Animate Cosmos > Quantum Cosmology > Gaia

Rubin, Sergio and Michel Crucifix. Earth’s Complexity is Non-Computable: The Limits of Scaling Laws, Nonlinearity and Chaos. Entropy. 23/7, 2021. Catholic University of Louvain, Georges Lemaitre Centre for Earth and Climate Research consider further ways that our home Gaia alive can be understood as a dynamic self-regulating and maintaining bioworld. In regard, they refer to Robert Rosen’s relational affinities and to Francisco Varela’s collegial autopoietic self-making theories for a more animate basis. Again much of the consternation is due to our betwixt mechanical and organic universes moment, which is an untenable situation. But a natural philosophical vista to resolve all this is mostly missing, which is what this resource is trying to facilitate. See also Lynn Margulis, Neocybernetics, and the End of the Anthropocene by Bruce Clarke (University of Minnesota Press, 2020) for a similar version.

Current physics commonly qualifies the Earth system as ‘complex’ because it includes numerous different processes operating over a large range of spatial scales. Here, we argue that understanding the Earth as a complex system requires a consideration of the Gaia hypothesis. The Earth is unique because it instantiates life and therefore an autopoietic, metabolic-repair organization at a planetary scale. This implies that our bioworld is a self-referential system that inherently is non-algorithmic and cannot be simulated in a Turing machine. We discuss the consequences of this, with reference to in-silico climate models, tipping points, planetary boundaries and feedback loops as units of adaptive evolution and selection. (Abstract excerpt)

Animate Cosmos > Quantum Cosmology > quantum CS

Sone, Akira and Sebastian Deffner. Quantum and Classical Ergotropy from Relative Entropies. Entropy. 23/9, 2021. We enter this paper by Center for Nonlinear Studies, LANL and University of Maryland physicists to note the latest theoretical exercises with regard to this open habitable frontier which is now known to be graced by these malleable qualities and much more. See also Quantum Coherence and Ergotropy by Gianluca Francica, et al at arXov:2006.05424.

The quantum ergotropy quantifies the maximal amount of work that can be extracted from a quantum state without changing its entropy. Given that the ergotropy can be expressed as the difference of quantum and classical relative entropies of the quantum state with respect to the thermal state, we define the classical ergotropy, which quantifies how much work can be extracted from distributions that are inhomogeneous on the energy surfaces. A unified approach to treat both quantum as well as classical scenarios is provided by geometric quantum mechanics, for which we define the geometric relative entropy. The analysis is concluded with an application of the conceptual insight to conditional thermal states, and the correspondingly tightened maximum work theorem. (Abstract)

My research interests focus on quantum information theory, spanning from quantum control theory to quantum thermodynamics, inspired by classical control and optimization, and their applications quantum computation, quantum simulation, quantum communication and quantum metrology. By working at industry, research universities or liberal arts colleges, I hope to contribute to developing the state-of-the-art quantum technology as a theoretical physicist. (Akira Sone)

Animate Cosmos > Quantum Cosmology > quantum CS

Spitz, Damiel, et al. Finding Universal Structures in Quantum Many-Body Dynamics via Persistent Homology. arXiv:2001.02616. We cite this entry by Heidelberg University physicists including Jurgen Berges (search) and Anna Wienhard for its report that this widely used mathematical method can be availed even in this deepest domain. Akin to its broad application to neural networks, galactic clusters and more, quantum phenomena are found to be quite amenable. Thus our Organics title and consequent universality is well supported.

Inspired by topological data analysis techniques, we introduce persistent homology observables and apply them in a geometric analysis of quantum field theories. As a test case, we consider a two-dimensional Bose gas far from equilibrium with a spectrum of dynamical scaling exponents. We find that the persistent homology exponents are inherently linked to the geometry of the system. The approach opens new ways to study quantum many-body dynamics in terms of robust topological structures. (Abstract)

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