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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 16 through 30 of 73 found.

Our Earthuman Edition: A 21st Century, PhiloSophia, eLibrary of eCosmos, PediaPedia Resource

The Genesis Vision > News

Udrescu, Silviu-Marian, et al. AI Feynman 2.0: Pareto Optimal Symbolic Regression Exploiting Graph Modularity. arXiv:2006,10762. MIT and Stanford physicists including Max Tegmark conceive and employ further effective techniques that can inform and serve this global computational ascent.

We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal and have the best accuracy for a given complexity. We develop a method for discovering generalized symmetries (arbitrary modularity in the computational graph of a formula) from gradient properties of a neural network fit. We use normalizing flows to generalize and aid probability distributions for which we only have samples, along with statistical hypothesis testing. (Excerpt)

The Genesis Vision > News

Zanin, Massimiliano and Johann Martinez. Analysing International Events through the Lens of Statistical Physics: The Case of Ukraine. arXiv:2203.07403. IFISC Institute for Cross-Disciplinary Physics and Complex Systems, University of the Balearic Islands, Spain theorists provide a timely and insightful application of 21st century complex network science advances, as these natural mathematics gain deeper roots in conducive physical phenomena. (Search Neil Johnson, Pedro Manrique, for more findings of how an independent dynamics can even underlie violent conflicts.) The paper was written before the invasion, but can convey a vital illumination. As our 2020s postings now confirm (A Naturome Code, Earthuman Integrations), an actual organic genesis is found to be animated and constrained by an independent source script in exemplary, genetic-like effect for each and every instance. So into this real March madness, maybe concurrent Earthwise Learnings can dispense such edifications we so need.

During the last years, statistical physics has received an increasing attention as a framework for the analysis of real complex systems. However, this is less clear in the case of international political events, partly due to a difficulty in securing relevant quantitative evidence. Here we consider a detailed data set of violent events that took place in Ukraine since January 2021, and analyse their temporal and spatial correlations through entropy and complexity metrics, and functional networks. Results depict an unstable scenario, with events occurring in a non-random fashion, but with eastern-most regions functionally disconnected from the remainder of the country. (Abstract)

During the last decades, statistical physics concepts and tools have ceased being exclusive of this scientific field, for becoming standard approaches used in the analysis of numerous and heterogeneous real-world problems. To illustrate but a few examples, complex networks have become an essential asset in epidemics spreading models, neuroscience, and climate; along with biomedical systems from brain to heart dynamic. The reason for such success is possibly rooted in statistical physics' ability for decoupling the dynamical and observational scales; while a system may only be observable at the macro-scale, conclusions about the underlying micro-scale source can still be drawn. (1)

Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Knowledge

A Learning Planet > The Spiral of Science > deep

Is AI Extending the Mind?. www.crosslabs.org/workshop-2022. A virtual workshop held on April 11 – 15, 2022 with video presentations such as On AI & Ecosystems by Alan Dorin, On Enactive AI by Tom Froese & Dobromir Dotov, and On Autonomous Agents and Semantic Information by Artemy Kolchinsky.

A Learning Planet > The Spiral of Science > deep

Alser, Mohammed, et al. Going from Molecules to Genomic Variations to Scientific Discovery. arXiv:2205.07957. We cite this entry by an eight person ETH Zurich team to record a dedicated project to access the latest deep learning techniques so as to achieve a realm of Iintelligent Algorithms and Architectures (hardware) for next generation sequencing needs.

A great need now exists to intelligently read, analyze, and interpret our genomes not more quickly, but accurately and efficiently enough to scale to population levels. Here we describe much improved genome studies by way of novel AI algorithms and architectures. Algorithms can access genomic structures as well as the underlying hardware. We move onto future challenges, benefits, and research directions opened by new sequencing technologies and specialized hardware chips. (Excerpt)

A Learning Planet > The Spiral of Science > deep

Chantada, Augusto, et al. Cosmological Informed Neural Networks to Solve the Background Dynamics of the Universe. arXiv:2205.02945. We cite this entry by five astro-analysts from Argentina and Harvard as an example of how 2020s AI (EI) techniques can achieve a epic advance (quantum leap) in analytic prowess as our collective Earthuman proceeds apace with this apparent task of ecosmic self-description. See also Stellar Mass and Radius Estimation using Artificial Intelligence by Andy Moya and R. Lopez-Sastre at 2203.06027 for another usage.

The field of machine learning has drawn increasing interest due to its ability to solve many different problems. In this work, we train artificial neural networks to represent differential equations that govern the background dynamics of the Universe. We chose four models to study: ΛCDM, parametric dark energy, quintessence and the Hu-Sawicki f(R) model. We performed statistical analyses to estimate each model's parameters by observational data. We found that the error of the solutions was ∼1% in the region of the parameter space. (Excerpt)

A Learning Planet > The Spiral of Science > deep

Chen, Boyuan, et al. Discovering State Variables Hidden In Experimental Data. arXiv:2112.10755. This entry by Columbia University computer scientists led by Hod Lipson offers a good survey of how this computational endeavor began and goes forth today. It opens by noting historic studies of physical laws and motions as a search for elusive values. From 2021, it is advised that as not before bovel AI methods can achieve deeper analyses so as to discern their presence in dynamic systems such as reaction-diffusion. See also Distilling Free-Form Natural Laws from Experimental Data by Michael Schmidt and Hod Lipson in Science (324/5923, 2009, second Abstract).

All Physical laws are based on relationships between state variables which give a description of the relevant system dynamics. However, the process of identifying the hidden state variables has so far resisted AI techniques. We propose a new principle to find how many state variables an observed system is likely to have, and what these variables might be. Without any prior knowledge of the underlying physics, our algorithm discovers the intrinsic dimension of the observed dynamics and identifies sets of state variables. We suggest that this approach could help catalyze the understanding, prediction and control of increasingly complex systems. (Excerpt)

For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena. Despite much computing power, the process of finding natural laws and their equations has resisted automation. We need to define an algorithm which can insightfully correlate observed data sets. Without prior knowledge about physics, kinematics, or geometry, our algorithm discovered Hamiltonians, Lagrangians, and momentum conservation.. (2009 Abstract)

A Learning Planet > The Spiral of Science > deep

Cranmer, Miles, et al. Discovering Symbolic Models from Deep Learning with Inductive Biases. arXiv;2006.11287. Seven Princeton U., Deep Mind, London, NYU, and Flatiron Institute, NYC computer specialists articulate yet another effective machine procedure as our learning (and hopefully thinking) planet begins to spiral up to a prodigious Earthropic sapiens phase.

We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs) that encourage sparse latent representations an apply symbolic regression to learned model components to extract physical relations. We go on to study a cosmology sample of detailed dark matter and are discover a analytic formula that can predict the concentration of dark matter from the mass distribution of nearby cosmic structures. Our approach offers new ways to interpret neural networks and revealing physical principles from their representations. (Abstract)

A Learning Planet > The Spiral of Science > deep

Krenn, Mario, et al. On Scientific Understanding with Artificial Intelligence. arXiv:2204.01467. Twelve scholars in Germany, Canada, the USA, and China including Alan Aspuru-Guzik post a wide ranging survey as an early effort to try to understand, orient, enhance and benefit from this imminent worldwise, computational transition. But the historic occasion of some cerebral, machine neural deep learning, cognizance going on by itself is such a revolutionary presence with many issues and quandaries. The second quote might give some idea. Thus we repurpose, expand and rename an Earthificial Intelligence: Deep Neural Network Computation Planetary Science section. See also Powerful ”Machine Scientists” Distill the Laws of Physics from Raw Data by Charlie Wood in Quanta (May 10, 2022) for another array of novel paths.

Imagine an “oracle” that predicts the outcome of a particle physics experiment, the products of a chemical reaction, or the function of every protein. As scientists, we would not be satisfied, for we need to comprehend how these predictions were conceived. This feat of scientific understanding, has long been the essential aim of science. Now, the ever-growing power of computers and AI poses this question: But today we ask how can advanced computer systems contribute to learning and discovery. At this early phase we seek advice from the philosophy of science, review the state of the art, and ask current researchers about how they acquired novel findings this way. We hope our perspective inspires and focuses research towards devices and methods that foster and empower this worldwide facility. (Abstract excerpt, edit)

Three Dimensions of Computer-Assisted Understanding: We use scientific literature and personal anecdotes of many active users, and the philosophy of science, to introduce a new classification of android contributions to scientific understanding. Such entities can act I) as a computational microscope, providing information not (yet) attainable by experiment, II) as a resource of inspiration or artificial muse,. In those two classes, the human investigator is essential to develop new insights to their full potential Finally, an android can be III) an agent of understanding by generalizing observations and finding novel scientific concepts. (4)

A Learning Planet > The Spiral of Science > deep

Marcus, Gary. The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. arXiv:2002.06177. The NYU polypsychologist and founder of Robust AI has rightly situated himself as a reality checker and quality control moderator as this hyper-active endeavor moves into every aspect that it can. See also his Rebooting AI: Building Artificial Intelligence We Can Trust 2019 book.

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.

A Learning Planet > The Spiral of Science > deep

Ornes, Stephen. Researchers Turn to Deep Learning to Decode Protein Structures. PNAS. 119/10, 2022. We note this science report to highlight now the growing broad avail of frontier neural net capabilities that are serving to revolutionize biochemical research and knowledge.

AlphaFold (DeepMind) uses AI to predict the shapes of proteins; structural biologists are using the program to deepen our understanding of the big molecules. This image shows AlphaFold's predicted structure (in magenta) of a glycoprotein found on the surface of a T cell. (1) The revolution in structural biology isn’t attributable to AI alone; the algorithms have to train on big datasets of high-resolution structures generated by technologies such as X-ray crystallography, NMR spectroscopy or cryogenic electron microscopy, which produced the above image of a protein complex called β-galactosidase. (3)

In the future, researchers see a role for deep learning not only in understanding a protein’s shape but also how it interacts within a living system. Deep learning models may predict not only the sequence of amino acids that would produce the needed shape, but also how they’ll behave — and interact with other molecules in their biological neighborhood — once they’re in place. (4)

A Learning Planet > The Spiral of Science > deep

Park, Sang Eon, et al. Quasi Anomalous Knowledge: Searching for New Physics with Embedded Knowledge. arXiv:2011.03550. This entry by MIT nuclear physicists is an example of how neural net machine methods can advance sub-atomic particle research.

Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged to detect anomalies but need more precision. Here we present a new strategy dubbed Quasi Anomalous Knowledge (QUAK) which can capture some of the salient features of physics signatures, allowing for the recovery of sensitivity even when signals vary. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider.

A Learning Planet > The Spiral of Science > deep

Seif, Alireza, et al. Machine Learning the Thermodynamic Arrow of Time. Nature Physics. 17/1, 2022. We cite this entry by University of Maryland physicists including Chris Jarzynski as example of how these 2020s bio-based, neural net techniques which run iterative programmed computations can serve as an advanced spiral stage of worldwise scientific studies. In this case, the old arrow of time problem gains a new depth of understanding which was heretofore inaccessible.

The asymmetry in the flow of events that is expressed as “time’s arrow’” traces back to the second law of thermodynamics. In the microscopic regime, fluctuations prevent us from discerning the direction of time with certainty. Here, we find that a machine learning algorithm trained to infer an actual aim identifies entropy production as the relevant physical quantity in its decision-making process. The algorithm rediscovers the fluctuation theorem as the prime thermodynamic principle. Our results indicate that machine learning methods can be used to study out of equilibrium systems and begin to uncover deep physical principles. (Abstract)

A Learning Planet > The Spiral of Science > deep

Wood, Charlie. Powerful ”Machine Scientists” Distill the Laws of Physics from Raw Data. Quanta. May 10, 2022. A science writer deftly realizes that a historic shift is underway as current AI methods begin to empower a revolutionary advance in how research studies can be carried out. The report goes on to survey diverse instances with an exemplary focus on the work of Spanish cell biologists Marta Sales-Pardo and Roger Guimera. The entry brings together so many uses that it has led me to rename and expand this Science Spiral section. In regard, we will be posting a plethora of papers from cosmological to quantum domains, and all in between. Wood’s entry begins with late 20th century starts so on to an array of algorithms by which can handle the vast data inputs that now flood in.

For instance, NYU climate physicist Laure Zanna models ocean turbulence, and Flatiron Institute and University of Washington programmers are busy ginning up equations. At Columbia U., Hod Lipson’s group finds agile software for better neural net performance, while Max Tegmark at MIT adds other versions (search both herein). For Sales-Pardo and Guimera’s cellular avail see Regulation of Cell Cycle Progression by Marina Utoz, et al in Nature Cell Biology (20/646, 2018) and A Bayesian Machine Scientist to Aid in the Solution of Challenging Scientific Problems by R. Guimera, et al in Scientific Advances (6/5, 202).

Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world. With the data revolution, we may now be in a position to uncover new models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need “machine scientists” that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which considers the plausibility of models by iteratively learning from a large empirical corpus of mathematical expressions. We show that this approach uncovers accurate models for synthetic and real data and provides predictions that are more accurate than other nonparametric methods. (Excerpt)

A Learning Planet > Mindkind Knowledge

Yang, Vicky and Anders Sandberg. Collective Intelligence as Infrastructure for Reducing Global Catastrophic Risks. arXiv:2205.03300. Santa Fe Institute and Oxford University, Future of Humanity Institute scholars open this posting by noting how all manner of groupings from cerebral function, fish schools, wildebeest herds, onto political elections can be seen as a natural propensity to form viable associations. By extension, it would be to our advantage if this process could be intentionally availed to serve our own survivability. See also Adaptive Social Networks Promote the Wisdom of Crowds by Almaatouq, A., et al in PNAS (117/21, 2020).

Academic and philanthropic endeavors have grown concerned with global catastrophic risks (GCRs) such as artificial intelligence safety, pandemics, biosecurity, and nuclear war. Study and resolution efforts often depend on the performance of human meetings, which can be seen to involve Collective Intelligence (CI) agendas. CI is a transdisciplinary perspective, whose application involves animal groups, economic markets, collections of neurons, and other distributed systems. Here we argue that better CI methods can improve general resilience against a wide variety of risks. GCR researchers can benefit from engaging more with behavioral researchers to impact critical social issues by engaging and enhancing these transdisciplinary efforts. (Abstract excerpt)

Ecosmos: A Fertile, Habitable, Solar-Bioplanet Lifescape

Animate Cosmos > cosmos

Di Pietro, Lorenzo, et al. Analyticity and Unitarity for Cosmological Correlators. arXiv:2108.01695. We cite this paper by University of Trieste, Stanford University and CERN, Switzerland physicists among many similar entries as an instance of awesome Earthuman collaborative mathematic abilities to explore and quantify any deepest realm or farthest reach of the quantum universe. How incredible is it that out of its temporal developmental via myriad galaxies, solar systems just now a rarest habitable sapiensphere can achieve these retrospective findings and an elibrary of eCosmos. A global occasion is evident by over 150 references. But as ever, where do “unitary correlations” come from, why can we learn, what agency put them there in the first place?

We study the fundamentals of quantum field theory on a rigid de Sitter space. We show that the perturbative expansion of late-time correlation functions to all orders can be equivalently generated by a non-unitary Lagrangian on a Euclidean AdS geometry. We use this relation to infer the analytic structure of the spectral density that captures the conformal partial wave expansion of a late-time four-point function, to derive an OPE expansion, and to constrain the operator spectrum. (Abstract excerpt)

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