II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Twintelligent Knowledge

1. Earthificial Cumulative Cognizance: AI Large Language Models Learn Much Like a Child

Schuman, Catherine, et al. A Survey of Neuromorphic Computing and Neural Networks in Hardware. arXiv:1705.06963. Oak Ridge Laboratory and University of Tennessee researchers provide a copious review of progress from years of machine computation to this novel advance to artificially avail the way our own iterative brains so adeptly recognize shapes and patterns. The moniker Neuromorphic means how fast we can say cat or car.

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history.

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)

Sejnowski, Terrence. The Deep Learning Revolution. Cambridge: MIT Press, 2018. The renown neuroscientist author has been at the innovative center of the AI computational machine to brain and behavior neural network advance since the 1980s. He recounts his national and worldwide experience with many collaborators in this volume which make it the best general introduction to the field. A great gift for any student, as the author has also been involved with learning how to learn methods for schools. The book is filled with vignettes of Francis Crick, Geoffrey Hinton, Stephen Wolfram, Barbara Oakley, John Hopfield, Sydney Brenner, Christof Koch and others across the years. An example of his interests and reach is as a speaker at the 2016 Grand Challenges in 21st Century Science (Google) in Singapore.

Terrence J. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at the University of California, San Diego. He was a member of the advisory committee for the Obama administration's BRAIN initiative and is founding President of the Neural Information Processing (NIPS) Foundation. He has published twelve books, including (with Patricia Churchland) The Computational Brain (25th Anniversary Edition, MIT Press).

Sejnowski, Terrence. The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence. Proceedings of the National Academy of Science. 117/30033, 2020. The senior Salk Institute neurobiologist introduces a Colloquium on the Science of Deep Learning as this AI neural net frontier goes rapidly forward. Some papers are Emergent Linguistic Structure in Artificial Neural Networks and Algorithms as Discrimination Detectors.

Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages. Although applications of deep learning networks to real-world problems have become ubiquitous, a deep understanding of why they are so effective lags behind. Paradoxes in their training and effectiveness are being investigated by way of the geometry of high-dimensional spaces. A mathematical theory would illuminate how they function, assess the strengths and weaknesses of network architectures, and more. (Abstract excerpt)

Senior, Andrew, et al. Improved Protein Structure Prediction using Potentials from Deep Learning. Nature. 577/706, 2020. Nineteen DeepMind London and University College London researchers including Demis Hassabis describe novel algorithms which are able to study, predict, and create life’s primary, variegated, multi-purpose biomolecule. Dubbed AlphaFold, the advance is touted as a good example of how Artificial Intelligence AL can be of increasing value and utility. A commentary in the same issue is Protein Structure Prediction Gets Real by Mohammed AlQuraraishi (627).

Shafiee, Mohammad, et al. Evolution in Groups: A Deeper Look at Synaptic Cluster Driven Evolution of Deep Neural Networks. arXiv:1704.02081. Shafiee and Elnaz Barshan, Iranian-Canadian, University of Waterloo, systems engineers, and Alexander Wong, DarwinAI (Waterloo), advance this frontier of a universe to human interpretation via multiplex computational cognitive dynamics. Their novel insight is a biological evolutionary setting by way of a deeper genetic and cerebral intelligence. Nature’s emergent profusion of cerebral nets are then alluded to as a generative offspring. See also by the authors these prior award-winning papers Evolutionary Synthesis of Deep Neural Networks via Synoptic Cluster-driven Genetic Encoding at 1609.01360, and Deep Learning with Darwin at 1606.04393.

A promising paradigm for achieving highly efficient deep neural networks is the idea of evolutionary deep intelligence, which mimics biological evolution processes to progressively synthesize more efficient networks. A crucial design factor in evolutionary deep intelligence is the genetic encoding scheme used to simulate heredity and determine the architectures of offspring networks. In this study, we take a deeper look at the notion of synaptic cluster-driven evolution of deep neural networks which guides the evolution process towards the formation of a highly sparse set of synaptic clusters in offspring networks. Utilizing a synaptic cluster-driven genetic encoding, the probabilistic encoding of synaptic traits considers not only individual synaptic properties but also inter-synaptic relationships within a deep neural network. (Abstract)

Shallue, Christopher and Andrew Vanderburg. Identifying Exoplanets with Deep Learning. arXiv:1712.05044. With A Five Planet Resonant Chain Around Kepler-80 and an Eighth Planet Around Kepler-90 subtitle, a Google Brain software engineer and a UT Austin astronomer report a novel application of artificial machine intelligence to successfully analyze huge data inputs from this planet-finder satellite. The achievement received wide press notice, along with a December 14 conference: NASA and Google to Announce AI Breakthrough.

NASA's Kepler Space Telescope was designed to determine the frequency of Earth-sized planets orbiting Sun-like stars, but these planets are on the very edge of the mission's detection sensitivity. Accurately determining the occurrence rate of these planets will require automatically and accurately assessing the likelihood that individual candidates are indeed planets, even at low signal-to-noise ratios. We present a method for classifying potential planet signals using deep learning, a class of machine learning algorithms that have recently become state-of-the-art in a wide variety of tasks. We train a deep convolutional neural network to predict whether a given signal is a transiting exoplanet or a false positive caused by astrophysical or instrumental phenomena.

We apply our model to a new set of candidate signals that we identified in a search of known Kepler multi-planet systems. We statistically validate two new planets that are identified with high confidence by our model. One of these planets is part of a five-planet resonant chain around Kepler-80, with an orbital period closely matching the prediction by three-body Laplace relations. The other planet orbits Kepler-90, a star which was previously known to host seven transiting planets. Our discovery of an eighth planet brings Kepler-90 into a tie with our Sun as the star known to host the most planets. (Abstract)

Sheneman, Leigh and Arend Hintze. Evolving Autonomous Learning in Cognitive Networks. Nature Scientific Reports. 7/16712, 2017. Michigan State University computer scientists post an example of the on-going revision of artificial intelligence, broadly conceived, from decades of dead mechanisms to be in vital accord with evolutionary cerebral architectures and activities. See also The Role of Conditional Independence in the Evolution of Intelligence Systems from this group including Larissa Albantakis at arXiv:1801.05462.

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning. (Abstract)

Smith, Michael J. and James Geach. Astronomia ex Machina: A History, Primer and Outlook on Neural Networks in Astronomy. Royal Society Open Science. November, 2022. University of Hertfordshire computer scientists post a detailed 21st century recount of this ascendant turn from local homo sapience when computers and internet websites came online in the early 2000s to a worldwise cerebral activity today. But this spiral anthropic to Earthuman stage proceeds by way of machine computations which can analyze vast cosmic data flows on their own. See also, for example, A Neural Network Subgrid Model of the Early Stages of Planet Formation by Thomas Pfeil, et al at arXiv:2211.04160.

In recent years, deep learning procedures have been taken up by many fields because it reduces the need for specialist knowledge and automating the process of knowledge discovery from data. This review describes how astronomy is similarly in the midst of a deep learning transformation. We trace astronomical connectionism from early multilayer perceptrons, through to recurrent neural networks, onto the current wave of self-supervised and unsupervised methods. We then preview a fourth phase of a “foundational” model by way of a symbiotic relationship between astro=science and connectionism. (Abstract excerpt)

Soltoggio, Andrea, et al. Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks. Neural Networks. 108/48, 2018. Loughborough University, University of Central Florida and University of Copenhagen computer scientists draw upon the evolutionary and biological origins of this ubiquitous multicomplex learning system to achieve further understandings and usages. Their theme is that life’s temporal development seems to be a learning, neuromodulation, plasticity, and discovery progression. The approach is seen as akin to the Evolutionary Neurodynamics school of Richard Watson, et al, see section V.C. See also herein Evolution in Groups: A Deeper Look at Synaptic Cluster Driven Evolution of Deep Neural Networks (M. Shafiee) and other similar entries.

Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks, artificial systems composed of sensors, outputs, and plastic components that change in response to sensory-output experiences in an environment. These systems may reveal key algorithmic ingredients of adaptation, autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. In particular, the limitations of hand-designed structures and algorithms currently used in most deep neural networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. (Abstract)

Over the course of millions of years, evolution has led to the emergence of innumerable biological systems, and intelligence itself, crowned by the discovery of the human brain. Evolution, development, and learning are the fundamental processes that underpin biological intelligence. Thus, it is no surprise that scientists have modeled artificial systems to reproduce such phenomena. However, our current knowledge of evolution, biology, and neuroscience remains insufficient to provide clear guidance on the essential mechanisms that are key to the emergence of such complex systems. (1)

This paper frames the field that attempts to evolve plastic artificial neural networks, and introduces the acronym EPANN. EPANNs are evolved because parts of their design are determined by an evolutionary algorithm; they are plastic because they undergo various time-scale changes, beyond neural activity, while experiencing sensory-motor information streams during a lifetime simulation. The final capabilities of such networks are a result of genetic instructions, determined by evolution, that enable learning once the network is placed in an environment. Static ANNs with evolved connection weights are not considered EPANN. (1) Whilst the range of inspiring ideas is large and heterogeneous, the analysis in this review proposes that EPANNs build upon five main concepts: evolutionary processes, inspiration from biological neural networks, abstractions in brain simulations, artificial plastic neural networks, and intelligence-testing environments. (3)

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)

Stanley, Kenneth, et al. Designing Neural Networks through Neuroevolution. Nature Machine Intelligence. January, 2019. Uber AI Labs, San Francisco researchers including Jeff Clune provide a tutorial to date for this active field to intentionally but respectfully facilitate external cognitive facilities. See also is this new journal and issue Evolving Embodies Intelligence from Materials to Machines by Davie Howard, et al.

Much of recent machine learning has focused on deep learning, in which neural network weights are trained through variants of stochastic gradient descent. An alternative approach comes from the field of neuroevolution, which harnesses evolutionary algorithms to optimize neural networks, inspired by the fact that natural brains themselves are the products of an evolutionary process. Neuroevolution enables important capabilities that are typically unavailable to gradient-based approaches, including learning neural network building blocks, hyperparameters, architectures and algorithms for learning itself. Neuroevolution differs deep reinforcement learning via a population of solutions during search, enabling exploration and parallelization. This Review looks at several key aspects of modern neuroevolution, including large-scale computing, the benefits of novelty and diversity, the power of indirect encoding, and the field’s contributions to meta-learning and architecture search. (Abstract excerpt)

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