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

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

Gencaga, Deniz. Information-Theoretic Approaches in Deep Learning. Entropy. August, 2018. An Antalya Bilim University, Turkey informatics engineer proposes a special issue with this title. It has a June 30, 2019 closing date for submissions.

Deep Learning (DL) has revolutionized machine learning, especially in the last decade. As a benefit of this unprecedented development, we are capable of working with very large Neural Networks (NNs), composed of multiple layers (Deep Neural Networks), in many applications, such as object recognition-detection, speech recognition and natural language processing. Although many Convolutive Neural Network (CNN) and Recurrent Neural Network (RNN) based algorithms have been proposed, a comprehensive theoretical understanding of DNNs remains a major research area. In this Special Issue, we would like to collect papers focusing on both the theory and applications of information-theoretic approaches, such as Mutual Information. The application areas are diverse and include object tracking/detection, speech recognition, natural language processing, neuroscience, bioinformatics, engineering, finance, astronomy, and Earth and space sciences.

George, Daniel and Eliu Antonio Huerta. Deep Neural Networks to Enable Real-Time Multimessenger Astrophysics. arXiv:1701.00008. Along with a pervasive employ of Bayesian (search) probabilities in mid 2010s science, as these University of Illinois astronomers explain, another incisive approach is the use of artificial neural nets as a generic self-organizing complex system of universal application. See also in this cosmic realm Deep Learning for Studies of Galaxy Morphology (1701.05917), and Star-Galaxy Classification using Deep Convolutional Neural Networks (1608.04369). (Spiral Science)

Gillet, Nicolas, et al. Deep Learning from 21cm Images of the Cosmic Dawn. arXiv:1805.02699. We cite as an example of how astrophysicists from Italy, Australia, the USA, and Canada are making use of brain-like neural networks to analyze patterns across the celestial reaches and depth of temporal origins.

Golkov, Vladimir, et al. 3D Deep Learning for Biological Function Prediction from Physical Fields. arXiv:1704.04039. As this turn to an organic AI actively advances, along with an evolutionary setting, a further rooting may be established into physical foundations.

Gomez-Vargas, Isidro, et al. Neural Networks Optimized by Genetic Algorithms in Cosmology. arXiv:2209.02685. We cite this entry by Universidad Nacional Autónoma de México astronomers to illustrate how collaborative scientific frontiers are now turning to and can readily apply a deep computational intelligence to study and quantify, so it seems, every sidereal breadth and depth. As the quotes say, the authors here meld ANNs with GA (aka evolutionary) capabilities to achieve more insightful synthesis.

Artificial neural networks have been successfully applied in cosmological studies over the the past decade, due to their ability of modeling large datasets and complex nonlinear functions. However, their full use needs hyperparameters to be carefully selected, which is a problem. In this paper, we propose to take further advantage of genetic algorithms (GA) to help get to optimum values.. As a proof, we analyze Type Ia Supernovae, dynamic cosmic content, and the Sloan Digital Sky Survey and found that GAs provide a considerable improvement. (Excerpt)

Genetic algorithms, by themselves, have been investigated in cosmology for quasar parameterisation, nonparametric reconstructions, string theory analysis, to name just a few. In other research areas, there are various cases in which artificial neural networks and genetic algorithms have been applied together; for example, in medicin, seismology and in string theory, among others. (2)

Goodfellow, Ian, et al, eds. Deep Learning. Cambridge: MIT Press, 2016. Seen as a most comprehensive introduction, pioneer theorists Goodfellow, Google Brain, with Yoshua Bengio and Aaron Courville, University of Montreal, provide a technical text on this broad, multi-faceted revolution from inept machine methods to animate algorithms as the way our brains learn and think.

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. (Publisher)

Gopnik, Alison. An AI that Knows the World like Children Do. Scientific American. June, 2017. The UC Berkeley child psychologist and philosopher achieves a good synopsis of this recent turn to a viable computational intelligence by modeling it on actual human cognitive processes than machines. The best teacher may then be how each of us were able to gain knowledge in the first place. Two paths to AI’s resurgence are thus identified as a bottom up deep iterative learning and top down Bayesian probabilities.

Graupe, Daniel. Deep Learning Neural Networks. Singapore: World Scientific, 2016. . A senior University of Illinois electrical engineer provides a technical intro to their basic concepts, scope, methods, back-propagation, convolutional, recurrence aspects and much more, along with many case studies.

Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance. This work is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.

Hassabis, Demis, et al. Neuroscience-Inspired Artificial Intelligence. Neuron. 95/2, 2017. We note this entry because the lead author is the 2010 founder of DeepMind, a premier AI enterprise based in London, which was purchased in 2014 by Google for over $500 million. It is a broad survey of the past, present, and future of this brain-based endeavor guided by advances in how cerebral network dynamics are composed, think, and actively learn.

The successful transfer of insights gained from neuroscience to the development of AI algorithms is critically dependent on the interaction between researchers working in both these fields, with insights often developing through a continual handing back and forth of ideas between fields. In the future, we hope that greater collaboration between researchers in neuroscience and AI, and the identification of a common language between the two fields, will permit a virtuous circle whereby research is accelerated through shared theoretical insights and common empirical advances. We believe that the quest to develop AI will ultimately also lead to a better understanding of our own minds and thought processes. (Conclusion, 255)

Hayakawa, Takashi and Toshio Aoyagi. Learning in Neural Networks Based on a Generalized Fluctuation Theorem. arXiv:1504.03132. Reported more in Universality Affirmations, as the Abstract details, Kyoto University researchers join systems physics and neuroscience to reveal a persistence of universally recurring phenomena, under a rubric of fluctuation theories.

Higgins, Irina, et al. Symmetry-Based Representations for Artificial and Biological General Intelligence. arXiv:2203.09250. DeepMind, London really intelligent persons IH, Sebastien Racaniere and Danilo Rezende scope out ways that an intersect of computational frontiers with neuroscience studies can benefit each field going forward. Once again, an Earthificial realm becomes more brain-like as human beings may first program so that the algorithms can process their appointed tasks (if all goes to plan) and come up with vital contributions on their own.

Biological intelligence is remarkable in its ability to produce complex behaviour in diverse situations. An ability to learn sensory representations is a vital need, however there is little agreement as to what a good representation should look like. In this review we contend argue that symmetry transformations are a main principle. The idea these phenomena affect some aspects of a system but not others, has become central in modern physics. Recently, symmetries have gained prominence in machine learning (ML) by way of more data efficient and generic algorithms that mimic complex behaviors. Taken together, these symmetrical effects suggest a natural framework that determines the structure of the universe and consequently shapes both biological and artificial intelligences. (Abstract excerpt)

Kelleher, John. Deep Learning. Cambridge: MIT Press, 2019. John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin provides another up-to-date survey that is wide-ranging in scope along with in depth examples.

In the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible, concise and comprehensive introduction to the artificial intelligence revolution and its techniques. He explains, for example, how deep learning enables data-driven decisions by identifying and extracting patterns from large datasets, how it learns from large, complex data sets and much more. He describes important deep learning architectures such as autoencoders, recurrent neural networks, as well as such recent developments as Generative Adversarial Networks.

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