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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

Baldi, Pierre. Deep Learning in Biomedical Data Science. Annual Review of Biomedical Data Science. Vol. 1, 2018. A UC Irvine, School of Information and Computer Sciences, Institute for Genomics and Bioinformatics, researcher introduces ways that artificial neural network advances can serve pattern finding and diagnostic needs across many realms of big biological and medical data analysis and synthesis.

Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. (Abstract)

Baronchelli, Andrea. Shaping New Norms for Artificial Intelligence: A Complex Systems Perspective. 2307.08564.. An Alan Turing Institute mathematician provides a unique treatment to date of common features between AI dynamic operations and how informed, interactive agencies spontaneously organize themselves. By so doing a novel synthesis is proposed as a better approach to understand, rein-in, get on board for much benefit.

As Artificial Intelligence (AI) becomes integrated into our lives, the need for new norms for social conventions to formal regulation becomes crucial. This paper considers these vital processes from a complex systems perspective. Focusing on how new norms can occur, it With a distinction on centralisation or decentralization, we highlight criticalities where new codes of behaviour are shaped by formal authorities, informal institutions, or emerge in a spontaneous fashion. On the latter point, the paper reports a conversation that took place between the author and ChatGPT on May 22, 2023, in which the LLM discusses standards it has observed. The conclusion presents an outlook on how AI could influence the formation of future social norms and emphasises the importance for open societies to anchor their formal deliberation process in an open, inclusive, and transparent public discourse. (Excerpt)

It is likely that 2023 will be remembered as the year of Artificial Intelligence (AI). ChatGPT was the fastest internet service to reach 100million users and the technology of Large Language Models (LLMs) at its core is the engine behind sister apps for images such as Dall-e2. One of the most fascinating aspects of LLM is that they exhibit unpredicted emergent features. Only in 2023 it was released that, for the past two years, chatGPT has consistently improved its performance in tests. For anyone familiar with complexity science, observing emergent properties in a complex system made of billions of artificial neurons is not surprising. But perils and misuse remain rife so new rules are needed to help the transition towards a world where humans and machine coexist to the benefit of the former, if not of both parties. (1, edits)

Bausch, Johannes and Felix Leditsky. Quantum Codes from Neural Networks. New Journal of Physics. 22/023005, 2020. We cite this paper by Cambridge University and University of Colorado computational physicists as a gppd instance of how readily cerebral architectures can be effectively applied acrpss far-removed domains. These common transfers open another window upon a universal, iconic bipartite (node/link) and triune (whole brain, genome, etc.) nature.

We examine the usefulness of applying neural networks as a variational state ansatz (approach) for many-body quantum systems for quantum information-processing tasks. In the neural network state, the complex amplitude function of a quantum state is computed. The resulting multipartite entanglement structure can describe the unitary dynamics of physical systems of interest. Here we show that neural networks can efficiently represent quantum codes for information transmission. Our main points are: a) Neural networks yield quantum codes with high coherent information for two important quantum channels, b) For the depolarizing channel, they find the best repetition codes and, c) Neural networks cam represent a special type of quantum error-correcting codes. (Abstract excerpt)

Beer, Kerstin, et al. Efficient Learning for Deep Quantum Neural Networks. arXiv:1902.10445. Leibniz University Hannover physicists describe how readily our own analytic cerebral topologies can be adapted to and availed in this basic physical realm, especially for computational methods. Google the above institute to learn about the authors and how their Light and Matter at the Quantum Frontier project is auguring to begin a new natural creation (let there be light a second time).

Neural networks enjoy widespread success in both research and industry and, with the imminent advent of quantum technology, it is now a crucial challenge to design neural networks for fully quantum learning tasks. Here we propose the use of neurons as a building block for quantum feed-forward networks capable of universal computation. We describe the efficient training of these networks using fidelity as a cost function and provide both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements such that the number of qudits required scales with only the width. (Abstract edits)

In this paper we have introduced natural quantum generalisations of perceptrons and (deep) neural networks, and proposed an efficient quantum training algorithm. The resulting QML (Quantum Machine Learning) algorithm, when applied to our QNNs, they demonstrate remarkable capabilities, including, the ability to generalise, tolerance to noisy training data, and an absence of a barren plateau in the cost function landscape. (4)

Beny, Cedric. Deep Learning and the Renormalization Group. arXiv:1301.3124. We cite this instance by a physicist at Leibniz University, Hanover, in 2013, presently at Hanyang University, Seoul, to record how quantum information, the title theory, complex neural networks, computational methods, and more are converging in a fertile process of cross-correlation.

Renormalization group (RG) methods, which model the way in which the effective behavior of a system depends on the scale at which it is observed, are key to modern condensed-matter theory and particle physics. We compare the ideas behind the RG on the one hand and deep machine learning on the other, where depth and scale play a similar role. In order to illustrate this connection, we review a recent numerical method based on the RG - the multiscale entanglement renormalization ansatz (MERA) - and show how it can be converted into a learning algorithm based on a generative hierarchical Bayesian network model. Under the assumption - common in physics - that the distribution to be learned is fully characterized by local correlations, this algorithm involves only explicit evaluation of probabilities, hence doing away with sampling. (Abstract)

Biamonte, Jacob, et al. Quantum Machine Learning. arXiv:1611.09347. A six member team with postings in Malta, Canada, Spain, Sweden, Germany, and the USA, including Seth Lloyd, advance a novel synthesis of recurrent neural net machine processes with quantum phenomena seen to possess algorithmic, complex dynamic system, information processing affinities. (Quantum Complex Systems)

Bocking, Claudi, et al.. Living guidelines for generative AI — why scientists must oversee its use. Nature. October 19, 2023. As many agencies and countries engage and confer, five senior authorities - CB, Eva van Dis, Robert van Rooij, and Willem Zuidema, University of Amsterdam and John Bollen, Indiana University – scope out an initial guide of ethical concepts along with proposing a dedicated oversight facility

Here, we share a first version of vital principles in a ‘Living guidelines for responsible use of generative AI in research” document. These adhere to the Universal Declaration of Human Rights, including the ‘right to science’ and comply with UNESCO’s Recommendation on the Ethics of AI, as well as the OECD’s AI Principles.

Bubeck, Sebastien, et al. Sparks of Artificial General Intelligence: Early Experiments with GPT-4. arXiv:2303.12712. We cite this entry by fourteen Microsoft Research, Redmond computer scientists because into this year its 155 pages of intense technical content is seen as an exemplary working resource as these undefined, unlimited capacities unfold. As readers know, a plethora of uses and misuses burst upon us each day. In regard, the AI field is so vast that it tends to cover a wide array of neural net algorithm aided usages from reading medical and scientific (genetic, cosmic) data all the way to novel, unruly ChatGPT versions, which no one yet seems to comprehend or have under any control. I suggest a post herein What Is a Large Language Model, the Tech Behind ChatGPT? (Muehmel) as an entry point.

Bundy, Alan, et al. Introduction to Cognitive Artificial Intelligence.. Philosophical Transactions A. June, 2023. AB, University of Edinburgh, Nick Chater, University of Warwick and Stephen Muggleton, Imperial College London, (A generic attribution seems to be computational bioinformatics) introduce this special issue from a June 2022 Royal Society Hooke Meeting about ways to integrate these closely aligned fields for social benefit, since AI might take off wildly on its own. But note that last year was still a pre Chat Bot stage. Typical authoritative entries among many are Representational change is integral to reasoning, Socially intelligent machines that learn from humans and help humans learn, and Emotion prediction as computation over a generative theory of mind.

This theme issue discusses current progress in making artificial intelligence systems think like humans. Many papers argue that despite the amazing results achieved by recent machine learning systems such as Chat GPT and Dall-E, enhancing them with human-like aware acumen will require major developments. Our interest is abilities to reason more like people, and support genuinely social interaction for machines to work alongside humans. The papers in this theme issue bring together the latest AI advances and related research from the cognitive sciences. These issues are crucial, at a time when AI is having an impact in many areas of society. (2023 Overview)

Carleo, Giuseppe and Mathias Troyer. Solving the Quantum Many-Body Problem with Artificial Neural Networks. Science. 355/602, 2017. As the Abstracts notes, ETH Zurich physicists find this generic iterative approach, as it gains utility in many areas, to be an apt method for dealing with and solving such seemingly intractable phenomena. The work merited a report in the same issue as Machine Learning for Quantum Physics (355/580).

The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning of the wave function can reduce this complexity to a tractable computational form for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons. A reinforcement-learning scheme we demonstrate is capable of both finding the ground state and describing the unitary time evolution of complex interacting quantum systems. (Abstract)

Carleo, Giuseppe, et al. Machine Learning and the Physical Sciences. arXiv:1903.10563. An eight member international teamwith postings such as Flatiron Institute Center for Computational Quantum Physics (GC), MPI Quantum Optics (Ignacio Cirac) and Maria Schuld (University of KwaZulu-Natal) consider applications of novel deep neural net methods, broadly conceived, across statistical, particle, cosmic, many-body quantum matter, and onto chemical phases. See also NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems at 1904.00031, and Neural Networks take on Open Quantum Systems in Physics Review Letters (122/25, 2019) by this extended group. As the project flourishes, by ready cross-transfers, one gets an inkling of a naturally cerebral ecosmos, just now trying to achieve via reinforcement learnings its own self-description, literacy, realization, and affirmative action going forward.

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges. (Abstract)

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, and What a neural network model learns about Cosmic Structure Formation by Drew Jamieson, et al at (2206.04573) for more usages.

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)

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