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

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

Aragon-Calvo, MIguel. Classifying the Large Scale Structure of the Universe with Deep Neural Networks. arXiv:1804.00816. We cite this posting by a National Autonomous University of Mexico astronomer as an example of how such novel brain-based methods are being applied to even quantify these celestial reaches. By this work and many similar entries, might our Earthwise sapiensphere be perceived as collectively beginning to quantify the whole multiverse? Could it also allude a sense of an affine nature as a cerebral, connectome cosmos? See also, e.g., An Algorithm for the Rotation Count of Pulsars at 1802.0721.

Bahri, Yasaman, et al. Statistical Mechanics of Deep Learning. Annual Review of Condensed Matter Physics. 11/501, 2020. Google Brain and Stanford University researchers scope out ways to root neural-like networks as they come pervade and apply everywhere into an increasingly conducive physical phenomena. We add that an implication might be a nascent sense of a cerebral cosmos trying to achieve its self-witness and re-presentation via our globally capacious intellect.

The recent success of deep neural networks in machine learning raises deep questions about underlying theoretical principles. We methods of interactive physical analysis rooted in statistical mechanics which have begun to yield conceptual connections between deep learning and diverse physical and mathematical topics, including random landscapes, spin glasses, jamming, dynamical phase transitions, chaos, Riemannian geometry, random matrix theory, free probability, and nonequilibrium phases. (Abstract excerpt)

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)

Bengio, Yoshua, et al. International Scientific Report on the Safety of Advanced AI.. arXiv:2412.05282.. This 132 page document is the report from the May 2024 AI Seoul Summit conference. An authoritative array of computer experts and business contributors, just about everybody, and a huge audience gave the event an international significance. Topical presentations emphasized both AG Intelligences while trying to get in front of many ethical ramifications. As a reference, we cite the original 2015 “Deep Learning” article by Yann LeCun, Y. Bengio and Gregory Hinton (Nature 521/436) only nine years ago for a sense of how fast and furious this global (knowsphere) facility is moving. We had better get ahold of the reins and steering wheel in time.

This is the interim publication of The first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group of 75 AI experts contributed to this report, including an international Expert Advisory Panel nominated by 30 countries, the EU, and the UN.

People around the world will only be able to enjoy general-purpose AI’s many potential benefits safely if its risks are appropriately managed. This report focuses on identifying these risks and evaluating technical methods for assessing and mitigating them. It does not aim to comprehensively assess all possible societal impacts of general-purpose AI, including its many potential benefits.

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.

Borghoff, Uwe, et al. Human-Artificial Interaction in the Age of Agentic AI: A System-Theoretical Approach.. arXiv:2502.14000. University of the Bundeswehr Munich, Sapienza University of Rome and Università degli Studi del Molise, Campobasso, Italy computer scientists add more reasons in support of a best balance of an active person and AI operating system reciprocity.

This paper presents a current perspective on human-computer interaction (HCI) as a dynamic interplay between personal and computational agents as a coordination and communication among heterogeneous individuals. A key distinction is made between multi-agent systems (MAS) and Centaurian systems whereby MAS maintains user cooperation, while the other way melds human and AI. Here we seek to combine them in communication spaces with surface, observation, and computation layers to ensure seamless architectures. (Excerpt)

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.

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