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

Levine, Yoav, et al. Deep Learning and Quantum Entanglement: A Fundamental Bridge. arXiv:1704.01552. Along with other entries (Beny, Golkov), Hebrew University of Jerusalem including Amnon Shashua, plumb the physical depths of this natural, informative synthesis across the physical cosmos to its cerebral emergence. (Over this stretch, might we imagine ourselves as a genesis universe’s way of attaining its own self-cognizance, and continuance?) See also Quantum Entanglement in Neural Network States at arXiv:1701.04844.

Deep convolutional networks have witnessed unprecedented success in various machine learning applications. Formal understanding on what makes these networks so successful is gradually unfolding, but for the most part there are still significant mysteries to unravel. The inductive bias, which reflects prior knowledge embedded in the network architecture, is one of them. In this work, we establish a fundamental connection between the fields of quantum physics and deep learning. We use this connection for asserting novel theoretical observations regarding the role that the number of channels in each layer of the convolutional network fulfills in the overall inductive bias. Specifically, we show an equivalence between the function realized by a deep convolutional arithmetic circuit and a quantum many-body wave function, which relies on their common underlying tensorial structure. This facilitates the use of quantum entanglement measures as well-defined quantifiers of a deep network's expressive ability to model intricate correlation structures of its inputs. (Abstract excerpt)

Li, Qing, et al. Progress and Opportunities of Foundation Models in Bioinformatics. arXiv:2402.04286. Chinese University of Hong Kong and BioMap, Beijing computer scientists provide a wide-ranging perspective on this mid 2020s synthesis of a Bioinformatic approach, whose journal goes back to 1985, and these novel AI neural net, large language models as they become amenable.

Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI) and the adoption of foundation models (FMs). These AI techniques have addressed prior issues in bioinformatics such as scarce annotations and of data noise. FMs are adept at handling large-scale, unlabeled data, which has allowed them to achieve notable results in downstream validation tasks. The primary goal of this survey is to conduct a systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed. Finally, we outline potential development paths and strategies for FMs in future biological research. (Excerpt)

Lin, Henry and Max Tegmark. Why does Deep and Cheap Learning Work so Well?. arXiv:1608.08225. The Harvard and MIT polymaths review the recent successes of these neural net, multiscale, algorithmic operations (definitions vary) from a statistical physics context such as renormalization groups and symmetric topologies. (Intelligent Evolution)

Liu, Weibo, et al. A Survey of Deep Neural Network Architectures and their Applications. Neurocomputing. 234/11, 2017. As the Abstract cites, Brunel University, London, Xiamen University, Yangzhou University, and King Abdulaziz University, Jeddah, computer engineers provide a wide-ranging tutorial on these increasingly useful cognitive methods.

Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep learning techniques have drawn ever-increasing research interests because of their inherent capability of overcoming the drawback of traditional algorithms dependent on hand-designed features. Deep learning approaches have also been found to be suitable for big data analysis with successful applications to computer vision, pattern recognition, speech recognition, natural language processing, and recommendation systems. In this paper, we discuss some widely-used deep learning architectures and their practical applications. An up-to-date overview is provided on four deep learning architectures, namely, autoencoder, convolutional neural network, deep belief network, and restricted Boltzmann machine. Different types of deep neural networks are surveyed and recent progresses are summarized. (Abstract)

Liu, Ziming, et al. KAN: Kolmogorov-Arnold Networks.. arXiv:2404.19756. MIT, Caltech and Northeastern University cognitive scholars including Max Tegmark draw on these companion mathematical theories to gin up a new, improved complementary version for the already capable artificial neural nets. See also Novel Architecture Makes Neural Networks More Understandable by Steve Nadis in Quanta for (September 11, 2024) for a good review article.

Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable functions on edges ("weights"). We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. Through examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs open opportunities for improving today's deep learning models. (Excerpt)

Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights") by which they can outperform in terms of accuracy and interpretability. Through two examples, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs. (S, Nadis)

Liu, Ziyin, et al. Systems Neuroscience: Cerebral Form and Perceptive Function Formation of Representations in Neural Networks. .. arXiv:2410.03006. MIT and Texas A&M University neuroscholars including Tomaso Poggio provide a more effective, thorough method to better activate, shepherd and facilitate AI cerebral, informational connectivities.

Better understandings of neural networks and representations will advance a scientific basis of AI systems. Here we propose the Canonical Representation Hypothesis (CRH), by way of alignment relations to govern the formation of perceptions in hidden net layers. Under the CRH, the latent representations (R), weights (W), and neuron gradients (G) become mutually aligned during training. We then show that breaking CRH leads to reciprocal power-law relations between R, W, and G, which we refer to as the Polynomial Alignment Hypothesis (PAH). (Excerpt)

Lucie-Smith, Luisa, et al. Machine Learning Cosmological Structure Formation. arXiv:1802.04271. We cite this entry by University College London astrophysicists including Hiranya Peiris as an example of the widest range that a new cerebral-based artificial intelligence methods can be applied. If to reflect, whom is this person/sapiensphere prodigy to so proceed as the universe’s way of achieving its own self-quantified description?

Maheswaranathan, Niru, et al. Universality and Individuality in Neural Dynamics across Large Populations of Recurrent Networks. arXiv:1907.08549. By virtue of the latest sophistications, Google Brain and Stanford University AI researchers are able to discern and report “representational similarities” between “biological and artificial networks.” These qualities are then seen in effect across an array of personal and communal affinities.

Manyika, James, ed. AI & Society. Daedulus. Spring, 2022. A timely, dedicated survey with entries like If We Succeed by Stuart Russell, A Golden Decade of Deep Learning by Jeffrey Dean, Language & Coding Creativity by Ermira Murati, and Signs Taken for Wonders: AI. Art & the Matter of Race by Michele Elam.

AI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its many forms has become ubiquitous and its benefits to society and the individual have grown, its impacts are varied. Concerns about its unintended effects and misuses have become paramount in conversations about the successful integration of AI in society. This volume explores the many facets of artificial intelligence: its technology, its potential futures, its effects on labor and the economy, its relationship with inequalities, its role in law and governance, its challenges to national security, and what it says about us as humans. (Issue review)

Manzalino, Antonio. Complex Deep Learning with Quantum Optics. Quantum Reports. 1/1, 2019. In this new MDPI online journal, a senior manager in the Innovation Dept. of Telecom Italia Mobile (TIM), bio below, advances the frontiers of this current assimilation of a lively quantum cosmos with human neural net cognizance. See also, e.g., a cited reference, Quantum Fields as Deep Learning, by Jae-Weon Lee at arXiv:1708.07408. While a prior physics mindset worries over an opaque strangeness, into these later 2010s, via instant global collaborations, a profound new understanding and treatment becomes possible.