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

Metz, Thomas and Robert Ewing, co-chairs. Decoding the Molecular Universe -- Workshop Report. arXiv:2311.11437. We cite this 60 page document by some 30 presenters as a good instance of how such large scientific projects are turning to broad AI capabilities so to empower the way forward.

On August 9-10, 2023, a workshop was held at the Pacific Northwest National Laboratory (PNNL) in Richland, WA of international researchers in metabolomics, chemical ecology, biological threat assessment, computational chemistry, cloud computing, artificial intelligence, and more. The subject was the feasibility of a grand-scale project to create new technologies to identify and quantify enough small molecules so to decode the molecular universe.

Min, Seonwoo, et al. Deep Learning in Bioinformatics. Briefings in Bioinformatics. 185, 2017. Seoul National University biologists present a tutorial survey of this novel union of profuse big data and deep neural net capabilities as they may serve studies of life’s informational essence. See also, e.g., Deep Learning for Computational Biology in Molecular Systems Biology (12/878, 2016.

Here, we review deep learning in bioinformatics. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. (Abstract excerpts)

Mitchell, Melanie. What Does It Mean to Align AI with Human Values? Quanta. December 12, 2022. In the midst of the current burst of advances (chatGPT) and algorithm concerns, the Santa Fe Institute complexity professor updates her prior attentions to this looming issue. For example see her 2019 work Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, Giroux, 2019)

Mudhsh, Mohammed and Rolla Almodfer. Arabic Handwritten Alphanumeric Character Recognition Using Very Deep Neural Network. Information. Online August, 2017. We record this entry by Wuhan University of Technology computer scientists to convey a nascent worldwide knowledge which is lately able to parse ancient scriptures by way of 21st century computational methods. A further notice is that a use of these dynamic cerebral architectures could well imply a global brain coming to its own retrospective and prospect.

The traditional algorithms for recognizing handwritten alphanumeric characters are dependent on hand-designed features. In recent days, deep learning techniques have brought about new breakthrough technology for pattern recognition applications, especially for handwritten recognition. However, deeper networks are needed to deliver state-of-the-art results in this area. In this paper, inspired by the success of the very deep state-of-the-art VGGNet, we propose Alphanumeric VGG net for Arabic handwritten alphanumeric character recognition. Alphanumeric VGG net is constructed by thirteen convolutional layers, two max-pooling layers, and three fully-connected layers. We have achieved very promising results, with a validation accuracy of 99.66% for the ADBase database and 97.32% for the HACDB database. (Abstract)

What Is a Large Language Model, the Tech Behind ChatGPT?. blog.dataiku.com/large-language-model-chatgpt. This is a tutorial posted by Dataiku, a global AI service group, which may approach a succinct, general explanation. Sections such as A Large Language Model is a Type of Neural Network, An LLM uses a Transformer Architecture, An LLM Builds Itself, and LLMs Produce Text that Sounds Right but Cannot Know that it is Right recite how these databases are composed. But one wonders who chooses the book, article, website info already on the Internet, what is the basis guide, for what certain application, and so on. Is it a personal eLibrarian, but one that can’t be trusted. At my wife’s Baystate Medical Center library in the 20210s I would see physicians search for relevant subject information – what is the difference, how do these “LLMs” know better?

Ornes, Stephen. Researchers Turn to Deep Learning to Decode Protein Structures. PNAS. 119/10, 2022. We note this science report to highlight now the growing broad avail of frontier neural net capabilities that are serving to revolutionize biochemical research and knowledge.

AlphaFold (DeepMind) uses AI to predict the shapes of proteins; structural biologists are using the program to deepen our understanding of the big molecules. This image shows AlphaFold's predicted structure (in magenta) of a glycoprotein found on the surface of a T cell. (1) The revolution in structural biology isn’t attributable to AI alone; the algorithms have to train on big datasets of high-resolution structures generated by technologies such as X-ray crystallography, NMR spectroscopy or cryogenic electron microscopy, which produced the above image of a protein complex called β-galactosidase. (3)

In the future, researchers see a role for deep learning not only in understanding a protein’s shape but also how it interacts within a living system. Deep learning models may predict not only the sequence of amino acids that would produce the needed shape, but also how they’ll behave — and interact with other molecules in their biological neighborhood — once they’re in place. (4)

Palazzi, Maria, et al. Online Division of Labour: Emergent Structures in Open Source Software. arXiv:1903.03375. Internet Interdisciplinary Institute, Open University of Catalonia computer theorists report that even group-wide developments of computational codes can be seen to take on and follow a common course as all other organic assemblies. A further implication, we add, would be another perception that cosmic evolutionary nature draws upon and repeat this same complex adaptive systems generative program at each and every instance. See also a cited reference Multi-scale Structure and Geographic Drivers of Cross-infection within Marine Bacteria and Phages in the ISME Journal (7/520, 2013) which describes a similar pattern for microbes.

The development Open Source Software fundamentally depends on the participation and commitment of volunteer developers to progress. Several works have presented strategies to increase the on-boarding and engagement of new contributors, but little is known on how these diverse groups of developers self-organise to work together. To understand this, one must consider that, on one hand, platforms like GitHub provide a virtually unlimited development framework: any number of actors can potentially join to contribute in a decentralised, distributed, remote, and asynchronous manner. On the other, however, it seems reasonable that some sort of hierarchy and division of labour must be in place to meet human biological and cognitive limits, and also to achieve some level of efficiency.

These latter features (hierarchy and division of labour) should translate into recognisable structural arrangements when projects are represented as developer-file bipartite networks. In this paper we analyse a set of popular open source projects from GitHub, placing the accent on three key properties: nestedness, modularity and in-block nestedness -which typify the emergence of heterogeneities among contributors, the emergence of subgroups of developers working on specific subgroups of files, and a mixture of the two previous, respectively. These analyses show that indeed projects evolve into internally organised blocks. (Abstract excerpts)

To answer these questions, we will look at three structural arrangements which have been identified as signatures of self-organisation in both natural and artificial systems: nestedness (i.e. do projects evolve in a way such that the emergence of generalists and specialists is favoured?); modularity (i.e. do OSS projects split in identifiable compartments, thus avoiding Brook’s law despite the addition of contributors? Are these compartments bounded?); and in-block nestedness (i.e. if bio-cognitive limits and division of labour are in place, do the resulting specialised modules self-organise internally?) (2)

Pantcheva, Marina. How do LLMs and humans differ in the way they learn and use language.. rws.com/blog/large-language-models-humans.. A Senior Group Manager at RWS (see below) with a PhD in Theoretical Linguistics addresses this aspect with a list of several ways by which youngsters become talkative and informed. She then makes note of a general affinity between these personal learning methods and the algorithmic, iterative processes that form LLMs content and capabilities.

The question of how children learn language is central to modern linguistics. Numerous contributions have sought to explain this process, here are a few:

Social interactionist theory suggests that feedback and corrections play a pivotal role in language acquisition along with dialogue between the child and the linguistic adults.
Behaviorist theory posits that children learn language by mimicking those around them and positive reinforcement for their endeavors.

Statistical learning theory proposes that children use the natural statistical properties of language to deduce its deep structure such as sound patterns, words, and grammar.
Universal grammar theory argues for the existence of constraints on what human language can look like. In essence, children possess an innate biological component that enables their rapid development of language. (MP)

Genuine Intelligence (GI). Generative AI and Large Language Models are redefining the boundaries of language and content transformation. GI is not just about AI and people working together, it composes a symbiotic blend of AI's computational capacity with human insight and creativity. RWS is a global company based in the UK for transforming content through translation, localization and AI technology blended with human expertise.

Park, Sang Eon, et al. Quasi Anomalous Knowledge: Searching for New Physics with Embedded Knowledge. arXiv:2011.03550. This entry by MIT nuclear physicists is an example of how neural net machine methods can advance sub-atomic particle research.

Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged to detect anomalies but need more precision. Here we present a new strategy dubbed Quasi Anomalous Knowledge (QUAK) which can capture some of the salient features of physics signatures, allowing for the recovery of sensitivity even when signals vary. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider.

Pedreschi, Dino, et al. Social AI and the Challenges of the Human-AI Ecosystem. arXiv:2306.13723. This is a significant contribution by sixteen senior scholars posted in Italy, the USA, Sweden, Austria, New Zealand, Greece, the UK and Germany including Albert-Laszlo Barabasi, Alex Pentland, and Alessandro Vespignani as an initial effort toward a working, practical integration of AI capabilities by way of 21st nonlinear sciences with stronger human intervention and guidance, and a dedicated program toward societal betterment.

The rise of large-scale socio-technical systems in which humans interact with artificial intelligences enables collective phenomena and tipping points, with unexpected, unintended consequences. In a positive way, we may foster the "wisdom of crowds" and beneficial effects to face social and environmental challenges. In order to understand the impact of AI on socio-technical systems and design better AIs that team with humans,some we consider and scope out some early lineaments and case studies of Social AI at the intersection of Complex Systems, Network Science and AI. (Excerpt)

Social AI is emerging at the crossroads of Complex Systems, Network Science and AI, and poses an array of open scientific and technical challenges. Network phenomena provides us with tools to understand the complexity of social systems; while AI provides us with new technological abilities that, together with norms and policy, may help us steer our social systems towards agreed sustainable development goals. Social AI, as the combination and synthesis of these approaches is a novel way to achieve a conceptual framework to lay out a next-generation AI that transparently serves our humans facilitation so to overcome the problems rather than exacerbate them. (10)

Pedreschi, Dino, et al. Social AI and the Challenges of the Human-AI Ecosystem. arXiv:2306.13723. Sixteen authorities from Italy, Greece, Sweden, New Zealand, Germany, the UK and USA including Albert Barabasi, Sandy Pentland and Alessandro Vespignani post an urgent call for a thorough effort to rein in and get in front of this sudden computational prowess that is bursting upon us. But as Eric Schmidt said on TV in August, the problem is that we lack any philosophic basis as a guide to what is going on. As a main premise of this website, we could suggest the phenomenal semblance of an emergent global sapiensphere brain and its own accumulated knowledge.

Large-scale socio-technical systems in which humans interact with artificial intelligence (AI) often leads to social phenomena and tipping points with unexpected, and unintended consequences. As a positive benefit, we may learn how to foster the "wisdom of crowds" and collective actions to face public and environmental challenges. In order to understand these effects and issues, next-generation AIs that team with humans to help overcome problems rather than exacerbate, we propose a Foundations of Social AI project that joins Complex Systems, Network Science and AI. In this perspective paper, we discuss relevant questions, outline technical and scientific challenges and suggest research agendas. (Abstract)

Pfau, David, et al. Accurate computation of quantum excited states with neural networks. Science. Vol. 385/Iss. 6711, 2024. We cite this paper by Google DeepMind, London computational scientists as an example of how AI neural net procedures are being readily applied to quantum phenomena, which in turn implies that this fundamental realm has an innate, analytic affinity with cerebral structures and facilities. See also Understanding quantum machine learning also requires rethinking generalization by Elies Gil-Fuster, et al in Nature Communications (15/2277, 2024) for another instance.

xcited states are important in many areas of physics and chemistry; however, scalable, accurate, and robust calculations of their properties from first principles remain al theoretical challenge. Recent advances in computing molecular systems driven by deep learning show much promise. Pfau et al. present a parameter-free mathematics by directly generalizing variational quantum Monte Carlo to their ground states. The proposed method achieves accurate excited-state calculations on a number of atoms and molecule, and can be applied to various quantum systems. (Editor Summary)

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