Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 1 through 15 of 116 found.

Planatural Genesis: A Phenomenal, PhiloSophia, Propaedutic, TwinKinder, PersonVerse Endeavor

Kastner, Ruth. The Quantum Master and its Classical Emissary.. arXiv:2410.10902. This entry is an invited talk the University of Maryland physicist given at "Metaphysics and the Matter with Thing" conference (search) in February 2024 arranged by the Center for Process Studies and CA Institute of Integral Studies. Its topical occasion was an extended appreciation of Iain McGilchrist’s latest two volume edition The Matter with Things: Our Brains, Our Delusions, and the Unmaking of the World. Her unique appreciation is conveyed by these quotes.

Western thought: All Yang, No Yin The prevailing Western left-brain framework puts us in a constraining metaphysical box characterized by an overdependence on Yang-like particles and neglect of crucial Yin processes. In effect, the classically-restricted Emissary is in charge instead of the quantum-aware Master.

Ian McGilchrist's works present the thesis that the two hemispheres of the brain have radically different modes of interacting with the world, and that their perceptions and functions must be integrated which requires restoring the right-brain to its proper place as "Master." I discuss a parallel to this insight in the dichotomous "worlds" of quantum and classical physics. In addition, I discuss the relevance of Whitehead's process philosophy, as well as the Taoist concepts of Yin and Yang, with particular attention to the primacy of Yin underlying the quantum level as "Master."

MY overall thesis is that the quantum level should be regarded as the fundamental "Master" of physical reality, while the classical level is a secondary "Emissary." In effect, the classical phase functions as a "user interface" between an observer and the totality of physical reality. As McGilchrist has noted, the left hemisphere of the brain employs analytic modes of thought about apparently separate objects. Thus, the left-brain works with a mechanical scheme based on its past behavior. In contrast, the right hemisphere has a global awareness with intuitive and synthetic aspects that transcends the analytic left-brain mode.

McGilchrist, Iain. McGilchrist, Iain. The Matter with Things: Our Brains, Our Delusions, and the Unmaking of the World. London: Perspectiva Press, 2023. The Scottish psychologist follows up his luminous, popular 2009 edition, noted below, with this two volume set whose total 1,800 pages provide an erudite survey of historical wisdom. Google his name to reach current interviews and his 2024 Darwin College talk A Revolution in Thought?. A prior referral is made by citing the traditional coincidence of opposites model, along with many more. An example of how aberrant, narrow focus our western mindset is, sans any integral sense, might be a total inability to perceive that the polarized me, right, conserve and We, left, create views are reflections of nature’s universal gender complements. See The Quantum Master and its Classical Emissary by Ruth Kastner at arXiv:2410.10902 for her views and links to a conference.

In this major work since The Master and His Emissary (2009), Iain McGilchrist addresses some of the oldest questions that humanity faces today. Who are we? What is the world? What is consciousness, matter, space and time? In so doing, he argues that we are trapped in an account of objects by the brain's left hemisphere alone that blinds us to an awe-inspiring reality. He suggests that to understand ourselves and the world we need science and intuition, reason and imagination for which the holistic right hemisphere plays the most important aspect. By way of the latest neuroscience, philosophy and physics, he enlightens a vision that returns the world to life, and us to a better way of living in it: one we must embrace if we are to survive.

Muthukrishna, Michael. Muthukrishna, Michael. A Theory of Everyone: The New Science of Who We Are, How We Got Here, and Where We're Going. Cambridge: MIT Press, 2025. As the quotes and bio support, this accessible work draws on the Sri Lankan author’s international educational and personal experiences to initially describe an inclusive synthesis of life’s emergent course due more to collaborative qualities than isolate individuals. With this conducive scenario in place, the essay goes on to advocate a systems view of creative policies for energy supply and use, equitable climate mitigation, empathic social ambience and so on.

Playing on the phrase “a theory of everything” from physics, Michael Muthukrishna’s ambitious, original, and deeply hopeful book A Theory of Everyone draws on recent research across the sciences, and humanities, to paint a panoramic picture of who we are and can become. Muthukrishna argues that it is our ability to create a shared culture of knowledge, skills, and experience that distinguishes us. But it is only by understanding and applying these attributes can we solve the practical challenges and divisions that daunt us today.

Energy, innovation, cooperation, and evolution are four laws; four interconnected ways to carve up the world and explain how geography, institutions, culture, and history have played out. For now, let me show you how these laws manifest in each of our lives and then in the history of all life. (30)

Our success as a species is due to an ability to innovate, but not by individual intelligence alone. Instead, innovations are a result of our collective brains as humans come together to learn from one another and share ideas. Indeed, it is these collective processes that have led to every innovation that surrounds us. Even the simplest things in our lives are the product of accumulated knowledge, borrowed and recombined across multiple generations in diverse cultures, spanning the globe. (135)

Michael Muthukrishna is a Professor of Economic Psychology in the Department of Psychological and Behavioural Science and Affiliate in Developmental Economics and Data Science at the London School of Economics and Political Science.

Ovchinnikov, Igor. Ubiquitous order known as chaos. Chaos, Solitons & Fractals. 181/114611, 2024.. Chaos, Solitons & Fractals. 181/114611, 2024. We cite this entry by a Russian-American researcher with a physics PhD from UCLA as a notable instance whence Western notions of a random, unwieldy nature can be perceived, if one is so moved, as suffused with an inherent orderliness. The basic source of this alternative view is a referral to a “supersymmetric theory of stochastic dynamics.” See also From Disorder to Design: Entropy-Driven Self-Organization in an Agent Based Swarming Model and Pattern Formation by Vinesh Vijayan, et al at arXiv:2503.18401 for a similar notice from India. In regard, each instance tacitly fassumes a phenomenal existence which is distinguished by an iconic self-similarity.

A close relation has recently emerged between two of the most fundamental concepts in physics and mathematics: chaos and supersymmetry. In contrast to the word 'chaos,' its true physical essence now appears to be a spontaneous order caused by the breakdown of the topological supersymmetry (TS) in all systems from cosmology to nanoscience. This new perspective be called the supersymmetric theory of stochastic dynamics (STS) as theoretical explanations of 1/f noise and self-organized criticality. In this paper, we discuss a field-theoretic embodiment of the butterfly effect which would provide its first consistent physical theory. (I. Ovchinnikov)
From a more general perspective, STS establishes a solid link between dynamical systems and high-energy physics theories. This link may help elevating fields such as hydrodynamics and neurodynamics to a higher level of mathematical precision, rigor, and predictive power. In return, high-energy physics can get access to a broad experimental testing ground for concepts that were previously confined solely to the realm of theoretical abstraction. (9)

This letter seeks to illuminate the profound connection between complexity, self-organization, emergent behaviour, pattern formation, and entropy concepts that are vital to understand our universe. By examining these aspects through the lenses of physics, information theory, and nonlinear dynamics, we uncover a fascinating narrative. Starting with a random cluster of particles possessing distinct internal properties, we activate their interactions and observe the occurrence of intricate patterns. This journey reveals a transition to more probable states. (V. Vijayan)

Teixeira de Melo, Ana. Families as Complex Systems: Love-Force, Change and Resilience. London: Routledge, 2025. The author (bio below) achieves a unique, empathic perception of familial settings as an human epitome of nature’s archetypal conducive complementary.

This book presents an innovative framework for viewing families as complex systems as a way to support positive change, adaptation and resilience. Its theoretical novelty is mostly expressed in the notion of a relational Love-Force emerging from coupling processes between individuals as transformative effects on them, their interactions and environments.

Ana Teixeira de Melo is a Psychologist and Researcher at the Centre for Social Studies, University of Coimbra, Portugal. She has been tracing an interdisciplinary research pathway within the contexts of professional practice. She is also the author of Performing Complexity: Building Foundations for the Practice of Complex Thinking (2020).

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

Durante, Chris. Flourishing – Now and for the Ages to Come: Discerning Ethical Wisdom in the Book of Nature. Fuller, Michael, et al, eds. Issues in Science and Theology:. Fuller, Michael, et al, eds. Issues in Science and Theology: Global Sustainability. Switzerland, Springer, 2023. A Saint Peter’s University, Jersey City provides an array of natural sensibilities such as symbiosis and biomimicry to make a latest ecological case for the presence of this traditional worldly edition.

Adopting the view that divine revelation is not limited to scripture but also occurs in the ‘book of nature,’ this essay seeks to develop a new vision of personal and planetary viability by reformulating Maximus Confessor’s two book theology in the context of contemporary life sciences. Yet, what type of exegesis would be required for theologically reading the ‘book of nature’? I will contend that this requires new empirical, rational, and spiritual modes of inquiry as we strive to ensure a sustainable future for humanity and our earthly kin with whom we share our planetary home. (Excerpt)

, . Zheng, Yizhen, et al. Large language models for scientific discovery in molecular property prediction. Nature Machine Intelligence. February 23, 2025. Nature Machine Intelligence. February 23, 2025. We cite this entry by seven Monash University computer scientists led by Geoffrey Webb to convey how researchers are assimilating AI facilities so as to achieve an effective human foresight and machine learning symbiosis. In addition to biochemistry, further usages for physical chemistry, quantum mechanics, physiology, and biophysics are illustrated. See also Generative AI as a tool to accelerate the field of ecology by Kasim Rafiq, et al in Nature Ecology & EvolutionNature Computational Science (February 2025) for similar syntheses.

Large language models (LLMs) are AI systems which contain vast knowledge in the form of natural language. Although LLMs have some usage, their potential for scientific discovery remains as yet unexplored. In this work, we introduce LLM4SD as custom designed for molecular property prediction by synthesizing information from literature and data. By using these features with interpretable models, LLM4SD can achieve sensible learnings by which to transform biomolecules into vital feature vectors. Our proven results show can foster across a range of benchmark properties for predicting molecular properties. (Excerpt)

Angeloudi, Eirini, et al. The Multimodal Universe: Enabling Large-Scale Machine Learning of 100TB of Astronomical Scientific Data.. arXiv:2412.02527. Twenty-nine Instituto de Astrofisica de Canarias, Universidad de La Laguna, MIT, Flatiron I nstitute, Oxford, Cambridge, Stanford, New York, Australian National, Princeton, Columbia, Paris Saclay, Toronto, UC Berkeley, Montreal and Johns Hopkins University astronomers including Miles Cranmer, aka The Multimodal Universe Collaboration describe this first worldwise edition to demonstrate how 100TB (terabytes) of received ciphers can be collected, curated and made accessible. See also The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning by Ruben Ohana, et al. (arXiv:2412.00568) and The Physicist Working to Build Science-Literate AI by John Pavlus in Quanta (February 28, 2025).

We present the MULTIMODAL UNIVERSE, a large-scale repository of scientific astronomical data compiled to facilitate machine learning research. Overall, it contains hundreds of millions of astronomical observations, hyper-spectral images, multivariate time series and more. We include benchmark representatives of standard practices for machine learning methods in astrophysics. (Excerpt)

Multimodal refers to data that comes in multiple formats or “modalities.”. For example, an image of a galaxy is a two-dimensional array of pixel intensities, while a spectrum encodes brightness at different wavelengths, and a time series captures how the brightness of a source evolves. Each modality offers a unique window into the physics of the source under study.

Bentley, Sarah. Knowing you know nothing in the age of generative AI.. Humanities & Social Sciences Communications. March 10, 2025. In this Nature journal, a psychologist at the University of Queensland, Commonwealth Scientific and Industrial Research Organization and a Responsible and Inclusive AI Women Winner 2024, posts an expansive survey that first reviews an historical context of learning methods from Greece to paper text to the digital internet. With this in place, the article carefully considers ways that a practical reciprocity of human persons and algorithmic agents, once again, might be achieved going forward. Our interest then extends to such a properly arranged global presence whom can gain salutary discovers on her/his own.

Generative AI is a revolutionary new technology whose impact promises to democratize knowledge. And yet, unlike the printing press, which served to amplify one voice to many, Gen AI reduces many voices to one. My Comment situates this novel resource within the evolutionary context of human information dissemination and knowledge production. Whilst noting its extraordinary potential, I propose that since factual cognitive content is such a valuable asset we should be applying it to better understand the impact of AI-mediated informational inputs on both personal and planetary welfare. (Abstract)

Not wishing to negate the enormous potential of generative AI, nor dampen its enthusiastic uptake, it would seem wise at this early point to evaluate the trending tendency to farm out our knowledge practices to this latest wave of technological hyperactivity. Given the value that we peoples place on relative know-how and the role it plays in education, innovation, societal ambience, an understanding of the impact of these new worldwide tools on—both quantitative and qualitative—would seem in order. (5)

Apidianaki, Marianna, et al.. Language Learning, Representation, and Processing in Humans and Machines. .. Computational Linguistics.. 50/4, 2025. University of Pennsylvania, Aix Marseille University and University of Stuttgart AI scholars introduce a special issue on this topic whereby practitioners compare how human persons and large language models gain their knowledge content and pursue and express its productive usage. Into this year, the tacit theme is now to find to ways align the two modes for mutual benefit. Some typical detailed entries are: Usage-based Grammar Induction from Minimal Cognitive Principles, Can Language Models Handle Recursively Nested Grammatical Structures? and Humans Learn Language from Situated Communicative Interactions.

Large Language Models (LLMs) and human beings acquire knowledge about language without direct supervision. LLMs do so by specific training objectives, while humans rely on sensory experience and social interaction. Yet, the differences in the way that language is processed by machines and humans in terms of learning mechanisms, data used, and different modalities make a direct translation difficult. The aim of this edited volume is to be a forum of exchange and debate along this line of research with contributions that seek similarities and differences between humans and LLMs.

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)

Eacersall, Douglas, et al. The ETHICAL Framework for Responsible Generative AI Research Use. arXiv:2501.09021. Fifteen Australian cyberscholars across fields from learning methods to nursing schools propose a considerate array of human scale check points to begin to contend with and rein in this regnant future phase of agent-like algorithmic agents and chatty programs.

The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research through automation of tasks, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework as a practical guide with seven key principles: Examine policies and guidelines, Think about social impacts, Harness understanding of the technology, Indicate use, Critically engage with outputs, Access secure versions, and Look at user agreements. We present this ETHICAL example as a pathway toward responsible innovation in research methodologies. (Excerpt)

Gottweis, Juraj, et al. Towards an AI co-scientist. arXiv:2502.18864. We cite this entry by thirty-four Google Cloud AI Research, Google DeepMind, Houston Methodist, Sequome, Imperial College London and Stanford University scholars as another 2025 version which advocates and describes an intentional, dedicated reciprocity between human persons and computational algorithms.

Scientific discovery advances by novel hypotheses that undergo experimental validation. Here we introduce an AI co-scientist as a multi-agent system built on Gemini 2.0 to help formulate research proposals based on prior evidence and aligned with human guidance. Key features are task execution for flexible scaling and a tournament evolution process. While general purpose, we focus on three biomedical areas: drug repurposing, novel target discovery, and anti-microbial resistance. Our positive results in each instance demonstrate the potential to augment biomedicine and scientific research and usher an era of AI empowered scientists. (Excerpt)

Johnson, Samuel, et al. Imagining and building wise machines: The centrality of AI metacognition. arXiv:2411.02478.. arXiv:2411.02478.. Eleven senior computer scientists at the University of Waterloo, University of Montreal, Stanford University, Allen Institute for Artificial Intelligence, Santa Fe Institute, MPI Human Development and MPI Intelligent Systems including Yoshua Bengio, Nick Chater and Melanie Mitchell join a current project to get ahead of and rein in this worldwide computational transition. As foundation and large language models, along with agentic behaviors, become understood and availed, it is vital to have a lead segment of informed human management through appropriate prompts, select data resources, proper algorithms and so on. See, for example, Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysing by Christoph Scheurer and Karsten Reuter in Nature Catalysis (January 2025). As we work through this critical phase, a beneficial balance of people in ethical charge, along with allowing agents to run pattern finding programs, could be a resolve.

While advances in artificial intelligence (AI) have shown to be capable of sophisticated performance on cognitive tasks, AI systems struggle in critical ways: unpredictable and novel environments (robustness), their reasoning (explainability), communication and commitment (cooperation), and harmful risks (safety). We argue that these issues stem from one basic lapse: AI systems lack wisdom. Drawing from philosophic mores, we define wisdom as the ability to navigate ambiguous, novel, chaotic problems through metacognitive strategies. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations. (Excerpts)

MPI Intelligent Systems Our goal is to understand the principles of Perception, Action and Learning that interact with complex environments. The Institute studies these aspects in biological, computational, hybrid, and material systems from nano to macro scales. The Physics for Inference and Optimization Group focuses on relations between the microscopic and macroscopic complex interactive networks by algorithms based on statistical physics.

Masry, Ahmed, et al. AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding. arXiv:2502.01341.. Sixteen AI experts at York University, McGill University, University of Waterloo and University of British Columbia including Yoshua Bengio propose and describe innovative computational ways to combine both words and pictures so to achieve more effective, enlightened results. And we note that would engage both brain hemispheres in meaningful unison.

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach ensures that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM achieves state-of-the-art performance and improved vision-text feature integration. (Excerpt)

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8  Next