Computing Information for Intelligent Society: Info-Computational Approach to Decision Making

With the powerful development of pervasive information-based technology, especially intelligent computing, the question arises: How do we imagine a future highly developed and humane (human-centered) intelligent information society? The answer will of course vary depending on time perspective. In a shorter-time perspective, we can try to anticipate based on the existing trends in the development. The first step is to understand the current state of the art of intelligent technology uses towards intelligent society. A longer-term perspective is more uncertain, as new intelligent technologies, especially in combination with biotechnologies and human augmentation and enhancement will be changing both the ways of being human as wellas the structures and behaviors of human societies, as argued by (Wu & Da, 2020) under the heading “The Impact of Intelligent Society on Human Essence and the New Evolution of Humans”. Wu and Da anticipate that the development of widely used AI technologies will lead to the evolution of the “human essence” that will lead to the convergence between social and biological evolution. That is a radically optimistic view that declares equality between the increase in human freedom with the disappearance of the necessity of regular human labor as a means to assure physical existence. In the future intelligent automated society, machines will secure the material basis of existence for everybody. It will remain to humans how to meaningfullyuse this newly conquered space of freedom

Computational Natural Philosophy: A Thread from Presocratics through Turing to ChatGPT

Modern computational natural philosophy conceptualizes the universe in terms of information and computation, establishing a framework for the study of cognition and intelligence. Despite some critiques, this computational perspective has significantly influenced our understanding of the natural world, leading to the development of AI systems like ChatGPT based on deep neural networks. Advancements in this domain have been facilitated by interdisciplinary research, integrating knowledge from multiple fields to simulate complex systems. Large Language Models (LLMs), such as ChatGPT, represent this approach's capabilities, utilizing reinforcement learning with human feedback (RLHF). Current research initiatives aim to integrate neural networks with symbolic computing, introducing a new generation of hybrid computational models.Comment: 17 page

Prolegomena to an operator theory of computation

Defining computation as information processing (information dynamics) with information as a relational property of data structures (the difference in one system that makes a difference in another system) makes it very suitable to use operator formulation, with similarities to category theory. The concept of the operator is exceedingly important in many knowledge areas as a tool of theoretical studies and practical applications. Here we introduce the operator theory of computing, opening new opportunities for the exploration of computing devices, processes, and their networks

Natural Computational Architectures for Cognitive Info-Communication

Recent comprehensive overview of 40 years of research in cognitive architectures, (Kotseruba and Tsotsos 2020), evaluates modelling of the core cognitive abilities in humans, but only marginally addresses biologically plausible approaches based on natural computation. This mini review presentsa set of perspectives and approaches which have shaped the development of biologically inspired computational models in the recent past that can lead to the development of biologically more realistic cognitive architectures. For describing continuum of natural cognitive architectures, from basal cellular to human-level cognition, we use evolutionary info-computational framework, where natural/ physical/ morphological computation leads to evolution of increasingly complex cognitive systems. Forty years ago, when the first cognitive architectures have been proposed, understanding of cognition, embodiment and evolution was different. So was the state of the art of information physics, bioinformatics, information chemistry, computational neuroscience, complexity theory, selforganization, theory of evolution, information and computation. Novel developments support a constructive interdisciplinary framework for cognitive architectures in the context of computing nature, where interactions between constituents at different levels of organization lead to complexification of agency and increased cognitive capacities. We identify several important research questions for further investigation that can increase understanding of cognition in nature and inspire new developments of cognitive technologies. Recently, basal cell cognition attracted a lot of interest for its possible applications in medicine, new computing technologies, as well as micro- and nanorobotics. Bio-cognition of cells connected into tissues/organs, and organisms with the group (social) levels of information processing provides insights into cognition mechanisms that can support the development of new AI platforms and cognitive robotics

Computational Dynamics of Natural Information Morphology, Discretely Continuous

This paper presents a theoretical study of the binary oppositions underlying the mechanisms of natural computation understood as dynamical processes on natural information morphologies. Of special interest are the oppositions of discrete vs. continuous, structure vs. process, and differentiation vs. integration. The framework used is that of computing nature, where all natural processes at different levels of organisation are computations over informational structures. The interactions at different levels of granularity/organisation in nature, and the character of the phenomena that unfold through those interactions, are modeled from the perspective of an observing agent. This brings us to the movement from binary oppositions to dynamic networks built upon mutually related binary oppositions, where each node has several properties

Computing Nature

The articles in this volume present a selection of works from the Symposium on Natu-ral/Unconventional Computing at AISB/IACAP (British Society for the Study of Artificial Intelligence and the Simulation of Behaviour and The International Association for Computing and Philosophy) World Congress 2012, held at the University of Birmingham, celebrating Turing centenary. This book is about nature considered as the totality of physical existence, the universe. By physical we mean all phenomena - objects and processes - that are possible to detect either directly by our senses or via instruments. Historically, there have been many ways of describ-ing the universe (cosmic egg, cosmic tree, theistic universe, mechanistic universe) and a par-ticularly prominent contemporary approach is computational universe

Physical computation as dynamics of form that glues everything together

The framework is proposed where matter can be seen as related to energy in a way structure relates to process and information relates to computation. In this scheme matter corresponds to a structure, which corresponds to information. Energy corresponds to the ability to carry out a process, which corresponds to computation. The relationship between each two complementary parts of each dichotomous pair (matter/energy, structure/process, information/computation) are analogous to the relationship between being and becoming, where being is the persistence of an existing structure while becoming is the emergence of a new structure through the process of interactions. This approach presents a unified view built on two fundamental ontological categories: Information and computation. Conceptualizing the physical world as an intricate tapestry of protoinformation networks evolving through processes of natural computation helps to make more coherent models of nature, connecting non-living and living worlds. It presents a suitable basis for incorporating current developments in understanding of biological/cognitive/social systems as generated by complexification of physicochemical processes through self-organization of molecules into dynamic adaptive complex systems by morphogenesis, adaptation and learning-all of which are understood as information processing

Естественные морфологические вычисления как основа способности к обучению у людей, других живых существ и интеллектуальных машин

Современная натурфилософия динамично развивается как сфера науки и является основой для комплексного подхода к рассмотрению естественных, искусственных практик и социально-гуманитарного знания. Как теоретические, так и практические знания приобретаются, систематизируются, накапливаются в активном и пассивном виде в процессе обучения. В данной статье рассматривается взаимосвязь между современными достижениями в понимании процесса обучения в различных научных сферах: прикладных науках об искусственном интеллекте (глубокое обучение, робототехника), естественных науках (нейробиология, когнитивистика, биология) и философии (вычислительная философия, философия сознания, натурфилософия). Рассматривается вопрос о том, что именно может помочь текущему развитию машинного обучения и искусственного интеллекта на данном этапе, вдохновленному естественными процессами, в частности: вычислительными моделями, например информационно-вычислительными методами морфологических вычислений. Помимо этого рассматривается, в какой степени модели и эксперименты в области машинного обучения и робототехники могут стимулировать исследования в области вычислительной когнитивной науки, нейробиологии и природных вычислений. Мы предполагаем, что понимание механизмов формирования способности к обучению может стать важным шагом в развитии глубокого обучения в контексте вычисления/обработки информации в рамках подхода, объединяющего коннекционизм и символьный подход. Так как все естественные интеллектуальные системы являются когнитивными, мы приводим аргументы в пользу эволюционного подхода к изучению познавательных процессов. Из этого следует, что достижение человеческого уровня интеллекта для иных систем возможно только через эволюцию и развитие. Таким образом, данная статья представляет собой вклад в теорию познания в рамках современной философии природы.КЛЮЧЕВЫЕ СЛОВАобучение,\ua0способность к обучению,\ua0глубокое обучение,\ua0обработка информации,\ua0естественные вычисления,\ua0морфологические вычисления,\ua0инфокомпьютинг,\ua0коннекционизм,\ua0символизм,\ua0познание,\ua0робототехника,\ua0искусственный интеллектFor citation:Dodig-Crnkovic G. Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines.\ua0Philosophical Problems of IT & Cyberspace (PhilIT&C). 2021;(1):4-34. (In Russ.)\ua0https://doi.org/10.17726/philIT.2021.1.1Просмотров: 65