Nature as a Network of Morphological Infocomputational Processes for Cognitive Agents

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted

Information and Disinformation Boundaries and Interfaces

This paper presents the highlights from the Boundaries of Disinformation workshop held at the Chalmers University of Technology. It addresses the phenomenon of disinformation—its historical and current forms. Digitalization and hyperconnectivity have been identified as leading contemporary sources of disinformation. In the effort to counteract disinformation globally, diverse strategies have been proposed. However, it is important not to forget the need for the balance between individual freedom of expression and institutionalized societal thinking used to prevent the spreading of disinformation. The important aspect of the solution is that the debate about adequate and truthful information, as opposed to disinformation, involves stakeholders

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

In search of a common, information-processing, agency-based framework for anthropogenic, biogenic, and abiotic cognition and intelligence

Learning from contemporary natural, formal, and social sciences, especially from biology, as well as from humanities, particularly contemporary philosophy of nature, requires updates of our old definitions of cognition and intelligence. The result of current insights into basal cognition of single cells and evolution of multicellular cognitive systems within the framework of extended evolutionary synthesis (EES) helps us better to understand mechanisms of cognition and intelligence as they appear in nature. New understanding of information and processes of physical (morphological) computation contribute to novel possibilities that can be used to inspire the development of abiotic cognitive systems (cognitive robotics), cognitive computing and artificial intelligence

Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature

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

Morphological Computation as Natural Ecosystem Service for Intelligent Technology

The basic idea of natural computing is learning from nature. The naturalist framework provides an info-computational architecture for cognizing agents, modeling living organisms as informational structures with computational dynamics. Intrinsic natural information processes can be used asnatural ecosystem services to perform resource-efficient computation, instead of explicitly controlling every step of the computational process. In robotics, morphological computing is using inherent material properties to produce behavior like passive walking or grasping. In general, morphology (structure, shape, form, material) is self-organizing into dynamic structures resulting in growth, development, and decision-making that represent processes of embodied cognition and constitute the naturalized basis of intelligent behavior

Natural Computation of Cognition, from single cells up

At the time when the first models of cognitive architectures have been proposed, some forty years ago, the understanding of cognition, embodiment, and evolution was substantially different from today. So was the state of the art of information physics, information chemistry, bioinformatics, neuroinformatics, computational neuroscience, complexity theory, self-organization, theory of evolution, as well as the basic concepts of information and computation. Novel developments support a constructive interdisciplinary framework for cognitive architectures based on natural morphological computing, where interactions between constituents at different levels of organization of matter-energy and their corresponding time-dependentdynamics, lead to the complexification of agency and increased cognitive capacities of living organisms that unfold through evolution. Proposed info-computational framework for naturalizing cognition considers present updates (generalizations) of the concepts of information, computation, cognition, and evolution in order to attain an alignment with the current state of the art in corresponding research fields. Some important open questions are suggested for future research with implications for further development of cognitive and intelligent technologies