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Volume 125, IOCRF 2025
 
 
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Proceedings, 2025, IOCPh 2025

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Number of Papers: 3
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9 pages, 237 KB  
Proceeding Paper
Jean Piaget and Objectivity—Genetic Epistemology’s Place in a View from Nowhere
by Mark A. Winstanley
Proceedings 2025, 126(1), 1; https://doi.org/10.3390/proceedings2025126001 - 13 Aug 2025
Viewed by 236
Abstract
Science pursues objectivity. According to Thomas Nagel, “we must get outside of ourselves, and view the world from nowhere within it” is the most natural expression of this goal. However, we cannot literally get outside of ourselves; realistically, we can only hope to [...] Read more.
Science pursues objectivity. According to Thomas Nagel, “we must get outside of ourselves, and view the world from nowhere within it” is the most natural expression of this goal. However, we cannot literally get outside of ourselves; realistically, we can only hope to achieve a more detached conception by relying “less and less on certain individual aspects, and more and more on something else, less individual, which is also part of us”. This “self-transcendent conception should ideally explain (1) what the world is like; (2) what we are like; (3) why the world appears to beings like us in certain respects as it is and in certain respects as it isn’t; (4) how beings like us can arrive at such a conception.” The natural and human sciences address (1)–(3), but the last condition is rarely met, according to Nagel. In this paper, I argue that the genetic epistemology conceived by Jean Piaget as a science of the growth of knowledge explains how beings like us meet condition (4). Full article
7 pages, 171 KB  
Proceeding Paper
The Evolution of Intelligence from Active Matter to Complex Intelligent Systems via Agent-Based Autopoiesis
by Gordana Dodig-Crnkovic
Proceedings 2025, 126(1), 2; https://doi.org/10.3390/proceedings2025126002 - 18 Aug 2025
Viewed by 326
Abstract
Intelligence is a central topic in computing and philosophy, yet its origins and biological roots remain poorly understood. The framework proposed in this paper approaches intelligence as the complexification of agency across multiple levels of organization—from active matter to symbolic and social systems. [...] Read more.
Intelligence is a central topic in computing and philosophy, yet its origins and biological roots remain poorly understood. The framework proposed in this paper approaches intelligence as the complexification of agency across multiple levels of organization—from active matter to symbolic and social systems. Agents gradually acquire the capacity to detect differences, regulate themselves, and sustain identity within dynamic environments. Grounded in autopoiesis, cognition is reframed as a recursive, embodied process sustaining life through self-construction. Intelligence evolves as a problem-solving capacity of increasing organizational complexity: from physical self-organization to collective and reflexive capabilities. The model integrates systems theory, cybernetics, enactivism, and computational approaches into a unified info-computational perspective. Full article
9 pages, 1005 KB  
Proceeding Paper
General Theory of Information and Mindful Machines
by Rao Mikkilineni
Proceedings 2025, 126(1), 3; https://doi.org/10.3390/proceedings2025126003 - 26 Aug 2025
Abstract
As artificial intelligence advances toward unprecedented capabilities, society faces a choice between two trajectories. One continues scaling transformer-based architectures, such as state-of-the-art large language models (LLMs) like GPT-4, Claude, and Gemini, aiming for broad generalization and emergent capabilities. This approach has produced powerful [...] Read more.
As artificial intelligence advances toward unprecedented capabilities, society faces a choice between two trajectories. One continues scaling transformer-based architectures, such as state-of-the-art large language models (LLMs) like GPT-4, Claude, and Gemini, aiming for broad generalization and emergent capabilities. This approach has produced powerful tools but remains largely statistical, with unclear potential to achieve hypothetical “superintelligence”—a term used here as a conceptual reference to systems that might outperform humans across most cognitive domains, though no consensus on its definition or framework currently exists. The alternative explored here is the Mindful Machines paradigm—AI systems that could, in future, integrate intelligence with semantic grounding, embedded ethical constraints, and goal-directed self-regulation. This paper outlines the Mindful Machine architecture, grounded in Mark Burgin’s General Theory of Information (GTI), and proposes a post-Turing model of cognition that directly encodes memory, meaning, and teleological goals into the computational substrate. Two implementations are cited as proofs of concept. Full article
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