Article
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General Theory of Information: The Bridge to Mindful Machines
Version 1
: Received: 31 October 2024 / Approved: 31 October 2024 / Online: 31 October 2024 (16:45:41 CET)
How to cite: Mikkilineni, R. General Theory of Information: The Bridge to Mindful Machines. Preprints 2024, 2024102564. https://doi.org/10.20944/preprints202410.2564.v1 Mikkilineni, R. General Theory of Information: The Bridge to Mindful Machines. Preprints 2024, 2024102564. https://doi.org/10.20944/preprints202410.2564.v1
Abstract
General Theory of Information (GTI) offers a groundbreaking framework for designing "mindful machines" that bridge the divide between biological intelligence and digital automation. GTI reimagines traditional computing by introducing cognitive, autopoietic (self-regulating) capabilities in digital systems, enabling them to perceive, adapt, and respond autonomously to changing conditions. Unlike conventional AI and AGI models that operate within predefined algorithmic constraints, GTI-based systems go beyond by incorporating self-awareness and a resilient digital "self," capable of storing interaction histories and learning from them. This paper explores GTI’s practical applications in systems requiring resilience, cognition, and ethical safeguards. In video streaming and medical assistance, GTI-based digital genomes enable systems to function independently, actively self-correct, and conform to policy-driven ethical standards. These mindful machines embody unique traits rarely achieved with traditional AI: robust resilience through self-corrective mechanisms, adaptive learning from accumulated experiences, and alignment with ethical principles that govern system behavior. In demonstrating how GTI can cultivate resilience, autonomy, and ethical decision-making, this paper reveals GTI’s potential to empower digital automata with human-like adaptability and values. As such, GTI emerges as a bridge to a new generation of digital systems designed not merely to execute but to understand, adapt, and ethically engage within complex real-world environments.
Keywords
Cognition; Computing Models; Deep Learning; Knowledge Structures; Structural Machines; Associative Memory; Event-Driven Transaction History
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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