The information and its observer: external and internal information processes, information cooperation, and the origin of the observer intellect
Vladimir S. Lerner

TL;DR
This paper develops a formal framework for understanding how information originates and self-organizes within an observer through interactive quantum-like processes, linking entropy, probabilities, and hierarchical structures to cognition and intelligence.
Contribution
It introduces a novel model of information creation and self-organization based on micro- and macroprocesses, integrating probabilistic impulses and triplet hierarchies to explain observer cognition.
Findings
Information emerges from interactive impulses following Kolmogorov and Bayesian probabilities.
Microprocesses generate bits with free information, forming hierarchical networks.
Observer intelligence correlates with the complexity of triplet hierarchies.
Abstract
The aim is formal principles of origin information and information process creating information observer self-creating information in interactive observations. The interactive phenomenon creates Yes-No actions of information Bits in its information observer. Information emerges from interacting random field of Kolmogorov probabilities, which link Kolmogorov 0-1 law probabilities and Bayesian probabilities observing Markov diffusion process by probabilistic 0-1 impulses. Each No-0 action cuts maximum of impulse minimal entropy while following Yes-1 action transfers maxim between impulses performing dual principle of converting process entropy to information. Merging Yes-No actions generate microprocess within bordered impulse producing Bit with free information when the microprocess probability approaches 1. Interacting bits memorize free information which attracts multiple Bits moving…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications · Cognitive Science and Education Research
