Where are the bits in atoms? A perspective on the physical origin and evolutionary nature of information

2024-07-06

AI Summary

The ontological foundation of information—particularly its physical origin and dynamical evolution in atomic and other physical systems—remains inadequately grounded in fundamental physics. Method: We propose an ontological definition: “information is a structural pattern that represents another structural pattern,” and systematically introduce causal set theory into the interdisciplinary domain of information philosophy and physics. Using causal sets to model structural generation processes, we formulate a physically realizable framework for the emergence of information. Contribution/Results: This framework unifies theoretical physics, computation theory, and evolutionary principles, offering a novel paradigm for interpreting quantum information, investigating the origins of life, and advancing foundational theories of artificial general intelligence. It bridges abstract informational concepts with concrete spacetime microstructure, thereby grounding information in relativistic causality and discrete geometry.

Technology Category

• Machine Learning: Information Theory
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Economic aspects and information design of Web, GenAI, and cloud computing
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Information is a structural pattern that represents another structural pattern. This perspective hypothesizes that modelling of structure creation through causal sets can elucidate the natural origin, evolution and ontology of information.

Problem

• Information dynamics
• Atomic systems
• Time evolution

Innovation

• Causal Relationships
• Information Transformation
• Educational Analogies