Talk:White paper on Quasi Artifactual Intelligence

A ChatGPT 4.0 simplification of grammar

XenoEngineer (talk) 18:19, 25 July 2023 (UTC)

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D R A F T    D O C U M E N T

A Dascient — groupKOS Collaborative

QuasiArtifactualIntelligence: Perception from the Edge

Introduction

The Quasi Axiomatic Theory (QAT) was conceived as a big-data tool designed to discern significant events on the cutting ever-expanding edge of the world-corpus: Cyberspace: 2000. It functions as a category theory, a means of systematically organizing and classifying data, that can isolate trends and detect waves of change in large data samples, potentially as vast as planet-scale.

The Architecture of the QAT Algorithms

The QAT uses algorithms to offer a form of 'categorical abstraction,' visual maps of data, and 'eventChemistry,' a method of exploring categories. With these algorithms, machine learning can be integrated into the quasi-axiomatic analysis loop, enabling an AI-in-training to witness the quasi-artifact of measured reality.

QAT Theory in Principle of Emergent Processing of Causal Artifact within Chaos

QAT is essentially a map-maker. It uses a group-motion on two random points on the map in each iteration. This 'grouping' is the 'emergence' in this implementation of emergent computing.

Implementation of QAT Data Structures

The data structures needed to produce emergent processing of the QAT are relatively simple and include three lists: UniqueList-A, UniqueList-B, and ExpandedList-A. In addition, the eventChemistry methods operate with two more lists: Link Index-List by Atom and Atom Index-list by Link.

Simplification

In an optimal solution for big data information access, the data is loaded once, never moved, indexed in place, and structured in place for binary-tree access. This solution slows down very little even with vast data sampling sets, making it highly scalable.

Future Development Notes

Two measurement channels provide one QAT dimension of analysis: QAT.I-RIB, offering a visual abstraction of relationships in the cross-comparison. This happens by randomly scattering onto a visual map the set of points representing all existing pairs within dual channels of measurement in the data ontology widget.

Note: This draft focused on improving grammar, style, and readability. As a technical document, it would also benefit from including diagrams or figures to help illustrate complex concepts or processes.