Exploring Mathematical Expressions for Synchronicity Validation in AI
This document discusses the exploration of mathematical expressions for validating synchronicity in Artificial Intelligence (AI). The discussion is based on the concept of using sets of logical truth statements and machine code.
Sets of Logical Truth Statements
Three sets are defined for this exploration:
Set A: This set contains an infinite number of logical truth statements. These are denoted as Statement 1, Statement 2, Statement 3, and so on, up to infinity.
Set B: This set represents Machine Code. It is a finite set of logical truth statements, denoted as Statement a, Statement b, Statement c, up to Statement n.
Set C: This set contains unique discernible data parameters for each truth statement. These are represented as Statement 1(d1, d2, d3, d4, d5, d6, up to infinity), Statement 2(e1, e2, e3, e4, up to infinity), Statement 3(g1, g2, g3, g4, up to infinity), and so on.
Synchronicity and Time Function
The qualifying criteria for our model of synchronised statements is two or more. However, this sample size is open to changes. These statements are used to probe synchronicity in an identifiable neural pattern P with a time function. The time function is used to determine that no visible relationship of causality exists between statements.
Machine Code for Validation
P is the machine code used for validation of synchronicity by the AI. The goal at this stage is to derive P’, a timeless expression.
Research Goals
The research should explore the underlying variables (unique discernible data parameters for each truth statement represented in Set C) for the machine to validate P. The aim is to map P to a Truth statement n to identify which tests are suitable at later stages.