Merge pull request #94 from Phuire-Research/UI

Included mention of ABI

README.md

@@ -28,6 +28,8 @@ The inspiration for STRX was that of Redux and its origin via the FLUX design pa
 
 Noting that in the clip above, the speaker is using behavior trees and the stopping term. Here within STRX, a behavior tree would be an ActionStrategy that is dispatched via a staged "Plan." What separates STRX from the approach above is that we are using the finite state machine pattern to avoid the use of the infinitely looping check of some observed value. In addition we are referring to this as halting.
 
+As the ability to halt within an intelligent system is the demonstration of its logical consistency. This can be directly determined via a systems generation of hallucinations and misinformation. A intelligent system that is capable of halting, would likewise be capable of determining if it can satisfy some input, without providing a generalized answer that appears to solve that output. There is strength in the ability to recognize the possibility of some solution within these hallucinations, but likewise the ability to recognize such would be an additional ability to halt. Would merely be, "I do not know how to solve that problem with what's available, but if I had access to these features I could solve that problem."
+
 ## How STRX Solves this Problem
 Further the Unified Turing also accomplishes what has been considered to be an impossible to solve problem of the original theoretical Turing Machine. The halting problem, this is accomplished via the finite state machine pattern in conjunction with the new ActionStrategy pattern. This new pattern is capable of representing any calculation, but must be designed with a conclusion. Thus the finite state machine of STRX can perform any calculation and halts upon their conclusion. Noting that here we are using logic to solve this and utilizing a set of specified requirements to have said solution. The primary requirement to satisfy the solution is that the main run time of a program, must be a recursive function. The ActionStrategy pattern in addition satisfies the next requirement, via being a specific set of instruction that concludes, but is capable of a branching behavior that affords for error correction. The specific interest in presenting this solution at this time is to demonstrate a method of safety as to disallow some run away effect from an Artificial Intelligence or Neural Network.
 
@@ -188,10 +190,10 @@ We could limit use case like the its inspiration as a state machine to control f
 
 This system may become more atomic over time to better represent the dynamics of language. But during this specific stage, this is a fine starting point towards the decomposition of the complexities of intelligence. As the weighted sum is still massively useful, when there is no direct obvious answer to find some solution to an input. But within this scope would be an entirely different type of Neural Network which does not currently exist. As that would be the generation of novelty over some averaged statistical gradient descent of known data. Would be finding new arrangements of concept and their qualities to find the next **Ah-Ha!**
 
-## What this Methodology is Proposing
+## What this Methodology is Proposing: ABI
 ![Rick and Morty - Butter Bot](https://github.com/Phuire-Research/STRX/blob/main/butterBot.gif?raw=true)
 
-What this methodology creates is the ability to formalize "Aut Intelligence," or baseline intelligence. Aut origins was originally a pictogram of an Ox and is the originating symbol for the letter A. and the purpose of creating this distinction is to categorize a form of artificial baseline intelligence that is predictable and safe. Where even if we achieve some super intelligence, we should not have that super intelligence pass butter. But instead together we should create some baseline intelligence that is written in plain text, that is only capable enough to pass butter.
+What this methodology creates is the ability to formalize "Autonomous Baseline Intelligence," or ABI. And the purpose of creating this distinction is to categorization of a form of intelligence that is predictable and safe. Where even if we achieve some super intelligence, we should not have that super intelligence pass butter. But instead together we should create some baseline intelligence that is written in plain text, that is only capable enough to pass butter and can be proven to halt.
 
 This would be the safest route of artificial intelligence deployment. Where we may have the truly intelligent models that might cross some threshold, be spared this type of realization. Further this would also allow us to design specialized chips to to run these intelligences within a limited specification. Going as far as printing the limited instruction sets that would formalize that aut intelligence itself onto the chips for additional safety.