I have an idea and I’d like to experiment it with a model.
Idea : teach a language or text based model with custom truths.
Prompt - Give me list of words in the model
Prompt - Give me list of action words in the model
Prompt - Build a fact tree of the model and return the text
Prompt - What is a fact ?
Answer - No answer
Prompt - A fact is a truth found after an experiment
Answer - (It doesnt know what is an experiment) What is an experiment
Prompt - feed information about experiment and how to conduct experiment
At this point, you have built a system that can take your natural language as instruction to model, and we can instruct it to take a shape that we intend to.
In other words, if I feed it grammar of a language, and the language, it runs against the grammar. If I feed it information on how to trace a control flow, then given a program, it can return information on the control flow that happens.
So what I’m trying to ask is, a way to make the model programmable, in natural language, force tell it to correct itself. If I try to correct it, I would want to visually see it happening. so having a fact tree will help me understand the model and be sure whether it actually learnt or not.
This also helps me to build a truth finder.
First step is to build a fact tree, where, subject part of a sentence is an action that happened in the past, and verb part of a sentence is action. At the very core of this fact tree is an ultimate truth, which is movement, time, space, and more units can appear above from it. Since we define a fundamental unit to this fact tree, and making the fact tree visual, it has information about its visual structure in itself, giving an opportunity to make it reach the state of singularity, In the beginning it may be just a visual singularity or consiousness, just to make the focus ring of the model to always remain in the core because thats where the truth is. The more the focus ring spends time on a node, more the memory get strengthened.
Terminology
Model - Nodes of information
Fact tree - A visual structure of the words in the model
Focus ring - Nodes spark up, based on the tuning level, or the text generation quantity, where ever, and whenever this focus ring moves across, a text is generated out. Just like how there is a focus part in our brain. This shape need not be ring, because it can be multi dimensional connecting dots from different pieces of the tree.
Fact tree - It changes shape visually. This is not performant technique.