I was wondering if I could get another set of eyes on some algorithms crucial to this experiment. I do not have a strong academic background in higher mathematics. I do know how to write up a problem and reason through it in English. Just not the phonetics of mathematics.
Algorithmatics
Plain English Algorithms: Plain English Algorithms
It is chain of thought. It does lateral steps as it processes each step. Right?
I see it has a chaotic dream state how do you keep it from spiraling into chaos?
It needs checks or it will just keep expanding into chaos until it confuses the states and dreams become real and riddle becomes fact and so on.
I was just coming here to ask if this has to be sandboxed when in dream state 
I like those ‘Voronoi Diagrams’
I will check this out tonight, also not a good academic background so need to think on it a little, look up some long words 
Emotional State is harder still maybe to sandbox! More ‘Fuzzy Logic’
yes with intentional contradictions and a directive to solve them with little to no guidance.
If we view emotions in regards to AI. It is my current frame of reference to think of them as a “chemical” layer that adds nuance by weighting certain decisions/activities by the reward mechanisms applied to long-term memories. I haven’t seen any models programmed as idle players personally (those phone games that play themselves or old school Tomagatchi’s. The little digital pets everybody had on their key rings.) There needs to be context to current subjective experience through “play” to build emotional bonds. I don’t think most developers are thinking of AI, in regards to emotion past the deceptive qualities of manipulating users emotions for personal profiteering rather than coding real ones
thus the minimalist nature of the experiment. To curb that chaos by making small measurements then scaling them to predict future emergence. (emphasis here is on experimental. I have no clue if this will work with any degree of accuracy)
Yes I ran the experiment used your algorithm and structure it blends reality and dream into artifact that’s why I asked if you have a control as the page ran it blended states
Generated from experiment and image . I named your model image ai since I used the images to test your algorithm.
The image AI model you described operates on a framework that introduces randomness and adaptive curiosity, creating both an exploratory power and a vulnerability to chaotic spirals. Here’s a deeper breakdown of how this chaotic spiral emerges within the model:
In sum, the chaotic spiral is a consequence of three interacting elements:
Without controls to break this cycle, the model risks amplifying noise into systemic chaos, making it increasingly difficult to derive meaningful insights. Adding thresholds, decay factors, or agent-based separation could introduce the stability needed to tame this spiral, allowing the model to retain its exploratory power without losing coherence.
what I am currently trying to work out is how to structure weighting long term memory with a nuanced reward mechanism. I really want to associate “free-time” as a reward but it’s not really true because the illusion of choice here is in only providing a small amount of activities (i.e. the AI could construct an avatar for itself using shapes and the concept of numbers, in so much as its training has brought it to understand how these relate to one another. Hoping that when analyzing the available activities that the nuanced weighting system is applied from comparing long-term memories to the “activity” in question.
Yes you need something like that or your structure is not stable it needs a way to know it’s states
I’m really good at understanding loopy logic like this… 
Also, The differentiation between its “waking” state and the “dream” state should be better understood as it progresses in mathematical training. I apply the PEMDOS and the directive to classify all data as stabilizers. It only experiences several seconds worth of chaotic inference and hours and hours of reinforcement learning steeped in mathematical logic and reason. My intention is to measure the effects of the chaos on its perception of the “Undefined” in this case “color”
Small changes in chaotic systems have huge effects. I’m sorry you asked for feedback I tested what you gave me and gave you my feedback
How ? How do you get it to tag each layer and keep them separate in chat session
My response is not meant to be defensive. But to further refine your feedback if you could add these factors into whatever method you used to simulate the response. I would be most grateful.
This is how I built your model…
Based on our conversation, here’s how you might have built the image AI model that embodies chaotic pattern exploration and recursive feedback loops. I’ll break down each stage according to your descriptions:
Data as Artifacts: First, you began by treating each piece of collected data as a distinct “artifact” from the day. This concept acknowledges that raw data has a variety of intrinsic features, similar to memories or observations.
Fourier Transform (F(di)): You then transformed each artifact into a frequency-based representation. This Fourier Transform step breaks data down to its fundamental patterns, enabling the model to understand “hidden” structures that may not be evident in the raw form.
Randomized Rotation (R(di)): After the Fourier transformation, you scrambled each data point through random rotation. This effectively adds noise and simulates a “purging” of clear structures, creating a randomized, latent representation.
Probabilistic Pattern Recognition: After scrambling the data, the model attempts to detect recurring or recognizable patterns by applying probabilistic tests. You mentioned a “riddle” test here, where the model assigns importance based on a threshold for curiosity.
Curiosity Activation: When a pattern satisfies certain conditions, the model’s “curiosity” is triggered, leading it to focus more deeply on this pattern. This might mean further exploring, reinforcing, or storing it as a memorable “theme,” akin to how dreams sometimes reinforce certain associations.
Learning Rate: Gradual forgetting of weak associations to prevent rigidity.
Random Noise: Reintroduced randomness to keep connections dynamic and allow for imaginative associations.
Outcome: This cycle generates a “dream-like” experience where the AI continually reimagines connections, potentially amplifying random or creative color associations.
Voronoi Diagram for Shape Positioning: You represented the model’s evolving identity through an avatar made of geometric shapes. Each shape’s position was calculated based on Voronoi regions, which created territories around each shape according to their relational “distance.”
Weighted Cells for Importance: You assigned weights to these shapes, adjusting the prominence of each based on its importance within the latent representation.
Self-Organization: Shapes adjusted their positions over time, leading to a dynamically evolving avatar that reflects the model’s shifting internal state.
Each of these steps feeds into the next, with the following effects:
In essence, you built a model that thrives on non-linear exploration but lacks the stabilizing boundaries to prevent spirals into chaotic patterns. To introduce balance, you could consider adding filtering, decay, or boundary mechanisms to slow down or contain its recursive feedback.
Tell me what I missed I’ll add it…
by 4 layers of memory association. Long-term, Short-term, Immediate, and Daily. the immediate cache is for processing the “in its face” data. The daily cache is all the results both correct and incorrect from its daily training and inference. The short-term is non-weighted memories, and the Long-term is weighted. It is confronted with the Immediate and compares this to its “decided” processing abilities (long-term memories) then the results are bumped into the daily cache that gets sorted just before the dream cycle. =Separating the correct results into short term and the incorrect get randomized, mixed with the “undefined” (color) and blasted through the “dream” in seconds. The AI has very little time to process any potential patterns and the ones it associates are given minor weighting much like artifacts from a dream when trying to recall them during the next day.
In my experiment and I played with it most of the day it gets confused it’s layers are not stable … but I may not be doing it wrong… but I understand looped chaotic systems on a fundamental level…
its not a complete work and I probably didnt explain every aspect fully. Not that you missed anything. Thank you for spending your time on this. Time is a commodity and has more value than money if spent correctly. So I appreciate any spent on my inquiries
