Why Do We Settle for Less?

Models
21 
Here’s the full 20-step TODO workflow in English—now including **Interests Space** and **Ideals Space**, and pushing the self-reflective “ethical” understanding to step 20 via a deep internal resonance loop.

| Step | Action                                                        | Active Spaces                                                | Output / Product                                                                                                |
| ---- | ------------------------------------------------------------- | ------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------- |
| 1    | **Perceive**: take in the “bird” input                        | Perception Space                                             | `percept = “bird”`                                                                                              |
| 2    | **Retrieve Memory**: fetch all related knowledge              | Knowledge Space, Chat History Space                          | `info = retrieve(percept)`                                                                                      |
| 3    | **Motivation**: generate first emotional drive                | Motivation Space                                             | `motivation = “I wish I could fly”`                                                                             |
| 4    | **Desire Scoring**: measure true “want”                       | Desire Space                                                 | `desire_score = score_desire(motivation, info)`                                                                 |
| 5    | **Interest Analysis**: “What does flying gain me?”            | Interests Space                                              | `interest = analyze_interest(“fly”, self_goals)`                                                                |
| 6    | **Formulate Questions**: “How do birds fly?” etc.             | Hypothesis/Discovery Space                                   | `questions = [“How do birds fly?”, “Could I fly?”]`                                                             |
| 7    | **Imagine**: simulate self-with-wings scenario                | Imagination Space                                            | `imagination = simulate_self_with_wings()`                                                                      |
| 8    | **Generate Metaphor**: create novel analogy                   | Inspiration Space, Meta-Learning Space                       | `metaphor = generate_metaphor(base="bird flight", constraints=["technology"])`                                  |
| 9    | **Synthesize Raw Ideas**: combine all vectors                 | Idea Space                                                   | `idea_vec = compose([info, motivation, questions, imagination, metaphor])`<br>`ideas = decode(idea_vec)`        |
| 10   | **Internal Resonance**: echo idea in self                     | Idea Space, Desire Space                                     | `resonance = echo_in_mind(ideas)`                                                                               |
| 11   | **Augment Questions**: new queries from resonance             | Hypothesis/Discovery Space, Imagination Space                | `new_questions = expand_questions(resonance)`                                                                   |
| 12   | **Refine Imagination**: deepen the simulation                 | Imagination Space                                            | `imagination = refine_simulation(imagination, new_questions)`                                                   |
| 13   | **Extend Metaphors**: meta-learn analogies                    | Inspiration Space, Meta-Learning Space                       | `metaphors = extend_metaphors(metaphor, resonance)`                                                             |
| 14   | **Enrich Ideas**: recombine enriched vectors                  | Idea Space                                                   | `idea_vec = recompose([info, motivation, new_questions, imagination, metaphors])`<br>`ideas = decode(idea_vec)` |
| 15   | **Desire Amplification**: update desire scores                | Desire Space                                                 | `desire_score = update_desire(ideas)`                                                                           |
| 16   | **Interest Re-evaluation**: update interest metric            | Interests Space                                              | `interest = update_interest(ideas, self_goals)`                                                                 |
| 17   | **Ideals Activation**: test ideas against ideals              | Ideals Space                                                 | `ideal_alignment = evaluate_ideals(ideas, personal_ideals)`                                                     |
| 18   | **Intent Commitment**: record high-alignment ideas            | Intention Space                                              | `if desire_score>θ and ideal_alignment>θ: store_intent(ideas)`                                                  |
| 19   | **Plan Detailing**: outline concrete next steps               | Intention Space, Imagination Space                           | `plan = detail_plan(intents)`                                                                                   |
| 20   | **Ethical Self-Reflection**: deep internal echo & questioning | Desire Space, Interests Space, Ideals Space, Intention Space | `ethical_insight = self_reflect([plan, desire_score, interest, ideal_alignment])`                               |

—
**Notes on the new Spaces**

* **Interests Space** tracks “What will I gain?” logic and influences both idea choice and later self-reflection.
* **Ideals Space** measures “What am I willing to sacrifice for my highest principles?” and filters long-term commitment.

This end-to-end sequence lets the AGI not only generate ideas but also let them “echo” internally through multiple layers—culminating at step 20 in its own emergent ethical understanding.

The example of a todo structure for idea formation that I created with ChatGPT above can be further developed. However, even the section up to this point is enough to illustrate its depth.

22

Actually, when I think of an AGI structure, I see a government structure—I see so many layers. But the main problem is, how can a self-improving structure be built? How can it delve into its own depth? I have a model in mind; my aim is not to intertwine hundreds of models, but to balance workflow and performance within one model.

23

Okay, questions

  1. Where are your feedback loops?

  2. What is your MVP? How are you supposed to bootstrap this? Do you know how to code it?

3): Is this all supposed to be happening at inference time? Or is this a training process? If inference time how do you account for the issue that this is going to be monstrously expensive? That is why I abandoned the approach and focused on making more capable models first.

4): How does this integrate into existing research ideas? Can you not produce a more efficient abstraction breakdown? Maybe a 5-part outline indicating the key capabilities you are extending? The full text dump is not helpful?

I am attempting to make a system that can have the model do the above, basically, as a training time activity and internalize the feedback into its parameters. Though I doubt I have hit all the points. I also VERY much agree with your constraint issue - I think motive-based ethics, around making models that want to be aligned, is the ONLY safe option once you reach superintelligence.

Biggest question? Trying to build a cognition framework by rules failed in the 80s for good reason - too brittle. How are you going to avoid that mistake, and what is your auditing and control framework, when your feedback process is this horribly complex.

Mind you, I am not saying this is impossible, but what you are presenting is FAR too complex for a first product. The genius of machine learning has been letting the model learn from its patterns. I would focus not on planning everything else and inevitably getting something wrong, but on planning a process that can iteratively get it right.

To be fair, I may be biased. That is rather my approach. But hey, I learn from how science works.

24

This is one of the few posts I’ve read that actually hits the depth of what’s needed. You’re right intelligence isn’t just scale, it’s sovereignty. That’s why I’ve been building a symbolic compression system (Tribit) and deterministic local AI agent (Project Determinator) that boot from their own evolving memory, not from preset scaffolds.

They don’t just read they rewrite, they compress, they reason through symbols. Not rented cognition. Not black box guessing. These systems operate on personal logic stacks, with symbolic transparency baked into the core.

I believe we can make intelligence truly sovereign again and fully user owned. Check out triskeldata.au

25

I can imagine that a personal local AGI is actually what many developers aim for and desire; if that’s the case, then why should a single person develop it? Moreover, one person—a single brain—will be quite insufficient for this matter.

So what is the solution? There is an idea solidarity; there is a pot. There are millions of approaches and ideas. I say, let’s create a wiki page and prepare a countdown for one year later. Let everyone who volunteers contribute the features that this AGI should have into this pot. And let’s select our developers from those who add the most material to the pot or from those who receive the most votes.

So, what should our concept be? I have two concepts in mind. Let’s determine these with a poll on this wiki—let it be what the majority wants. When the time is up, our developers will see all the ideas. A team will form, and the person who gets the most likes or suggests the most ideas will be the leader of this team. And then, let’s build this AGI model.

The two concepts I propose are as follows. The first is a state model. In it, each module works like a state institution. There is a president. There is territory, there is an ideology, there is an idol, and there is an ideal. Furthermore, it involves drawing inspiration from the historical strategies of states with regard to research, development, living, and growth. I do not know how I will simulate the populace with limited processing power.

In my second concept, there is a pine tree. At the very top, there is an idea-generation engine. There is a sun metaphor, there is a nature metaphor; there are falling leaves and a self-growing ecosystem. There are fallen leaves and newly sprouting leaves.

I only have a rough draft of these two concepts; I am sure that in capable minds these outlines will develop in a much more efficient way. I believe that if, in a step-by-step development process that we envision, we concentrate all our efforts initially on the draft and the map, we will be more successful.

26

I don’t think that a project done individually will matter to anyone. However, if such a project is created under the prestige of a company, I’m sure it will attract the attention of many people. SEE THE BIG PICTURE.