AGI vs AI



How Current AI Video Generators vs. My AGI Blueprint Handle a Simple Prompt


Prompt: “Make a rotating apple glowing with the text ‘apple’ on it.”

(NOTE: My AGI is not prompt based, it has perpetual thought.)


Current AI Video


How It Thinks


  1. Breaks down prompt into keywords: "apple", "rotating", "glowing", "text"
  2. Embeds text into latent space vector
  3. Samples noise and refines it using learned training data
  4. Interpolates a plausible video: vague red object, maybe rotating, glowy lighting, floating text (hit or miss)
  5. Zero understanding of what an apple is, how rotation works, or text placement

Cognitive depth: 0 — pure style prediction

My AGI Blueprint


How It Processes the Prompt:


  1. Language Parser Module: Parses prompt into symbolic command
  2. Conceptual Resolver: Links “apple” to stored schemas with attributes and meanings
  3. Visual Thought Constructor: Builds an internal 3D scene with apple mesh, glowing effect, rotation, and text
  4. Memory Mapper: Stores scene as a retrievable chunk linked to linguistic cues
  5. Rendering System: Uses a renderer (Unity, Blender) to visualize the scene grounded in intent

Cognitive depth: 10/10 — full symbolic simulation and meaning grounding

Summary:

Feature Current AI Video My AGI Blueprint
Understands what an apple is? ❌ ✅
Knows how rotation works? ❌ Mimics rotation superficially ✅ (simulates motion)
Places text logically? ❌ (floats randomly) ✅ (intentionally placed)
Can remember & reflect? ❌ ✅
Has intent? ❌ ✅
Produces output with semantic meaning? ❌ ✅
Editable via symbolic feedback? ❌ ✅
Replicable for reasoning tasks? ❌ ✅

“Current AI videos are like kids smearing paint with their eyes closed.
Mine builds the object, names it, rotates it in mind, and then chooses how to show it — because it understands what it means, by seeing it. First we imagine, then we do.”


Difference in Rotation Application: AGI vs. AI Video

This section explains how rotation math is applied differently in typical AI video generators compared to my AGI blueprint.


1. Purpose & Context of Rotation

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Aspect AI Video Generator My AGI Blueprint
Why rotate? To generate visually plausible frames matching “rotation” To simulate the object’s spatial state within a mental model supporting reasoning and memory
What rotates? Pixels or latent vectors representing image features Symbolic 3D model of the apple, including geometry and semantic properties

2. Level of Understanding


Aspect AI Video Generator My AGI Blueprint
Rotation math applied? Yes, as a visual effect — often 2D transforms or latent-space shifts Yes, as part of 3D spatial transformations integrated into symbolic simulation
Is rotation conceptually understood? No — just “make frames look like rotation happened” Yes — rotation is meaningful in object state and environmental interaction
Linked to other cognition? No — isolated pixel/feature manipulation Yes — affects memory, reasoning, planning, narrative context

3. Integration with Other Systems


Aspect AI Video Generator My AGI Blueprint
Memory & recall No Yes
Reflection & reasoning No Yes
Output control Limited to generating frames based on prompt Full control over symbolic scene & animation, modifiable via symbolic feedback loops


How AI Rotates an Object vs How AGI Does It


AI video generator: Generates frames of color blobs vaguely resembling a turning apple.

My AGI: Builds a 3D mental model of the apple, applies rotation matrices to its coordinates, understands axis, speed, lighting, and renders a semantically rich scene with intentional text placement.


Summary


Key Difference AI Video My AGI
Math application Surface pixel/feature transformation to approximate rotation visually Deep geometric transformation tied to symbolic model and cognition
Understanding None Full conceptual model integrated with memory, reasoning, and reflection
Result Video clip with illusion of rotation Interpretable mental scene with semantic depth and recallability