Two fundamental ideas that could transform how AI understands and interacts with the world
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Complex patterns can be represented with minimal information when we understand their underlying structure.
Example: 1000 equilateral triangles on a screen.
The same visual information can be stored as either 6000 coordinates or ~10 parameters that describe the pattern.
Store every vertex position
(1000 triangles × 3 vertices × 2 coordinates)
• Large storage requirement
• No pattern understanding
Store pattern parameters
• Same visual result
• 600:1 compression ratio
Every aspect of a system that is orthogonal (independent) can be represented separately.
Similar to eigenvectors - finding the most efficient basis for representing information.
Part of intelligence means knowing what level of detail you need to solve a problem.
Example: Modeling a biological cell
From billions of DNA base pairs to just the parameters you need.
Viewing whole cell structure and major organelles.
Level 1-3: Cell Type
Shape, size, basic structure
100-1,000 parameters
Level 4-7: Organelle Analysis
Mitochondria, nucleus, ER detail
1,000-10,000 parameters
Level 8-10: Molecular Dynamics
Proteins, DNA, chemical reactions
10,000-100,000 parameters
Combine optimal compression with adaptive complexity
Result: AI that can efficiently model any system at the right level of detail
With these algorithms in place, the path to AGI becomes clearer.
The combination of optimal knowledge compression and adaptive complexity could enable AI systems that truly understand and model the world at any required level of detail.
"When you see how you can go from billions of DNA pairs to just 10k numbers that describe the same complexity... it's pretty crazy."