The Rosetta Stone geometric vocabulary and the ramping up capacity. 
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The Rosetta Stone geometric vocabulary and the ramping up capacity. 
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What makes this particular invariant special, is the existence within all structures I've tested so far. I had Claude write up the direct article based on what we built together, but I've tested it on many substructures. This is flawed, and I have a series of answers to making it more accurate.
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What makes this particular invariant special, is the existence within all structures I've tested so far. I had Claude write up the direct article based on what we built together, but I've tested it on many substructures. This is flawed, and I have a series of answers to making it more accurate.
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First a reconstruction from the ground up. This means each shape is specifically built upward from the substructure to the point of inductive deviance. This will be less quick at first and then build speed as I optimize like the last system did.
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First a reconstruction from the ground up. This means each shape is specifically built upward from the substructure to the point of inductive deviance. This will be less quick at first and then build speed as I optimize like the last system did.
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The "saddle" problem; the system detected saddles because there wasn't enough deviance in the shapes to attenuate to more cardinality and more aligned substructures. The blobs were around 30-40% of the overall patches, which interpolated into the others produced a fair approximation.
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The "saddle" problem; the system detected saddles because there wasn't enough deviance in the shapes to attenuate to more cardinality and more aligned substructures. The blobs were around 30-40% of the overall patches, which interpolated into the others produced a fair approximation.
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It MOST DEFINITELY did see those shapes in their voxel complexity. This is real.
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It MOST DEFINITELY did see those shapes in their voxel complexity. This is real.
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https://claude.ai/public/artifacts/bf1256c7-726d-4943-88ad-d6addb263b8b
https://claude.ai/public/artifacts/bf1256c7-726d-4943-88ad-d6addb263b8b
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You can play with a public claude artifact dedicated to viewing the current shape spectrum - and with that know exactly why it's flawed.
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You can play with a public claude artifact dedicated to viewing the current shape spectrum - and with that know exactly why it's flawed.
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The flawed and repetitive shapes. I rapid prototyped and there are multiple redundant shapes that simply don't classify well or at all. Not to mention the rotation simply doesn't help much of the time, or doesn't exist with many shapes. This will be rectified in the next variation.
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The flawed and repetitive shapes. I rapid prototyped and there are multiple redundant shapes that simply don't classify well or at all. Not to mention the rotation simply doesn't help much of the time, or doesn't exist with many shapes. This will be rectified in the next variation.
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Projecting to shared latent space as a catalyst to allow growing subjective geoflow matched step variance, rather than simply direct classification. This will theoretically allow for full channel-to-channel invariant features to be mapped from structure to structure, and the very formula that encapsulated them to be directly baked into the math rather than classified as a substructure analysis.
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Projecting to shared latent space as a catalyst to allow growing subjective geoflow matched step variance, rather than simply direct classification. This will theoretically allow for full channel-to-channel invariant features to be mapped from structure to structure, and the very formula that encapsulated them to be directly baked into the math rather than classified as a substructure analysis.
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There are many challenges between here and there, so stay tuned my friends as I plot the geometric language of pretrained AI.
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There are many challenges between here and there, so stay tuned my friends as I plot the geometric language of pretrained AI.
The Rosetta Stone geometric vocabulary and the ramping up capacity. 

What makes this particular invariant special, is the existence within all structures I've tested so far. I had Claude write up the direct article based on what we built together, but I've tested it on many substructures. This is flawed, and I have a series of answers to making it more accurate.

First a reconstruction from the ground up. This means each shape is specifically built upward from the substructure to the point of inductive deviance. This will be less quick at first and then build speed as I optimize like the last system did.

The "saddle" problem; the system detected saddles because there wasn't enough deviance in the shapes to attenuate to more cardinality and more aligned substructures. The blobs were around 30-40% of the overall patches, which interpolated into the others produced a fair approximation.
It MOST DEFINITELY did see those shapes in their voxel complexity. This is real.

https://claude.ai/public/artifacts/bf1256c7-726d-4943-88ad-d6addb263b8b
You can play with a public claude artifact dedicated to viewing the current shape spectrum - and with that know exactly why it's flawed.

The flawed and repetitive shapes. I rapid prototyped and there are multiple redundant shapes that simply don't classify well or at all. Not to mention the rotation simply doesn't help much of the time, or doesn't exist with many shapes. This will be rectified in the next variation.

Projecting to shared latent space as a catalyst to allow growing subjective geoflow matched step variance, rather than simply direct classification. This will theoretically allow for full channel-to-channel invariant features to be mapped from structure to structure, and the very formula that encapsulated them to be directly baked into the math rather than classified as a substructure analysis.

There are many challenges between here and there, so stay tuned my friends as I plot the geometric language of pretrained AI.
👀
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