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The dimensionality of data isn't the dimensionality of the encoding medium (e.g. images -> 2D data). All data can be re-encoded with a different number of dimensions. What matters is the dimensionality of the latent manifold. The structure of correlations in your data.
So it is definitely not the case that painting is 2D, music is 1D, and mathematics is infD. Mathematics, like computing, is typically encoded as manipulations on strings of symbols, so by that logic it would be a 1D problem.https://twitter.com/pmddomingos/status/1251341882828795904 …
Arguably all of these domains have the same "dimension", which is the intrinsic dimensionality of the human mind.
(I'm expecting pedantic, braindead replies about how mathematics is not a product of the human mind 
)
Is there a lot of work going on in data compression? Last I checked it was only two forms of mathematics and applied to the fax, video and music but have there been any good advances lately? I should think it is highly applicable to 5G
I can only agree on this. This is often why NLP is sometimes perceived as "simpler" than image/video since it can be encoded efficiently. But latent semantic space of language is much bigger (could I dare to say infinite?).
This is an interesting thought.. Can one speak more things than one can see? Or can everything that can be spoken also be (visually) perceived? They're definitely overlapping sets, but I'm not sure which is bigger..
Isso me lembra da correlações de pcas com os dados normalizados, pra garantir os principais pcas
I have not seen Dimensions explained in simpler english
Reminds me of ”the map is not the territory”. Isn't the encoding just a projection from one dimension to another? Question: With multimodal data (e.g. image caption), what can be considered the dimension of the latent manifold within the model?
@DanceGrist now add some music and dance
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