Molecular descriptors, embedding and frequency

I was discussing how my friend used molecular descriptors and some form of backpropagation to find “similar” molecules for specific purposes. The process was similar or identical (if considered in general terms) with the “embedding” process in AI.. “Embedding” is supposed to convert a type of information in a different type so in the end apples and oranges can be compared. Restricting the output of e perceptron to 0 and 1 or -1 to 1 is also a way to make data comparable still an embedding problem.. Our brain seems to have found the ultimate embedding, transforming all data from all sources, in frequency (and phase) so everything can be compared.

I’m struggling with converting an image data (say RGB values) into something that can be used by the AI algorithm. If information is not converted “properly” (what’s proper is unknown) then the algorithm will fail to detect differences in the input data, fail to learn anything… which may actually be the reason why I spent so much time with no progress what so ever…

I’m still far away from showing anything concrete. My plan was to create a test program that shows how two colors can be learned, but I took a very very twisted approach and now I’m further away from my goal then when I started. Why ? Not sure.. Maybe I believe this is more complex than on paper ? Maybe I believe that even if this works is not proving anything ? So by observing my approach I must conclude that I’m getting ready for multiple problems which cannot be solved unless I understand very fast what the real problem is.. So I’ve been motivated only to construct more an more tools to better visualize the data and eliminate ambiguous options … and still hesitate to take more decisive steps.

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