[pyinstallwoes]

So the alphabet a to z… on their own the symbols mean nothing but when compared to every other letter meaning arises. Then iterate / recursively out for every growth in structure and letter to letter, words to words, paragraphs to paragraphs. Each one has a “dependent arising” of meaning based precisely on the relation to other.

Which is more or less word2vec as far as I understand but then trying to extrapolate that as a universal principle to all things that can be represented by using a “common signature : hash based off a signal like a complex waveform” and then doing a difference on signal composition and its shape/bandwidth to compare its properties to other things and when they reference similar objects even in different modalities they’d be associated by being triggered together.

So “dog” vs image of dog would both translate to a primordial signal : identity representation and in the domain of frequency do the comparison and project a coordinate in the spatial sense and eventually those two nodes would more likely be triggered at the same time due to the likelihood of “dog” being next to image of dog when parsing information across future events.

Whew. Maybe I’m just talking to myself. At least it’s out there if it makes sense to anyone else.

[minimaxir]

> So “dog” vs image of dog would both translate to a primordial signal : identity representation and in the domain of frequency do the comparison and project a coordinate in the spatial sense and eventually those two nodes would more likely be triggered at the same time due to the likelihood of “dog” being next to image of dog when parsing information across future events.

That is how CLIP embeddings work and were trained to work.

Hugging Face transformers now has a get_image_features() and get_text_features() function for CLIP models to make getting the embeddings for different modalities easy: https://huggingface.co/docs/transformers/model_doc/clip#tran...

[pyinstallwoes]

Yeah but it doesn’t use a universal method does it? And it requires labeling.

The method I’m describing requires no labeling. Labeling would be a local only translation (alias). Labels emerge based on meaning. But the labels are more of an interface - not the actual nodes themselves which arise off the not identity principle * event proximity * comparisons.

[warkdarrior]

Labeling (which is typically manual and thus not scalable) is a proxy for comparisons. Two things are the same if they have the same label. The question is how else to encode the comparison information.

[pyinstallwoes]

Right one is manual the other is automatic and my hypothesis is you can have automatic universal labeling the way I describe

[pyinstallwoes]

The key requirement in my mind here is that the “universal identifier” is a form of attempting something like a deterministic signature for all things. The hunch is based on the hypothesis that the primordial representation of any and all things is frequency.

But of course each “ontological capable system” would still need to process the identity function to start making sense of things based on signals being unlike other signals, so deterministic is shallow but concrete.