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I have been working on a knowledge base for things I'm leaning about bike parts. Notes on compatibility, part weights, bookmarks to helpful links, etc A big ch

I have been working on a knowledge base for things I'm leaning about bike parts. Notes on compatibility, part weights, bookmarks to helpful links, etc A big challenge I have is when I add a new tag, I'll want to apply it to relevant content I already have. But this takes a long time and until it happens the relevant data can't be retrieved by the tag. I am curious how the semantic hash is implemented in a conceptarium. If I could focus on adding content and it can drive relationships automatically, that is awesome.

paubric 5y ago
The conceptarium (currently) uses OpenAI's CLIP model which encodes both texts and images into a semantic space so that related items are close to each other. This means that if you have a picture of a bike pump and a written note about bike pumps, then they'll get similar hashes. This means that you can search for written notes on bike pumps by taking a picture of a bike pump (Google Lens style) and also search for pictures using text queries, or a mix. And oh man a dream of mine as an expat in the Netherlands is to learn how to take my bike apart and put it back together to better understand how it works and how to fix it myself. Though this idea never made it too high in my priorities list.