Embeddings are one of the most useful but unfortunately underdiscussed concepts in the artificial intelligence space relative to the modern generative AI gigahype. Embeddings are a set of hundreds of numbers which uniquely correspond to a given object that define its dimensionality, nowadays in a multiple of 128 such as 384D, 768D, or even 1536D. The larger the embeddings, the more “information” and distinctiveness each can contain, in theory.

These embeddings can be used as-is for traditional regression and classification problems with your favorite statistical modeling library, but what’s really useful about these embeddings is that if you can find the minimum mathematical distance between a given query embedding and another set of embeddings, you can then find which is the most similar: extremely useful for many real-world use cases such as search.

You wanna cut through the hype about AI? Here's the key takeaway: it boils down to a bunch of math nerds figuring out interesting relationships between numbers.

Which, of course, is useless to all of us non-math nerds... except for when you apply this information in the context of Pokémon.

Joking aside, I have a basic understanding of embeddings, but this article, with its basis in Pokémon lore, is the clearest explanation for how embeddings work in practice that I’ve seen.

Warning: there's still a lot of involved math happening here, but stay with it. You might learn a concept or two!