I think if you can make the vectorization methods more customizable and transparent, this could be a research accelerant too, since as a lot of AI R&D on new do

I think if you can make the vectorization methods more customizable and transparent, this could be a research accelerant too, since as a lot of AI R&D on new domains or datasets has "make a good embedding" as a first step. It's not a very hard step right now, but I think you can probably make it faster to rapidly prototype something and then iterate on it, so long as you set it up to be possible to do the latter (IE inspectable, interoperable, etc) I think even though some people doubt the value of being able to compare disparate types via embedding, allowing it to be done more seamlessly makes a kind of "silly" (or more charitably "playful") research I happen to like a lot more feasible. In particular, artificially-produced "synesthesia" that comes from tuning weird embedding comparisons could end up being really useful in some domains, because like human synesthetes, the underlying structure of one domain might provide counterintuitive insight or legibility into the other in some cases But all of this requires that the library allows fine-tuning and retraining of the underlying embeddings. It would be useful to natively support coembeddings of different domains, as things like CLIP drove the current wave of multimodal generative models.