I can definitely see it speeding up manual efforts along the data processing pipeline, but data engineering is more than schema generation, just as NLP is more than feature extraction from structured documents.

Very interested in what new tools and workflows would emerge from this though!

Given the constant evolution of information through time combining LLMs with retrieval and reasoning modules is the way forward imo

It was Marco Polo (and his contemporaries) who heard something akin to “yit pun kok” and mapped it to “cipangu”.

“mei guo/mei kok” is likely more related to how “America” was first transliterated into “mei li jian/mei lei keen” rather than for expressing beauty, although that choice is reflected in the written character :)

I believe there’s ongoing work related to this at OpenAI and similar research in generative models in general, like this submission currently under review.

What is “good” music?

Certainly not the full picture, but imo one of the reasons we don’t see music generation taking off the same way as image/text is it’s more difficult to evaluate and therefore benchmark and iterate.

It faces similar challenges as generative modelling in other modalities, but is arguably more subjective, time-consuming, and require more training if using human evaluation. A layperson can easily tell if an image or text is “good”, it’s more complex for music once it gets above a certain minimum quality threshold.

From a business perspective it’s harder to sell too given the scope of applications (relative to language and vision), as interesting as the problem sounds to us.

Plus, echoing the other comment, I feel it’s reductionistic to flatten music into spectrograms when there are interlaying elements. My intuition is it’ll be better to model dependencies between individual “tracks” as well. I’m sure there’s extensive work on music generation with good results already, just not quite in the spotlight yet.