Not a stupid question, this issue is actually addressed in the essay, in the section about interior modeling vs unsupervised learning. The latter is very vague and general, while the former is much more specific and also intrinsically difficult. The difficulty and preciseness of the objective make it much better as a goal for a research community.
Your reply seems to treat “unsupervised learning” and “self-supervised learning” as synonyms, but I don’t think they are. Self-supervised is more specific. Things like clustering and dimensionality reduction and feature extraction are not examples of self-supervised learning, I don’t think. Predicting the next few seconds of a video, or predicting the next word of a text, or deleting a word from the middle of a sentence and training your model to guess what it is, would all be examples of self-supervised learning.
Not a stupid question, this issue is actually addressed in the essay, in the section about interior modeling vs unsupervised learning. The latter is very vague and general, while the former is much more specific and also intrinsically difficult. The difficulty and preciseness of the objective make it much better as a goal for a research community.
Your reply seems to treat “unsupervised learning” and “self-supervised learning” as synonyms, but I don’t think they are. Self-supervised is more specific. Things like clustering and dimensionality reduction and feature extraction are not examples of self-supervised learning, I don’t think. Predicting the next few seconds of a video, or predicting the next word of a text, or deleting a word from the middle of a sentence and training your model to guess what it is, would all be examples of self-supervised learning.