4 ms·
I'm fairly familiar with ML, and I'd say that's a definitive no. Implementing a layout spec is exactly the kind of thing that is "easy for computers, hard for
by davidatbu 4y ago
I'm fairly familiar with ML, and I'd say that's a definitive no.
Implementing a layout spec is exactly the kind of thing that is "easy for computers, hard for humans". ML is for things that are "hard for computers, easy for humans" (like, telling dogs apart from cats, or transcribing speech, etc).
- NaturalPhallacy 4y agoMy favorite example is differentiating between blueberry muffins and chihuahuas. :)
- eigenvalue 4y agoYou could probably get something that would work pretty well for most common things but wouldn't have the same behavior in more complex edge cases.
- davidatbu 4y agoI'd bet even that would be only with a some or all of (1) a LOT of training data, (2) a LOT of preprocessing, and (3) using less famous architectures (perhaps recursive neural nets). I say so because (among other reasons) in general, current popular ML architectures (like transformers) count processing heirarchical data as one of their weaknesses. For example, there's a theoretical paper proving that self attention blocks (which are central to transformers) cannot solve arbitrarily nested negation(Ie, resolve not(not(not(true))) to true). Practically as well, we see most times that language models have trouble dealing with naturally occurring double negation in language, etc. But CSS/HTML is, I think, very heirarchical.