It's the one question that AIs seem unable to answer correctly.

Calculating the JSD could be more difficult, the expression uses a mixture between the 'true' and 'fitted' distribution. You can still simulate this, but half the time you'd be fitting the model to itself, and I just don't see why that would be useful.

I think the JSD is most useful when you need an actual metric, but as long as you have a fitted and target distribution the KL divergence is a natural fit since you can interpret the result as information loss.

Actually I think I recall some GPUs storing textures that way, but I'm not entirely sure.

This is literally the same with every single technological development.

Sometimes using something well involves not using it at all.

I mean if you've got a feature set to merge into dev, and it suddenly merges into main after someone merged dev into main then that's very annoying.

I mean a branch is just jamming a flag into a commit with a polite note to move the flag along if you're working on it. You make a long trail, leave several flags and merge the whole thing back.

Of course leaving multiple waypoints only makes sense if merging the earlier parts makes any sense, and if the way you continue actually depends on the previous work.

If you can split it into several small changes made to a central branch it's a lot easier to merge things. Otherwise you risk making a new feature codependent on another even if there was no need to.

But it's an insane move. If anything AI has made it more important than ever to know who authored something and then someone does this to promote AI.

That's two neat tricks that I'm definitely adding to my bag of python trickery.

If you don't care about efficiency you can just do len(set(text.split())), but that's barely worth making a function for.