From the study:

> We find enormous heterogeneity in algorithmic progress, with nearly half of algorithm families experiencing virtually no progress, while 14% experienced improvements orders of magnitude larger than hardware improvement (including Moore’s law). Overall, we find that algorithmic progress for the median algorithm family increased substantially but by less than Moore’s law for moderate-sized problems and by more than Moore’s law for big data problems.

I did look at the paper too so not sure why you need to just re-post an excerpt, and that is basically what I said. Like they said more than half of the families of algorithms they looked at basically are at a stand still.

I mean, of course you're not going to do much better at sorting, for example. But things like machine learning and optimization are advancing at a rapid pace, and I'm not sure I would characterize them as "open problems that no one is sure on yet." Unless you just want to apply that characterization to anything that's still improving, which would be a bit of a tautology.