29galoseats

I had a similar reaction about this being a little premature.

On the other hand, I'm wondering if this will help a little with the dependency hell that's caused me to drift away from Julia over the last year or so.

At first I was fairly excited about Julia, and greatly preferred it over R or Python for numerical work, library resources aside. It was fast and I liked the language design itself.

Over the last year or two, though, I've had recurring problems with trying to install and use packages, and them failing at some point due to some dependency not working. Sometimes the target package itself won't install, but most of the time it's some lower-level package.

It's more frustrating than not having packages available, because it creates some sense that packages are available, when they're not. At first I looked at it as some idiosyncratic case with one package, but it's happened over and over and over again.

Basically in this time I've given up on Julia, because there's such a huge discrepancy between what it looks like on the surface and what it looks like in practice, once you get out of certain limited-use cases, or when you're not coding everything yourself from the base language. (Related concerns about misleading speed claims have been raised, although my personal experience in that regard has been mixed, because my experience overall has been pretty good in some critical performance cases, but there have also been some wildly unpredictable exceptions that act as bottlenecks... but it's still much better than R or Python).

When I've tried to figure out what's going on with dependency hell, usually it's because some package that was previously working with a earlier version is no longer working. So maybe stabilizing the language will help that?

Vitanyi and colleagues have a paper where they show that although K(X) is uncomputable, in practice, you can attach a probability to one X having greater or lesser K(X) than another X (to ridiculously oversimplify; K(X|M) might be better notation). This is kind of analogous to p-values and is related to the statistical interpretations of Kolmogorov complexity. The uncomputability is important but is not the intractable obstacle it seems like if not elaborated on.

I think there are ups and downs with genres. I think horror is currently in a new golden age (not the only one--there have been others in the past), and actually think the article is part of this trend. It's easy to suggest that the current wave of outstanding horror reflects a kind of "backhanded compliment" when it was preceded by overworn cliche films, for decades.

This is recognized within the horror fan community as well. There are lots of people on various forums talking about the cornucopia of great horror at the moment, and my guess is a number of fans will spend years catching up on their expanding lists of films to see and rewatch. It's really remarkable.

There's a broader context that I think the author is missing, or at least ignoring: film and visual arts critics don't care about genre, they care about film. Genre tends to create a comparative context that doesn't necessarily exist if your scope is film at large. So, horror films exist to cater to a horror film fan base, romantic comedies exist to cater to that, superhero films cater to that, science fiction, fantasy, and so forth and so on. If it is a backhanded compliment, my guess is the same could be said of any genre to some extent.