> stop the world GC (my car ran you over because of a random unpredictable pause!)
Given a certain baseline of available resources and for the places where GC issues can be problematic (regimes I do not believe common) unpredictability of GC is the main issue, which can be readily addressed. You can for example, allocate and then manually manage or opt for a specialized JVM. If startup-time is an issue there are native compilation options. Or just not use the JVM, that's an option too.
I'll also note that Automatic reference counting is no panacea either and Python is no better suited for the scenario you've given. Personally, I've not found this to be an issue given modern low pause concurrent GC but your mileage may vary.
> its bytecode is object-oriented
> impossible at the moment to do CUDA style GPU programming without horrible JNI calls
I don't think these are deal breakers. More easily writing kernels in a high level language is very much an open problem. Even Tensorflow faces this issue, with most workflows optimized for a handful of prewritten kernels. On the JVM there are options such as https://index.scala-lang.org/thoughtworksinc/compute.scala/b... or http://aparapi.com/ for GPU backed ND-arrays or JVM translation.
> F# has two big crippling factors: (1) the .NET at the end of its name and (2) functional programming, which is a huge barrier for 99% of developers (and it's not functional enough for the other 1%). F# is dead in the water--I mean that respectfully. To replace Python for deep learning, you need a language that other people will actually use, not should.
I don't think (1) is true or even if so, see its technical relevance. If you're doing numeric/array-based differentiable programming then you shouldn't have any problem with functional programming. I'd even argue functional programming makes things easier as you get many things free from existing combinators. Many concepts are naturally expressed with the features such languages tend to have.
I don't think Python needs replacement, I'd much more rather see interoperability and language agnosticism. I can tell you that for each of Julia, Haskell, Scala, Kotlin, Ocaml, Nim, F#, Rust and of course Swift, at least one fascinating machine learning library is being built. I think that's a great thing.