Notably, all of those are static languages and none of them have array types as nice as PyTorch or NumPy, among many other packages in the Python ecosystem. Those two facts are likely closely related.
Python is just the more popular language. Julia array manipulation is mostly better (better syntax, better integration, larger standard library) or as good as python. Julia is also dynamically typed. It is also faster than Python, except for the jit issues.
The "issue" is Julia is not Just-in-Time, but a "Just-Ahead-of-Time" language. This means code is compiled before getting executed, and this can get expensive for interactive use.
The famous "Time To First Plot" problem was about taking several minutes to do something like `using Plots; Plots.plot(sin)`.
But to be fair recent Julia releases improved a lot of it, the code above in Julia 1.10 takes 1.5s on my 3-year old laptop
Julia's JIT compiles code when its first executed, so Julia has a noticable delay from you start the program and until it starts running. This is anywhere from a few hundred milliseconds for small scripts, to tens of seconds or even minutes for large packages.
I wonder why they don't just have an optional pre-compilation, so once you have a version you're happy with and want to run in production, you just have a fully compiled version of the code that you run.
Effectively, it does - one of the things recent releases of Julia have done is to add more precompilation caching on package install.
Julia 1.10 feels considerably snappier than 1.0 as a result - that "first time to plot" is now only a couple of seconds thanks to this (and subsequent plots are, of course, much faster than that).
If only there were a dynamic language which performs comparably to C and Fortran, and was specifically designed to have excellent array processing facilities.
Unfortunately, the closest thing we have to that is Julia, which fails to meet none of the requirements. Alas.
