I think in Python, I saw a good deal of adoption in the Sciences early on.
I worked on a Physics experiment almost 15 years ago, there were inklings of Python dripping in to doing numerical analysis.
NumPy just using existing Fortran libraries is a good example of it just being bootstrapped by the scientific community.
Most of these guys come from a Fortran background, and some used the C++ ROOT framework.
Ruby was just too alien for them.
ROOT had made some excellent Ruby bindings, and I spent some time showing some of my colleagues how much time they could save using them, but they just found it to be too different.
It's sort of funny to think, because languages made for AI originally like LISP used functional concepts like map, collect, etc etc...
I think the AI trend using Python was boosted by numerical analysis/computing libraries already being present.
I worked on a Physics experiment almost 15 years ago, there were inklings of Python dripping in to doing numerical analysis.
NumPy just using existing Fortran libraries is a good example of it just being bootstrapped by the scientific community.
Most of these guys come from a Fortran background, and some used the C++ ROOT framework.
Ruby was just too alien for them.
ROOT had made some excellent Ruby bindings, and I spent some time showing some of my colleagues how much time they could save using them, but they just found it to be too different.
It's sort of funny to think, because languages made for AI originally like LISP used functional concepts like map, collect, etc etc...
I think the AI trend using Python was boosted by numerical analysis/computing libraries already being present.