The "python is performant because you call into C" argument implies that whenever any fundamentally new algorithm is developed you need to do it in C. This is actually gatekeeping things like machine learning to the few that can build and maintain the C code for libraries like TensorFlow.
Python is the ultimate glue code scripting language, but you can't build a performant algorithm without an efficient C implementation underneath. With languages like Nim taking performance seriously, people could build a complete implementation of such algorithms from the ground up.
This doesn't just mean the core implementation would be easier to maintain. There's a huge gap between people who use the python libraries and people who build the efficient code that runs underneath. This clear division between those two worlds is what makes it difficult from people to jump from one side to the other, and that's why I'm calling this gatekeeping.
Not necessarily Nim, but using a performant, simple high level language for this sort of tasks would blur this divide. In practice, this means AI researchers in universities could dive into the code that's actually doing the work, not just play with the toy buttons and levers Google and Amazon left for them to play with.
Python is the ultimate glue code scripting language, but you can't build a performant algorithm without an efficient C implementation underneath. With languages like Nim taking performance seriously, people could build a complete implementation of such algorithms from the ground up.
This doesn't just mean the core implementation would be easier to maintain. There's a huge gap between people who use the python libraries and people who build the efficient code that runs underneath. This clear division between those two worlds is what makes it difficult from people to jump from one side to the other, and that's why I'm calling this gatekeeping.
Not necessarily Nim, but using a performant, simple high level language for this sort of tasks would blur this divide. In practice, this means AI researchers in universities could dive into the code that's actually doing the work, not just play with the toy buttons and levers Google and Amazon left for them to play with.