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I've been wondering for awhile now - what was ever the benefit to building these things in Python, other than pytorch and numpy being there to experiment on in

I've been wondering for awhile now - what was ever the benefit to building these things in Python, other than pytorch and numpy being there to experiment on in hobbyist ways? There's no way that a serious AI is really going to be built in a scripting language, is there? Once you know what you actually want it to do, you're definitely going to rebuild it as close to the metal as you can, right? Not to mention, to really protect source code and all the sugar around the training systems, it's going to be a good investment to get out of hobby land and manage your own memory and just code them in C/C++. It strikes me that the hobby AI ethos aligns very well with scripting languages in that they both assume the availability of endless resources to push things a little dirtier and messier and see if anything interesting emerges. Which is great for hobby AI. It's probably not the future, though, unless resource availability outpaces the imagination of people to write more and more bloated scripts to accomplish what's already been proven.

nologic01 3y ago
incredibly condescending which always pairs well with ignorance. "Hobby AI" are the people (mathematicians, domain experts etc) that made this all possible so that you can now "just code it in C/C++". Have you ever tried to iterate developing any serious class of algorithms in C++? > Once you know what you actually want it to do When is that exactly? Even the last few months of LLM land development show very clearly how everything is rapidly evolving (and will very likely continue for quite some time). Numerical linear algebra stabilized decades ago so you do have low-level libraries in C++ (or even fortran) but there is quite some distance between an LLM and linear algebra.
- fisf 3y ago
  > Have you ever tried to iterate developing any serious class of algorithms in C++? Yes, and it's not so bad. A lot of ML deployments have been based on C/C++ for inference anyway (with Python driving the training). So that's really nothing new. I.e. most Python research code is not deployable in terms of quality / performance.
  - nologic01 3y ago
    > Yes, and it's not so bad. In the absence of alternatives its workable. Libraries like eigen or armadillo help a lot. But Python with numpy has been extremely popular for a good reason.

nologic01 3y ago

incredibly condescending which always pairs well with ignorance. "Hobby AI" are the people (mathematicians, domain experts etc) that made this all possible so that you can now "just code it in C/C++". Have you ever tried to iterate developing any serious class of algorithms in C++? > Once you know what you actually want it to do When is that exactly? Even the last few months of LLM land development show very clearly how everything is rapidly evolving (and will very likely continue for quite some time). Numerical linear algebra stabilized decades ago so you do have low-level libraries in C++ (or even fortran) but there is quite some distance between an LLM and linear algebra.

fisf 3y ago
> Have you ever tried to iterate developing any serious class of algorithms in C++? Yes, and it's not so bad. A lot of ML deployments have been based on C/C++ for inference anyway (with Python driving the training). So that's really nothing new. I.e. most Python research code is not deployable in terms of quality / performance.
- nologic01 3y ago
  > Yes, and it's not so bad. In the absence of alternatives its workable. Libraries like eigen or armadillo help a lot. But Python with numpy has been extremely popular for a good reason.