4 ms·

I think it's all just being more accessible for people like me to integrate into applications via AIs. With that said - the barrier for entry also lowered subst

I think it's all just being more accessible for people like me to integrate into applications via AIs. With that said - the barrier for entry also lowered substantially in the last few years to a point where implementing your own neural network from scratch with Pytorch is something you can do in a few (took me a weekend) hours coming in blind, but reading the docs and scrolling some existing repositories. I remember a decade ago taking a stab at it and being very much over my head. Not sure if i'm getting older and there's less magic going on around it or if it's the ecosystem/docs/examples/pytorch/jax/hours of youtube content/etc but somewhere in there, at least for me personally, it's gotten much more accessible.

amilios 3y ago
Personally I think it's a combination of a) much better resources being available than in the past, which explain concepts clearly and in relatively simple terms, and b) much better tooling existing now than ever before, so many things that you used to need to do "by hand" are now taken care of by relatively standardized tooling. Even just automatic differentiation engines are a huge deal, not having to backprop "by hand" (first release of TensorFlow was 7 years ago, so if you looked at it a decade ago I'm assuming you had to implement backprop yourself). Beyond that another jump was from Tensorflow's delayed execution/compilation model (it really was a headache to work around its APIs/set up the computation graph) as well as just having a generally ugly API, to PyTorch's "literal" setup, where it feels like you're just writing regular Python code and performing operations "normally"