the range of problems you can solve with ML/AI is simply too wide for there to be fully-canned solutions for everything. Sure, there will be canned solutions for _some_ things - maybe even for cat detection, because it's fun so why not.
But, a library that uses AI to optimize the production of your business' flux capacitors? Ain't gonna happen, you need to build that yourself. To have a library/product that solves problems using AI, you need a "language" to describe the problem (like you can e.g. use SQL to describe any data query you may have). But describing problems is notoriously hard - accurately & precisely describing the problem is very often just as hard as solving it.
Mm, it's a bit like arguing that "the range of text editor customization is simply too wide for there to be fully-canned solutions for everything." Meanwhile, elisp wiki go brr.
I think ML solutions will increasingly take the form of an elisp script rather than a python library, but it'll take a little while to get there.
> it's a bit like arguing that "the range of text editor customization is simply too wide for there to be fully-canned solutions for everything."
But the the range of editor customization really isn't that wide. That's exactly what I'm arguing, that ML/AI is more like "math" than like "editor customization".
But, a library that uses AI to optimize the production of your business' flux capacitors? Ain't gonna happen, you need to build that yourself. To have a library/product that solves problems using AI, you need a "language" to describe the problem (like you can e.g. use SQL to describe any data query you may have). But describing problems is notoriously hard - accurately & precisely describing the problem is very often just as hard as solving it.