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I'm curious if you see this as a good thing, bad thing, or a bit of both? I did lots of enterprise programming in 1999 to ~2004 and had to struggle with stupid

I'm curious if you see this as a good thing, bad thing, or a bit of both? I did lots of enterprise programming in 1999 to ~2004 and had to struggle with stupid stuff like creating wire protocols or FTP modules. Working in tech is so much fun now as I can do the actual work without worrying about foundational stuff. And for those who like to do foundational stuff, they can work for one of the vendors who create the foundational stuff. In the 1990s in college, it was worse, I had to sometimes write some data structures, etc because there wasnt a good standard utilities package. I'm pretty tired with all the grunt work I need to do these days as a machine learning practitioner. I'd love some abstraction or at least automation of the rote work. Keras is great at some of this, but i'd love a lot more of it. I'm VERY INTERESTED in hearing everyone's ideas on this, this is a big pain point for me, to the extent that i'm trying to actually build a solution around it.

m3kw9 9y ago
To me deep learning architecture is what CPU architecture are from yeaterday. Abstraction bring speed and standardizeation over customization, and most people doesn’t need a lot of customizations. So to answer you, yes you will get a lot more tooling in the near future, the only thing stopping it is how fast AI changes. With the speed of progress, if someone discovers a brand new brilliant idea that absolutes they way you train a system, then you could start back at ground zero
barbersturgeon 9y ago
Could you elaborate on what you feel is currently grunt work? Do you mean the difficulty in building and debugging complex models or something more abstract?
sgt101 9y ago
To me the grunt work is getting data and for supervised, getting labelled data. Algorithm implementation and design is fairly straightforward and lots of fun. Of course, getting really good results in a domain is hard work - but again it's the fun bit. Then there are the domains and problems that can't be treated with off the shelf algorithms... well good luck!

m3kw9 9y ago

To me deep learning architecture is what CPU architecture are from yeaterday. Abstraction bring speed and standardizeation over customization, and most people doesn’t need a lot of customizations. So to answer you, yes you will get a lot more tooling in the near future, the only thing stopping it is how fast AI changes. With the speed of progress, if someone discovers a brand new brilliant idea that absolutes they way you train a system, then you could start back at ground zero

barbersturgeon 9y ago

Could you elaborate on what you feel is currently grunt work? Do you mean the difficulty in building and debugging complex models or something more abstract?

sgt101 9y ago

To me the grunt work is getting data and for supervised, getting labelled data. Algorithm implementation and design is fairly straightforward and lots of fun. Of course, getting really good results in a domain is hard work - but again it's the fun bit. Then there are the domains and problems that can't be treated with off the shelf algorithms... well good luck!