Please don't let my comments that 'The problem people are having is figuring out input and output for domains other than image processing' suggest to you that I

Please don't let my comments that 'The problem people are having is figuring out input and output for domains other than image processing' suggest to you that I'm saying that no one is doing interesting things with machine learning, or that I think that the solvable problems with it are somehow limited in scope to just that. There are plenty of things you can do with it and its quite remarkable how effective it can be, you're quite right. I feel https://www.tensorflow.org/tutorials/word2vec https://www.tensorflow.org/tutorials/word2vec is really good example of what I'm actually talking about. Given a corpus, how do you represent it in a form that is suitable for processing? Actually, it turns out that's a really hard problem which a lot of work has gone into; but many tutorials and introductions to ML skip over it as though it were a trivial implementation detail on the back of which type of NN you decide to use, or how many layers you want. I wrote the parent comment because on reading http://aiplaybook.a16z.com/docs/intro/survey-goals http://aiplaybook.a16z.com/docs/intro/survey-goals, I got excited that maybe someone had actually broached this topic in a meaningful way. ...but then as I read through yet another guide to installing tensorflow followed by assert!(example 101 works), I lost all enthusiasm for it. I don't know; I'm happy to admit I'm perhaps overly critical, but this wasn't a false negative for me, it was just a frustrating unfulfilled promise. Just read the tensorflow tutorials, they're better.