I feel 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, 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.
Here's someone else who cherry-picked a quote and ignored the central argument: https://news.ycombinator.com/item?id=14347068
Almost every one of your sentences are talking past the parent. If you want to be argumentative instead of helping the parent as he asked, then it might be best to find actual quotes to disagree with.
> That's. All. It. Does.
No. That's. WHAT. It. Does.
Your emotional overreaction to this site has induced bias leading to a false negative -- mistaking hype for signal. Fact is, using deep learning, newbie grad students have routinely and resoundingly outperformed nearly all the state-of-the-art techniques in signal processing in less than FIVE years of trying. Image analysis and speech recognition have been revolutionized; 40+ years of R&D expertise has been largely supplanted in the blink of an eye. Dismiss this tsunami at your own peril.
Does DL scale to harder problems? Why yes, it does. No computer implementation of the game Go had outperformed the best humans until deep learning was used, then it beat the best humans almost immediately (unlike chess which took decades to reach that level using traditional AI methods). I have little doubt that significant achievements await DL's application to more complex tasks like language analysis too. To believe otherwise would be severely short-sighted.
Does deep learning also solve trivial pattern rec problems like regression or curve fitting as efficiently as some linear ML models? No, but who cares? By that reasoning, we should dismiss Einstein because many 12 year old children can multiply numbers faster and for less money.