I love stuff like this, and I think "unreasonable" is almost an understatement. It's "unreasonable" mainly because it occasionally captures subtle aspects of t

I love stuff like this, and I think "unreasonable" is almost an understatement. It's "unreasonable" mainly because it occasionally captures subtle aspects of the data source for "free". If you've worked with procedurally generated content, Markov chains, and so on, you probably have had to perform a few tweaks in order to get plausible results[1]. From the article, an excerpt of the output from an RNN trained on Shakespeare: Second Lord: They would be ruled after this chamber, and my fair nues begun out of the fact, to be conveyed, Whose noble souls I'll have the heart of the wars. Clown: Come, sir, I will make did behold your worship. VIOLA: I'll drink it. Sure, the individual blocks are similar to what you'd get from a Markov text generator-- but it gets that after a full stop, there comes a newline, a new character name, and a new text block. To my eyes, this is a qualitative leap in performance. It suggests that the model has figured out some things about the data stream that you'd normally have to add in by hand[2]. It's also unreasonable that the same framework works well for so many different data sources. My experience with other generative methods has been that they were fragile and prone to pathological behaviour, and that getting them to work required for a specific use case required a bunch of unprincipled hacks[3]. It used to be that when a talk started to veer towards generative models, I'd start looking around the room, wondering whether I could survive the drop from any outside-facing windows. But with RNNs using LSTM (or neural Turing machines!) you can consider incorporating a generative model in the solution you're putting together without having to spend a huge chunk of time massaging it into usefulness and purchasing time on a supercomputer[4] 1. I once wrote quick a Reddit bot with the aim of learning to repost frequent highly upvoted comments and trained it using a simple k-Markov model... it was not good at first, and in order to get it to work I had to do a lot of non-fun stuff like sanitizing input, adding heuristics for when/where to post, and at the end it was mediocre. 2. Alex Graves (from DeepMind) has a demo about using RNNs to "hallucinate" the evolution of Atari games, using the pixels from the screen as inputs. It's interesting because it shows that same sort of tendency to capture the subtle stuff: https://youtu.be/-yX1SYeDHbg?t=2968 https://youtu.be/-yX1SYeDHbg?t=2968 3. As in occult knowledge and rules-of-thumb, but you might also read this as a double entendre about myself and my colleagues. 4. Well, you still might need an AWS GPU instance if you don't have a fancy graphics card.