I think that would be pretty awesome and amazing, to be honest.
I think that would be pretty awesome and amazing, to be honest.
Imagine the best book you've ever read. Entrancing, enlightening, cathartic. You reach the end, and it's ... perfect. Oh hey, a sequel. Wow, the sequel is just as good as the first book. It expands upon it without diminishing the original -- you feel better, more complete for having read it. Wait, is that a third book in the series? Wow, it's even better than the first two! A fourth -- well, maybe you should go to work now, it's Monday, but the book is so good. Calling in sick once won't hurt anything.
Imagine a perfect series of books, published without end, each better than the last, a new one coming out weekly ... daily ... hourly ...
Scary :)
The practical problem with this is that, as I understand it, the deep learning system needs a pretty large data set to work with to infer rules from. You can do this with go because there is a constraint on legal moves and a deterministic win condition, but given how vast the number of potential novels is (If we count the space of all ten thousand word collections of grammatically acceptable sentences) the existing number of novels may no be enough to infer a pattern. (Though possibly you could split the problem up by separately doing the natural language processing and abstractin out the plot)
In the end all life is is one choice after another, and making good ones over bad mostly leads to a happier life.