AI News, We Taught a Neural Network to Write a Blog

We Taught a Neural Network to Write a Blog

Our main company blog has over 8 million words and our technical blog is currently sitting at 114,000 words (though I just added another 2500 words through this post and the two generated posts!).

Initially, I didn’t have a specific project in mind but as I started my research I thought it’d be funny to use ML to create blog posts.

tl;dr I used Google’s Colaboratory, Max Woolf’s textgenrnn, and Jeremy Singer-Vine’s markovify to create a couple of funny blog posts by training it against our existing posts.

This article would be way too long if I covered what an RNN is and how it works, so if you’d like more information check out this great article by Andrej Karpathy.

When I tried to train the network on G Adventures’ main blog posts it was taking 9 hours per iteration!

I wrote a quick script that generated a blog post title and 7 paragraphs of text and moved on.

As I was waiting for the initial RNN training to be completed I researched how others managed to train their networks on relatively small datasets.

9 hours per iteration (90 hours total) seemed like a really long time and I thought there had to be a better way.

Markov Chain’s basically pair up all of the words in your text corpus (blog posts in this case) and determine the next word in a sentence based off the probability of pairs found in the past.

By default, the make_sentence method tries a maximum of 10 times per invocation, to make a sentence that doesn't overlap too much with the original text.

The generated blog posts won’t win any Pulitzers – they don’t even pass for English – but I found it funny and the company got a kick out of it.

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