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Thanks for letting me put my story on your blog. Mainstream media is crap and no one would have believed me anyway.
This starts in September 2017. I was working for a small online ad startup. You know the ads on Facebook and Twitter? We tell companies how to get them the most clicks. This startup – I won’t tell you the name – was going to add deep learning, because investors will throw money at anything that uses the words “deep learning”. We train a network to predict how many upvotes something will get on Reddit. Then we ask it how many likes different ads would get. Then we use whatever ad would get the most likes. This guy (who is not me) explains it better. Why Reddit? Because the upvotes and downvotes are simpler than all the different Facebook reacts, plus the subreddits allow demographic targeting, plus there’s an archive of 1.7 billion Reddit comments you can download for training data. We trained a network to predict upvotes of Reddit posts based on their titles.
Any predictive network doubles as a generative network. If you teach a neural net to recognize dogs, you can run it in reverse to get dog pictures. If you train a network to predict Reddit upvotes, you can run it in reverse to generate titles it predicts will be highly upvoted. We tried this and it was pretty funny. I don’t remember the exact wording, but for /r/politics it was something like “Donald Trump is no longer the president. All transgender people are the president.” For r/technology it was about Elon Musk saving Net Neutrality. You can also generate titles that will get maximum downvotes, but this is boring: it will just say things that sound like spam about penis pills.
Reddit has a feature where you can sort posts by controversial. You can see the algorithm here, but tl;dr it multiplies magnitude of total votes (upvotes + downvotes) by balance (upvote:downvote ratio or vice versa, whichever is smaller) to highlight posts that provoke disagreement. Controversy sells, so we trained our network to predict this too. The project went to this new-ish Indian woman with a long name who went by Shiri, and she couldn’t get it to work, so our boss Brad sent me to help. Shiri had tested the network on the big 1.7 billion comment archive, and it had produced controversial-sounding hypothethical scenarios about US politics. So far so good.
The Japanese tested their bioweapons on Chinese prisoners. The Tuskegee Institute tested syphilis on African-Americans. We were either nicer or dumber than they were, because we tested Shiri’s Scissor on ourselves...
(cont'd)
https://slatestarcodex.com/2018/10/30/sort-by-controversial/
Designing an algorithm to optimize controversy - now with political consequences!
@Jack V Savage I noticed you linked to Slate Star the other day, you catch this one? Creeped me out pretty thoroughly.