![]() The Japanese tested their bioweapons on Chinese prisoners. ![]() Shiri had tested the network on the big 1.7 billion comment archive, and it had produced controversial-sounding hypothethical scenarios about US politics. ![]() 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. Controversy sells, so we trained our network to predict this too. 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. Reddit has a feature where you can sort posts by controversial. 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. All transgender people are the president.” For r/technology it was about Elon Musk saving Net Neutrality. I don’t remember the exact wording, but for /r/politics it was something like “Donald Trump is no longer the president. If you train a network to predict Reddit upvotes, you can run it in reverse to generate titles it predicts will be highly upvoted. If you teach a neural net to recognize dogs, you can run it in reverse to get dog pictures. We trained a network to predict upvotes of Reddit posts based on their titles.Īny predictive network doubles as a generative network. 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. This guy (who is not me) explains it better. Then we use whatever ad would get the most likes. Then we ask it how many likes different ads would get. We train a network to predict how many upvotes something will get on Reddit. 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”. You know the ads on Facebook and Twitter? We tell companies how to get them the most clicks. I was working for a small online ad startup. Mainstream media is crap and no one would have believed me anyway. Thanks for letting me put my story on your blog.
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