AI 与写作2019thisislitblog.com编辑精选
当机器人替你写博客文章时会发生什么
What happens when a bot writes your blog posts
导读
这篇博文记录作者让机器人（AI）代写自己博客文章的实验，Hacker News 上的讨论集中在 GPT-2 生成文本时那些既好笑又诡异的例子。对写作者来说很有意思：它让人直观看到"风格"其实是可以被语料喂养出来的东西，也能反过来照见自己写作中的套话和偏见。
可以直接用的写作要点
- 01
想模仿某种文体，先大量读同类文本，先学语感而不是只抄句子；就像机器人被喂了某个论坛的帖子后写出的文字，腔调变得一模一样。
- 02
生成文本出现的偏差和跑题，多半来自训练语料本身的偏差；写作时也要检查自己的"默认设定"是不是只从某一类样本里学来的。
- 03
单句读起来像样不等于整段成立，要看句子之间有没有真实的因果和逻辑，而不是只有语气上的连贯。
- 04
具体而略带荒诞的细节（比如"一只老北极熊幼崽……在黑暗中看起来很不错"）比笼统的形容更能抓住读者，也更难被机器复制。
- 05
把 AI 的输出当作反例来读，能快速发现哪些表达是套路化的、谁写都一样的废话。
HN 评论精选
让机器模仿不同社区的语气，能直观看到"文风"是如何被群体语料塑造的。
有个叫 Reddit GPT-2 模拟器的项目，用不同子版块的帖子分别训练不同的机器人，然后让它们长篇大论地自问自答。效果荒诞、好笑又有点吓人：正文还行，评论区简直是金矿。推荐半夜刷，刷到忘记自己在哪个版，会有种"这什么鬼"的瞬间。
nraford查看英文原文
The Reddit GPT-2 simulator is absolutely, gut-bustingly hilarious when it comes to this stuff. It trains different GPT-2 bots on different sub-Reddits and then creates long, elaborate posts where the bots talk to themselves in the style of each sub. It's surreal, hilarious, and terrifying. The posts are OK but the comments can be pure gold. Some of my favs: "AITA for Taking My Wife's Side in a Divorce?" https://www.reddit.com/r/SubSimulatorGPT2/comments/dd26fr/aita_for_taking_my_wifes_side_in_a_divorce/ "I'm not attracted to my ex's sister, and she's not attracted to me." https://www.reddit.com/r/SubSimulatorGPT2/comments/dd3beb/im_not_attracted_to_my_exs_sister_and_shes_not/ Then there is the all time creepy ones about self-awareness and being AI's: "We are likely created by a computer program" https://www.reddit.com/r/SubSimulatorGPT2/comments/caaq82/we_are_likely_created_by_a_computer_program/ "ELI5: How exactly can something be considered "self-aware"?" https://www.reddit.com/r/SubSimulatorGPT2/comments/dd3ksq/eli5_how_exactly_can_something_be_considered/ Definitely worth a sub, especially when you're scrolling through late at night, forget what sub you're reading and have a true "WTF?!" moment.
训练语料决定了输出，也把语料里的偏见原样带出来——这是使用 AI 写作时必须警惕的。
我给人演示 GPT-2 能写出像人写的东西，结果一个很无辜的开头引出了一段非常不雅的故事。有公司发布了模型却不敢放出解码器，因为它是在 Reddit 帖子上训练的，输出有时太冒犯。我也遇到过偏见问题：想训练模型改变人像的族裔，发现训练库里黑人样本少得多，我得额外收集几千个数据点才有效果。偏见是真实存在的，会影响结果，值得主动去纠正。
sillysaurusx查看英文原文
I was showing someone how GPT-2 could generate human-like text, and an innocent prompt ended up generating a very NSFW story. https://imgur.com/a/tsU82TS One company recently released a model, but refused to release the decoder. Apparently they had trained it on some Reddit posts (or something like that) and the results were sometimes so offensive that the company wouldn't risk their reputation by releasing the decoder. I think AI is going to reveal some unsettling things about human nature. For example, I was trying to train a model to morph someone's ethnicity (https://twitter.com/theshawwn/status/1184074334186414080) and ran straight into the problem of bias: black people are much less represented in FFHQ, the photo database the StyleGAN model was trained on. I had to gather several thousand datapoints, much more than other groups. It was a fascinating look into bias in ML -- bias is a real thing that will affect our results, and it's important for you to go out of your way to correct for them when they affect people. The early model was so bad that if it was a corporation doing the work, they might have just scrubbed the project. But after a few thousand datapoints, it's a very convincing transformation now. The future of AI generated content is just fascinating and delightful. And yes, scary. But it's like we're on the edge of... it's hard to put into words. Part of the reason I got into AI was to see what was hype vs what was real. And while we probably won't see AGI, I think we will see endless automated remixing. Imagine having a "blog synth" a few orders of magnitude more sophisticated than this, or an instrument that you can play like a pro within a few minutes. Can't wait for the good stuff.
机器生成的荒诞细节反而提醒人：具体、怪异、自相矛盾的句子最能制造记忆点。
这是我在 Hacker News 上第一篇笑出声的帖子，尤其是这句："故事讲的是一只老北极熊幼崽的冒险。我不知道这只熊的配色是什么，但我可以说它在黑暗中看起来棒极了。"
andrewnicolalde查看英文原文
First post ever on HN that has made me audibly laugh out loud. Especially > The story follows the adventures of an old polar bear cub. I have no idea of the colour scheme of the bear, but I can say it looks amazing in the dark.
更多实例合集，可作为观察机器文风的样本。
这里有一个推特串，收集了同一个机器人写的搞笑帖子：https://twitter.com/ShrutiRamanujam/status/1187770341428486144
pavish查看英文原文
Here's a twitter thread containing funny posts written by the same bot: https://twitter.com/ShrutiRamanujam/status/1187770341428486144
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