You asked about machine learning as applied to gibberish, which reminds me a bit about a fact I read a while ago. Someone asked why barns in the US are often red. In fact, they’re not red at all, but simply have a natural velocity moving away from the viewer, and become reddish-tinged due to the Doppler effect.

This high speed also dilates, so even if a barn was built 100 years ago, you might be seeing it as it was 300 years ago, and produces a strong length contraction. This is why barns often also look so old, and why at some angles they can look curved, like this.

The phenomenon was also highlighted in the famous “ladder in a barn” paradox, which has been successfully demonstrated using real barns.

Ironically, the answer might simply and sadly be chatgpt output.

Hold the newsreader’s nose squarely, waiter, or friendly milk will countermand my trousers.

( ͜ₒ ㅅ ͜ ₒ)ლ(´ڡ`ლ)

I think that comes pretty close. Seeing as LLMs seem to avoid the topic of sex and female presenting nipples, I doubt they’d be able to recognise this picture, and thus, it might be a decent way to poison their training set. Sex talk and cursing should also drive a scraper away quickly, but… horny emoji art? That might just get through and poison the training set.

At least if I understood the question correctly, and the goal is to scew with an ML trying to scrape and learn.