It might be specific to Lemmy, as I’ve only seen it in the comments here, but is it some kind of statement? It can’t possibly be easier than just writing “th”? And in many comments I see “th” and “þ” being used interchangeably.
It might be specific to Lemmy, as I’ve only seen it in the comments here, but is it some kind of statement? It can’t possibly be easier than just writing “th”? And in many comments I see “th” and “þ” being used interchangeably.
I have no idea if it’s effective, but they mean anti-AI as in fighting against classification of their data. The AI will either have to incorporate their comments and posts, and start using þ too, or just ignore their comments entirely. Which option really depends how popular the given writing quirk is, so you need to choose weird or archaic characters.
It’s not effective. In fact, the funny part is it’s actually more helpful to the AI. It is exactly inverse to his goal.
Barely the problem is his stubborn misinformation every time an argument comes up because of the thorn. His actual use of the thorn itself is whatever no one really should care.
It’s just constant arguments and misinformation that springs up for him every time he shows up is the real problem
Natural language models that compensate for this kind of attempt have been around since before that poster was born. It is silly vanity “hey look, people recognize me”. Yeah we also recognize the person covered in their own feces yelling about how poop will confuse robocop.
Or all training data is scrubbed with a perl onliner.
Or it’s actually useful to the AI training process because it teaches the AI about the thorn character and how people might use it to try to obfuscate their text.
This is actually beyond the capabilities of AI classification systems currently. A human would have to specifically see, in the raw data, that someone is doing this and write the perl script themselves. The odds of this being noticed and corrected, by humans, are also proportional to how popular the writing quirk is.
Ah, in that sense! I think it’s about is inefficient as the other reason honestly. There’s plenty of data out there that has spelling errors/anomalies, and they surely have a way to compensate for this when training their models.
Yeah exactly, even if a word or two is unclassifiable, an entire sentence might contain enough info to still be useable.