| Though I'm still pissed at Kagi about their collaboration with Yandex, this particular kind of fight against AI slop has always striked me as a bit of Don Quixote vs windmill. AI slop eventually will get as good as your average blogger. Even now if you put an effort into prompting and context building, you can achieve 100% human like results. I am terrified of AI generated content taking over and consuming search engines. But this tagging is more a fight against bad writing [by/with AI]. This is not solving the problem. Yes, now it's possible somehow to distinguish AI slop from normal writing often times by just looking at it, but I am sure that there is a lot of content which is generated by AI but indistinguishable from one written by mere human. Aso - are we 100% sure that we're not indirectly helping AI and people using it to slopify internet by helping them understand what is actually good slop and what is bad? :) We're in for a lot of false positives as well. |
Hey, Kagi ML lead here.
For images/videos/sound, not at the current moment, diffusion and GANs leave visible artifacts. There's a bit of issues with edge cases like high resolution images that have been JPEG compressed to hell, but even with those the framing of AI images tends to be pretty consistent.
For human slop there's a bunch of detection methods that bypass human checks:
1. Within the category of "slop" the vast mass of it is low effort. The majority of text slop is default-settings chatGPT, which has a particular and recognizable wording and style.
2.Checking the source of the content instead of the content itself is generally a better signal.
For instance, is the author posting inhumanly often all of a sudden? Are they using particular wordpress page setups and plugins that are common with SEO spammers? What about inboud/outbound links to that page -- are they linked to by humans at all? Are they a random, new page doing a bunch of product reviews all of a sudden with amazon affiliate links?
Aggregating a bunch of partial signals like this is much better than just scoring the text itself on the LLM perplexity score, which is obviously not a robust strategy.