I know the steel made before this particular year is look after because was not affected or build during periods of radiation, and that gives it qualities sought after.
When AI started I got a copy of 6 months before of wikipedia and open street map.
At some point you cannot trust anything because everything has been touched by everything as a regurgitation of everything.
So what's the solution? I imagine the right one isn't specific to fan wikis. We're going to need sites where 100% of contributed content is human-written. Who is building this? How will it work?
If the solution merely makes it high friction to post AI content that is probably good enough. In other words if someone has to copy/paste the output of an LLM into a text field and even then the tech detects that and throws an error that would be a big step in the right direction.
Reddit, Wikipedia, fan wikis, reviews, Hacker News comments - I'm sure there are a dozen more categories I'm missing.
Wait until you have to audit your average codebase post-AI.
AI is rotting pretty much everything that was carefully curated over the last couple of decades. If it can be expressed in bits it will be degraded. This is one of the most annoying side effects of AI, the general decrease in quality. Quality of discourse, quality in literature, quality of information, quality of software. It's everywhere.
There are at least different kinds of messes, and the new kind may offer different challenges which may eventually require different (easier? harder? invented-yet?) techniques than the ones we're used-to.
The problem I'm already having with LLMs (in miniature) is code/comments/docs where I don't know whether it's someone's intent or whether its un-reviewed LLM babble. That creates a continual stumbling block for figuring out whether the thing should be the way it is, or whether I'm looking at a mistake.
Before code would be intentionally a certain way, done as a known compromise that's less than ideal, or the developer flubbed in some way. Using commit comments and other surrounding details you might be able to identify which in many cases which could guide your next steps. I don't think you can do that with an LLM's output.
I know the steel made before this particular year is look after because was not affected or build during periods of radiation, and that gives it qualities sought after.
When AI started I got a copy of 6 months before of wikipedia and open street map.
At some point you cannot trust anything because everything has been touched by everything as a regurgitation of everything.
In case anyone isn't familiar with that type of steel: https://en.wikipedia.org/wiki/Low-background_steel
So what's the solution? I imagine the right one isn't specific to fan wikis. We're going to need sites where 100% of contributed content is human-written. Who is building this? How will it work?
If the solution merely makes it high friction to post AI content that is probably good enough. In other words if someone has to copy/paste the output of an LLM into a text field and even then the tech detects that and throws an error that would be a big step in the right direction.
Reddit, Wikipedia, fan wikis, reviews, Hacker News comments - I'm sure there are a dozen more categories I'm missing.
Fandom (ex-Wikia) has been a dumpster fire since day one.
As witness the choice to squat on the name "Fandom" when they were notoriously averse to treating fandoms reasonably.
Wait until you have to audit your average codebase post-AI.
AI is rotting pretty much everything that was carefully curated over the last couple of decades. If it can be expressed in bits it will be degraded. This is one of the most annoying side effects of AI, the general decrease in quality. Quality of discourse, quality in literature, quality of information, quality of software. It's everywhere.
I’m not sure I’d call most enterprise code repositories, carefully curated. I think lava flow is way more standard https://en.wikipedia.org/wiki/Lava_flow_(programming)
There are at least different kinds of messes, and the new kind may offer different challenges which may eventually require different (easier? harder? invented-yet?) techniques than the ones we're used-to.
The problem I'm already having with LLMs (in miniature) is code/comments/docs where I don't know whether it's someone's intent or whether its un-reviewed LLM babble. That creates a continual stumbling block for figuring out whether the thing should be the way it is, or whether I'm looking at a mistake.
Before code would be intentionally a certain way, done as a known compromise that's less than ideal, or the developer flubbed in some way. Using commit comments and other surrounding details you might be able to identify which in many cases which could guide your next steps. I don't think you can do that with an LLM's output.