Wikipedia's founder said he used ChatGPT in the review process for an article and thought it could be helpful. Editors replied to point out it was full of mistakes.
Wales’s quote isn’t nearly as bad as the byline makes it out to be:
Wales explains that the article was originally rejected several years ago, then someone tried to improve it, resubmitted it, and got the same exact template rejection again.
“It’s a form letter response that might as well be ‘Computer says no’ (that article’s worth a read if you don’t know the expression),” Wales said. “It wasn’t a computer who says no, but a human using AFCH, a helper script […] In order to try to help, I personally felt at a loss. I am not sure what the rejection referred to specifically. So I fed the page to ChatGPT to ask for advice. And I got what seems to me to be pretty good. And so I’m wondering if we might start to think about how a tool like AFCH might be improved so that instead of a generic template, a new editor gets actual advice. It would be better, obviously, if we had lovingly crafted human responses to every situation like this, but we all know that the volunteers who are dealing with a high volume of various situations can’t reasonably have time to do it. The templates are helpful - an AI-written note could be even more helpful.”
That being said, it still reeks of “CEO Speak.” And trying to find a place to shove AI in.
More NLP could absolutely be useful to Wikipedia, especially for flagging spam and malicious edits for human editors to review. This is an excellent task for dirt cheap, small and open models, where an error rate isn’t super important. Cost, volume, and reducing stress on precious human editors is. It’s a existential issue that needs work.
…Using an expensive, proprietary API to give error prone yet “pretty good” sounding suggestions to new editors is not.
Wasting dev time trying to make it work is not.
This is the problem. Not natural language processing itself, but the seemingly contagious compulsion among executives to find some place to shove it when the technical extent of their knowledge is occasionally typing something into ChatGPT.
It’s okay for them to not really understand it.
It’s not okay to push it differently than other technology because “AI” is somehow super special and trendy.
This is another reason why I hate bubbles. There is something potentially useful in here. It needs to be considered very carefully. However, it gets to a point where everyone’s kneejerk reaction is that it’s bad.
I can’t even say that people are wrong for feeling that way. The AI bubble has affected our economy and lives in a multitude of ways that go far beyond any reasonable use. I don’t blame anyone for saying “everything under this is bad, period”. The reasonable uses of it are so buried in shit that I don’t expect people to even bother trying to reach into that muck to clean it off.
Dotcom was, well, a useful thing. I guess valuations were nuts, but it looks like the hate was mostly in the enshittified aftermath that would come.
Crypto is a series of bubbles trying to prop up flavored pyramid schemes for a neat niche concept, but people largely figured that out after they popped. And it’s not as attention grabbing as AI.
Machine Learning is a long running, useful field, but ever since ChatGPT caught investors eyes, the cart has felt so far ahead of the horse. The hate started, and got polarized, waaay before the bubble popping.
…In other words, AI hate almost feels more political than bubble fueled. If that makes any sense. It is a bubble, but the extreme hate would still be there even if it wasn’t.
Crypto was an annoying bubble. If you were in the tech industry, you had a couple of years where people asked you if you could add blockchain to whatever your project was and then a few more years of hearing about NFTs. And GPUs shot up in price. Crypto people promised to revolutionize banking and then get rich quick schemes. It took time for the hype to die down, for people to realize that the tech wasn’t useful, and that the costs of running it weren’t worth it.
The AI bubble is different. The proponents are gleeful while they explain how AI will let you fire all your copywriters, your graphics designers, your programmers, your customer support, etc. Every company is trying to figure out how to shoehorn AI into their products. While AI is a useful tool, the bubble around it has hurt a lot of people.
That’s the bubble side. It also gets a lot of baggage because of the slop generated by it, the way it’s trained, the power usage, the way people just turn off their brains and regurgitate whatever it says, etc. It’s harder to avoid than crypto.
Yeah, you’re right. My thoughts were kinda uncollected.
Though I will argue some of the negatives (like inference power usage) are massively overstated, and even if they aren’t, are just the result of corporate enshittification more than the AI bubble itself.
I believe that the bad behavior of corporate interests is often one of the key contributors to these financial bubbles in every sector where they appear.
To say that some of the bad things about this particular financial bubble are because of a bunch of companies being irresponsible and/or unethical seems not to acknowledge that one is primarily caused by the other.
So… I actually proposed a use case for NLP and LLMs in 2017. I don’t actually know if it was used.
But the usecase was generating large sets of fake data that looked real enough for performance testing enterprise sized data transformations. That way we could skip a large portion of the risk associated with using actual customer data. We wouldn’t have to generate the data beforehand, we could validate logic with it, and we could just plop it in the replica non-prodiction environment.
At the time we didn’t have any LLMs. So it didn’t go anywhere. But it’s always funny when I see all this “LLMs can do x” because I always think about how my proposal was to use it… For fake data.
That being said, it still wreaks of “CEO Speak.” And trying to find a place to shove AI in.
I don’t see how this is “shoved in.” Wales identified a situation where Wikipedia’s existing non-AI process doesn’t work well and then realized that adding AI assistance could improve it.
Neither did Wales. Hence, the next part of the article:
For example, the response suggested the article cite a source that isn’t included in the draft article, and rely on Harvard Business School press releases for other citations, despite Wikipedia policies explicitly defining press releases as non-independent sources that cannot help prove notability, a basic requirement for Wikipedia articles.
Editors also found that the ChatGPT-generated response Wales shared “has no idea what the difference between” some of these basic Wikipedia policies, like notability (WP:N), verifiability (WP:V), and properly representing minority and more widely held views on subjects in an article (WP:WEIGHT).
“Something to take into consideration is how newcomers will interpret those answers. If they believe the LLM advice accurately reflects our policies, and it is wrong/inaccurate even 5% of the time, they will learn a skewed version of our policies and might reproduce the unhelpful advice on other pages,” one editor said.
It doesn’t mean the original process isn’t problematic, or can’t be helpfully augmented with some kind of LLM-generated supplement. But this is like a poster child of a troublesome AI implementation: where a general purpose LLM needs understanding of context it isn’t presented (but the reader assumes it has), where hallucinations have knock-on effects, and where even the founder/CEO of Wikipedia seemingly missed such errors.
Don’t mistake me for being blanket anti-AI, clearly it’s a tool Wikipedia can use. But the scope has to be narrow, and the problem specific.
Wales’s quote isn’t nearly as bad as the byline makes it out to be:
That being said, it still reeks of “CEO Speak.” And trying to find a place to shove AI in.
More NLP could absolutely be useful to Wikipedia, especially for flagging spam and malicious edits for human editors to review. This is an excellent task for dirt cheap, small and open models, where an error rate isn’t super important. Cost, volume, and reducing stress on precious human editors is. It’s a existential issue that needs work.
…Using an expensive, proprietary API to give error prone yet “pretty good” sounding suggestions to new editors is not.
Wasting dev time trying to make it work is not.
This is the problem. Not natural language processing itself, but the seemingly contagious compulsion among executives to find some place to shove it when the technical extent of their knowledge is occasionally typing something into ChatGPT.
It’s okay for them to not really understand it.
It’s not okay to push it differently than other technology because “AI” is somehow super special and trendy.
This is another reason why I hate bubbles. There is something potentially useful in here. It needs to be considered very carefully. However, it gets to a point where everyone’s kneejerk reaction is that it’s bad.
I can’t even say that people are wrong for feeling that way. The AI bubble has affected our economy and lives in a multitude of ways that go far beyond any reasonable use. I don’t blame anyone for saying “everything under this is bad, period”. The reasonable uses of it are so buried in shit that I don’t expect people to even bother trying to reach into that muck to clean it off.
This bubble’s hate is pretty front-loaded though.
Dotcom was, well, a useful thing. I guess valuations were nuts, but it looks like the hate was mostly in the enshittified aftermath that would come.
Crypto is a series of bubbles trying to prop up flavored pyramid schemes for a neat niche concept, but people largely figured that out after they popped. And it’s not as attention grabbing as AI.
Machine Learning is a long running, useful field, but ever since ChatGPT caught investors eyes, the cart has felt so far ahead of the horse. The hate started, and got polarized, waaay before the bubble popping.
…In other words, AI hate almost feels more political than bubble fueled. If that makes any sense. It is a bubble, but the extreme hate would still be there even if it wasn’t.
Crypto was an annoying bubble. If you were in the tech industry, you had a couple of years where people asked you if you could add blockchain to whatever your project was and then a few more years of hearing about NFTs. And GPUs shot up in price. Crypto people promised to revolutionize banking and then get rich quick schemes. It took time for the hype to die down, for people to realize that the tech wasn’t useful, and that the costs of running it weren’t worth it.
The AI bubble is different. The proponents are gleeful while they explain how AI will let you fire all your copywriters, your graphics designers, your programmers, your customer support, etc. Every company is trying to figure out how to shoehorn AI into their products. While AI is a useful tool, the bubble around it has hurt a lot of people.
That’s the bubble side. It also gets a lot of baggage because of the slop generated by it, the way it’s trained, the power usage, the way people just turn off their brains and regurgitate whatever it says, etc. It’s harder to avoid than crypto.
God I had coworkers that had never used a vr headset claiming the metaverse was going to be the next big thing. I wish common sense was common.
“The metaverse” changed it’s definition depending on who you talked to. Some definitions didn’t even include VR.
“AI” also changes it’s definition depending on who you talk to.
Vague definitions = hype
Yeah, you’re right. My thoughts were kinda uncollected.
Though I will argue some of the negatives (like inference power usage) are massively overstated, and even if they aren’t, are just the result of corporate enshittification more than the AI bubble itself.
Even the large scale training is apparently largely useless: https://old.reddit.com/r/LocalLLaMA/comments/1mw2lme/frontier_ai_labs_publicized_100kh100_training/
I believe that the bad behavior of corporate interests is often one of the key contributors to these financial bubbles in every sector where they appear.
To say that some of the bad things about this particular financial bubble are because of a bunch of companies being irresponsible and/or unethical seems not to acknowledge that one is primarily caused by the other.
So… I actually proposed a use case for NLP and LLMs in 2017. I don’t actually know if it was used.
But the usecase was generating large sets of fake data that looked real enough for performance testing enterprise sized data transformations. That way we could skip a large portion of the risk associated with using actual customer data. We wouldn’t have to generate the data beforehand, we could validate logic with it, and we could just plop it in the replica non-prodiction environment.
At the time we didn’t have any LLMs. So it didn’t go anywhere. But it’s always funny when I see all this “LLMs can do x” because I always think about how my proposal was to use it… For fake data.
I think you mean reeks, which means to stink, having a foul odor.
Those homophones have reeked havoc for too long!
Waves hands “You didn’t see anything.”
Thank you. Glad to know I am not the only one that got triggered, lol.
I don’t see how this is “shoved in.” Wales identified a situation where Wikipedia’s existing non-AI process doesn’t work well and then realized that adding AI assistance could improve it.
Neither did Wales. Hence, the next part of the article:
It doesn’t mean the original process isn’t problematic, or can’t be helpfully augmented with some kind of LLM-generated supplement. But this is like a poster child of a troublesome AI implementation: where a general purpose LLM needs understanding of context it isn’t presented (but the reader assumes it has), where hallucinations have knock-on effects, and where even the founder/CEO of Wikipedia seemingly missed such errors.
Don’t mistake me for being blanket anti-AI, clearly it’s a tool Wikipedia can use. But the scope has to be narrow, and the problem specific.