What must be avoided is Wikipedia becoming a repository of AI-generated slop (and possibly feeding the next generation models, becoming a recursive loop of even more slop).
But this way? It makes sense. This won't create content for articles, it's just assistance for editors.
Honestly, I think anyone who's a humanist should encourage that. Given the way AI is being hyped by the business elite, making it ineffective is an important goal and worth the cost of a compromised Wikipedia.
If businesses destroy something of value to you, that's not a win.
E.g. a city in France’s BRT system:
Some random examples where the Wikipedia page in the native language is much more detailed:
https://en.wikipedia.org/wiki/Trams_in_Florence vs https://it.wikipedia.org/wiki/Rete_tranviaria_di_Firenze (and an entire second article for the historic system!)
https://en.wikipedia.org/wiki/La_D%C3%A9fense vs https://fr.wikipedia.org/wiki/La_Défense
https://en.wikipedia.org/wiki/Stasi vs https://de.wikipedia.org/wiki/Ministerium_f%C3%BCr_Staatssic...
https://en.wikipedia.org/wiki/Muslim_conquest_of_the_Iberian... vs https://es.wikipedia.org/wiki/Conquista_omeya_de_Hispania ... but https://ar.wikipedia.org/wiki/%D8%A7%D9%84%D9%81%D8%AA%D8%AD... is better than both of them
Does anyone else have good examples? Or are there counter-examples, where enwiki has a better article than the native language wiki?
It isn't a replacement for an expert writing the real page in the language though. But there are a lot of languages with very little content. German has less than half as many articles as English (Cebuano is number 2 by number of articles - but that seems fishy. German is number 3). At the bottom Cree has only 13 articles. There are also a small number of inactive wikipedias, some of which may want a translation option as better than nothing.
Having recently spent some time doing translations with the current models, my experience is that their output quality can have a rather high variance. That, along with a tendency toward hallucination, often makes them a "fuzzy guess" at best. When I'm the user asking for that translation, I can factor that fuzziness into my understanding of the output. If its already translated for me, I'm depending on someone else to have done the work of making sure it is faithful to the original.
They would never do that.
Industry has a good track record of this.
so, a chatbot on top of some tooling probably
- Giving Wikipedia’s editors time back by improving the discoverability of information on Wikipedia to leave more time for human deliberation, judgment, and consensus building;
extremely vague, but probably the "train a specialized ai on our own corpus to answer questions about it" style bot helper? these make stuff up left and right too
- Helping editors share local perspectives or context by automating the translation and adaptation of common topics;
automated translations would be a big step in the wrong direction
- Scaling the onboarding of new Wikipedia volunteers with guided mentorship.
you can't do "mentorship" with ai in any real way. all in all seems a box checking exercise for their ML officer.
Depending on what's meant by "mentorship": this is the bit I'm most keen to see. Much of the criticism lobbed at Wikipedia nowadays seems to come from how much of a pain it is to contribute (which perhaps leads to second-order criticisms about censorship, even if that's not the intention).
It's often been the case that the person most-worthy to speak on a subject is not the most worthy steward of a Wikipedia page on it. Any attempt to make those two people one-and-the-same seems welcome.
If this "mentorship" takes the form of UI hints, I think they'd go a long way. Having volunteers take an AI course, on the other hand, might be useful but might also be a complete waste of time.
I suspect what you're getting at is that "mentorship" is really code for using AI to step in when people are making the wrong kind of changes to a Wikipedia page. (IE, introducing bias, promoting products, edit wars, ect.)
I'm curious to see how this plays out.
I wonder too if it could be used to help the edit reviewing process, but I can imagine it runs a risk of becoming an accountability sink[^1] if reviewers can merely defer their judgement to what the bot says is OK to purge. It might have a chilling effect on edits if everyone, including procedure hawks, can rely on it in that way. I'm not enough of a contributor there to know.
Just look for the keyword "streamlining" in any sentence and they will.
Why would they do this? All of wikipedia is publicly available for any use. They literally do not have a competitive advantage (and don't seem interested in it, either).
This raises the thorny issue of what constitutes an 'authoritative reference' and given that many if not most such references are hidden behind various paywalls, and that the editors and volunteers are anonymoous actors with unknown special interests and biases, the conclusion is that Wikipedia is not a reliable source of information, and a wikipedia citation is essentially useless and should never be allowed in any reputable publication.
https://en.wikipedia.org/wiki/Wikipedia:Citing_Wikipedia
> Normal academic usage of Wikipedia is for getting the general facts of a problem and to gather keywords, references and bibliographical pointers, but not as a source in itself.
There is always an issue about claim not in source. Often this is a matter of perception and has to be decided by an argument about what the source means, or what a conventional reading of the source is.
could be pretty helpful. I edit a bit and it's confusing in a number of ways, some I still haven't got the hang of. There's very little "thank you for your contribution but it needs a better source - why not this?" and usually just your work reverted without thanks or much explanation.
And then what to do about "original research" that should've been moved to a different platform or better (also with community review) instead of being deleted?
Wikipedia:No_original_research: https://en.wikipedia.org/wiki/Wikipedia:No_original_research #Using_sources
MarkusQ•9mo ago