https://www.tandfonline.com/doi/epdf/10.1080/25785648.2023.2...
Quote from the conclusion:
> This essay has shown that in the last decade, a handful of editors have been steering Wikipedia’s narrative on Holocaust history away from sound, evidence-driven research, toward a skewed version of events touted by right-wing Polish groups. Wikipedia’s articles on Jewish topics, especially on Polish–Jewish history before, during, and after World War II, contain and bolster harmful stereotypes and fallacies. Our study provides numerous examples, but many more exist. We have shown how the distortionist editors add false content and use unreliable sources or misrepresent legitimate ones.
For a more recent paper, "Disinformation as a tool for digital political activism: Croatian Wikipedia and the case for critical information literacy" by Car et al. says that:
> The Hr.WP [Croatian Wikipedia] case exemplifies disinformation not only as content manipulation, but also as process manipulation weaponising neutrality and verifiability policies to suppress dissent and enforce a single ideological position.
Vs.
What Elon is doing...
Then we're not even comparing fruits to fruits.
As soon as there is a plausible agenda for selecting a narrative the way Wikipedia works we should be sceptical.
For recent examples, everything to do with Biden and family, and Gamergate. These pages are still full of discussion; and what's written is more ideological than factual. You can follow these pages to see how an in-group selects a narrative.
And these topics are not nearly as controversial as race, feminism, or transgender topics.
I really like Wikipedia, though, and I think over time we will get around to fixing it up.
So you can understand someone not liking something, but you cannot understand that person liking the idea of an alternative? What is the idea for you if not just an alternative to the established service with the undesired part changed?
Which one is the "undesirable part changed" here? Wikipedia is written by humans, it has a not-for-profit governance model, it encompasses a large, international community of authors/editors that attempt to operate democratically, it has an investment/commitment in being an openly available and public source of information. Grokipedia, on the other hand, is AI-generated, and operated by a for-profit AI company. Even if "grokipedia" managed somehow to get traction and "overthrow" wikipedia, there is no reason on earth why a company would operate it for free and not try to make profit out of it, or use it for their ends in ways much more direct than what may or may not be happening to wikipedia. Having a billionaire basically control something that may be considered "ground truth" of information seems a bad idea, and having AI generate that an even worse one.
I can understand somebody not liking something in how wikipedia is governed or operating, after all whatever has to do with getting humans work together in such a scale is bound to be challenging. I can understand somebody ideologically disagreeing with some of the stances that such a project has to take eventually (even if one tries to be neutral as much as possible, it is inevitable to avoid some clash somewhere about where this neutrality exactly lies). But grokipedia much more than "wikipedia but different ideologically".
edit: just to be clear, I see a critique of the "idea of grokipedia" as eg the critique of it being a billionaire controlled, AI generated project to substitute wikipedia; a critique of the implementation would be finding flaws to actual articles in grokipedia (overall). I think the idea of it is already flawed enough.
Really? Have you used AI to write documentation for software? Or used AI to generate deep research reports by scouring the internet?
Because, while both can have some issues (but so do humans), AI already does extremely well at both those tasks (multiple models do, look at the various labs' Deep Research products, or look at NotebookLM).
Grokipedia is roughly the same concept of "take these 10,000 topics, and for each topic make a deep research report, verify stuff, etc, and make minimal changes to the existing deep research report on it. preserve citations"
So it's not like it's automatically some anti-woke can't-be-trusted thing. In fact, if you trust the idea of an AI doing deep research reports, this is a generalizable and automated form of that.
We can judge an idea by its merits, politics aside. I think it's a fascinating idea in general (like the idea of writing software documentation or doing deep research reports), whether it needs tweaks to remove political bias aside.
Hi. I have edited AI-generated first drafts of documentation -- in the last few months, so we are not talking about old and moldy models -- and describing the performance as "extremely well" is exceedingly generous. Large language models write documentation the same way they do all tasks, i.e., through statistical computation of the most likely output. So, in no particular order:
- AI-authored documentation is not aware of your house style guide. (No, giving it your style guide will not help.)
- AI-authored documentation will not match your house voice. (No, saying "please write this in the voice of the other documentation in this repo" will not help.)
- The generated documentation will tend to be extremely generic and repetitive, often effectively duplicating other work in your documentation repo.
- Internal links to other pages will often be incorrect.
- Summaries will often be superfluous.
- It will love "here is a common problem and here is how to fix it" sections, whether or not that's appropriate for the kind of document it's writing. (It won't distinguish reliably between tutorial documentation, reference documentation, and cookbook articles.)
- The common problems it tells you how to fix are sometimes imagined and frequently not actually problems worth documenting.
- It's subject to unnecessary digression, e.g., while writing a high-level overview of how to accomplish a task, it will mention that using version control is a good idea, then detour for a hundred lines giving you a quick introduction to Git.
As for using AI "to generate deep research reports by scouring the internet", that sounds like an incredibly fraught idea. LLMs are not doing searches, they are doing statistical computation of likely results. In practice the results of that computation and a web search frequently line up, but "frequently" is not good enough for "deep research": the fewer points of reference for a complex query there are in an LLM's training corpus, the more likely it is to generate a bullshit answer delivered with a veneer of absolute confidence. Perhaps you can make the case that that's still a good place to start, but it is absolutely not something to rely on.
edit: I am not very excited by AI-generated documentations either. I think that LLMs are very useful tools, but I see a potential problem when the sources of information that their usefulness is largely based on is also LLM-generated. I am afraid that this will inevitably result in drop in quality that will also affect the LLMs themselves downstream. I think we underestimate the importance that intentionality in human-written text plays in being in the training sets/context windows of LLMs for them to give relevant/useful output.
