The world’s most important knowledge platform needs young editors to rescue it from chatbots – and its own tired practices
Established in 2001, Wikipedia is an “old man” by internet standards. But the role it plays in our collective knowledge of the world remains astonishing. Content from the free internet encyclopedia appears in everything from high-school term papers and pub trivia questions to search engine summaries and voice assistants. Tools like Google’s AI Overviews and ChatGPT rely heavily on Wikipedia, although they rarely credit the site in their responses.
And therein lies the problem: as Wikipedia’s visibility diminishes, reduced to mere training data for AI applications, it also loses prominence in the minds of readers and potential contributors. When someone notices a topic that is poorly described on Wikipedia, they might feel motivated to correct it. But this can-do spirit goes away when the error comes through an AI summary, where the source of the information isn’t clear.
No, but they can easily generate text that is statistically likely to look like a source.
LLMs are a probabilistic model of language, not an information source.
I don’t get it then, why are all these companies so gung-ho to replace something that was working with an AI that doesn’t?
It’s less accurate, it uses way more energy, it doesn’t show its work, it doesn’t cite its source, and it’ll make up shit that sounds right when it needs to. Why would anyone think AI is worth putting in any consumer product at this rate?
Because new
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Damn it I hate how simple and accurate this answer is.
As far as I can tell, it is hype because it is the hot new toy that they can sell.
LLMs are great for tasks like handling natural language data or classifying and identifying semantic meaning of text, but they are NOT good at math, logic, or as a store of facts/information. I think that they do actually deserve a lot of hype for these specific use cases, because they really accomplish these extraordinarily better than previous/traditional approaches.
The big problem is that they are being used for things that they are not good at, like when people ask a chatbot questions they they expect a factual answer to. They are also surprisingly bad at summarizing text (in my opinion and also this has been shown by some studies) despite companies like Google and Microsoft using them for things like summarizing and present search results. I think these companies are ultimately shooting themselves in the foot when they use LLMs for things that LLMs aren’t great for.
Think back to when blockchain was being shoved into everything possible, even places where blockchain makes no sense. And before blockchain, it was cloud