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.
AI Chat bots could easily refer to their source. But the companies that own the chat bots don’t wanna do that.
It’s actually not easy to ensure that an LLM will cite a correct source, in the same way it’s not easy to ensure that it will provide accurate information. It’s based on token probability, not deterministic lookups of “this data came from this source.” It could entirely make something up, then write “Source:” and then probabilistically write “Wikipedia” because those tokens commonly follow those for “Source.”
If you have an AI bot that looks up information in real time, then that would be easy. But for a trained LLM, the training process is highly destructive. Original information is not preserved except in relationships based on probability.
The more I learn about AI, the less I like it.
It’s a fun toy. It’s not a research aid, it’s not a productivity tool, and it’s not particularly useful in the workplace.
It’s honestly very similar to the VR craze of a few years back. Silicon Valley invented a fun toy and then tried to convince everyone that it would transform the workplace. Meetings in VR and simulated workstations and all that. Ultimately everyone figured out that VR is completely useless in the workplace and Silicon Valley was just trying to find ways to sell their fun toy. Now we’re going through the same learnings with AI.
I love VR. I have so many hours in some of the slower paced fps titles that it’s almost matched my video game time total for non-vr games on steam.
The one thing I learned for sure is that I don’t want anyone else telling me when I have to put on the headset and when I’m allowed to take it off.
Never will wear a vr headset in the workspace.
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.
Agreed. ChatGPT doesn’t like to cite sources. Microsoft CoPilot and Google Gemini do link to some sources, though not as accurate or thorough like Wikipedia.
What I don’t understand is how Microsoft has/has Watson which was able to answer questions well enough to go on Jeopardy and dominate. And now, more than a decade later these LLMs absolutely suck at it.
It makes me wonder if Watson was nothing more than a Mechanical Turk because what is out there now seems like a huge step backwards.
They just work in entirely different ways. An car and a horse are both able to serve as transportation, but they aren’t anything alike in other ways. LLMs compared to previous sorts of bots are similar.
The main difference is that an LLM isn’t fetching whole answers from some database somewhere. It’s generating them fresh. You have to hope it generates the right stuff, which it does a certain percentage of the time.