Well, anyone who used an LLM long enough knows that there’s always a randomness to their answers and sometimes they can output a totally weird and nonsense answer too. Just start a new chat and ask it again, it’ll give a new answer.
This is actually the best way to know whether it’s hallucinating something, if it answers the same thing two or more times in different chats, it’s likely not making it up.
I wonder if they say people should be careful with Chrome 😂
Still what makes you think that an alternative isn’t going to get flooded with AI too? That ship has sank already.
And the same applies to smartphones since a while ago.
Meanwhile there was a video interviewing illegal Brazilians in the US who supported Trump. Most of those were in favor of expelling immigrants, if they broke laws (ignoring they are illegal themselves). They saw themselves as “good guys”, so it wouldn’t apply to them. And some of them complained that nowadays there was more immigrants and hence more competition for jobs, so they wanted it to be more difficult to get in. Typical “now that I’m here, kick the stairs”. I just assume most of his supporters are selfish people who wouldn’t miss the chance to throw someone else at a bus to have some personal gain—as long as they don’t stain their own hands with blood.
I would look for the video, but it was in Portuguese anyway.
Having more people does help, but only to a certain extent. At some point, it just becomes difficult to moderate and having a higher number of casual users that don’t give a shit about the rules.
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And I’d have thought the potential customers segment are exactly the Mastodon users.
This seems to me like just a semantic difference though. People will say the LLM is “making shit up” when they’re outputting something that isn’t correct, and that happens (according to my knowledge) usually because the information you’re asking wasn’t represented enough in the training data to guide the answer always to that information.
In any case, there is an expectation from users that LLMs can somehow be deterministic when they’re not at all. They’re a deep learning model that’s so complicated that’s impossible to predict what effect a small change in the input will have on the output. So it could give an expected answer for a certain question and give a very unexpected one just by adding or changing some word on the input, even if that appears irrelevant.