The technological struggles are in some ways beside the point. The financial bet on artificial general intelligence is so big that failure could cause a depression.
Calling the errors “hallucinations” is kinda misleading because it implies there’s regular real knowledge but false stuff gets mixed in. That’s not how LLMs work.
LLMs are purely about word associations to other words. It’s just massive enough that it can add a lot of context to those associations and seem conversational about almost any topic, but it has no depth to any of it. Where it seems like it does is just because the contexts of its training got very specific, which is bound to happen when it’s trained on every online conversation its owners (or rather people hired by people hired by its owners) could get their hands on.
All it does is, given the set of tokens provided and already predicted, plus a bit of randomness, what is the most likely token to come next, then repeat until it predicts an “end” token.
Earlier on when using LLMs, I’d ask it about how it did things or why it would fail at certain things. ChatGPT would answer, but only because it was trained on text that explained what it could and couldn’t do. Its capabilities don’t actually include any self-reflection or self-understanding, or any understanding at all. The text it was trained on doesn’t even have to reflect how it really works.
Well, you described pretty well what llms were trained to do. But from there you can’t derive how they are doing it. Maybe they don’t have real knowledge, or maybe they do. Right now literally no one can definitively claim one way or the other, not even top of the field ML researchers. (They may have opinions though)
I think it’s perfectly justified to hate AI, but it’s better to have a less biased view of what it is.
Calling the errors “hallucinations” is kinda misleading because it implies there’s regular real knowledge but false stuff gets mixed in. That’s not how LLMs work.
LLMs are purely about word associations to other words. It’s just massive enough that it can add a lot of context to those associations and seem conversational about almost any topic, but it has no depth to any of it. Where it seems like it does is just because the contexts of its training got very specific, which is bound to happen when it’s trained on every online conversation its owners (or rather people hired by people hired by its owners) could get their hands on.
All it does is, given the set of tokens provided and already predicted, plus a bit of randomness, what is the most likely token to come next, then repeat until it predicts an “end” token.
Earlier on when using LLMs, I’d ask it about how it did things or why it would fail at certain things. ChatGPT would answer, but only because it was trained on text that explained what it could and couldn’t do. Its capabilities don’t actually include any self-reflection or self-understanding, or any understanding at all. The text it was trained on doesn’t even have to reflect how it really works.
Well, you described pretty well what llms were trained to do. But from there you can’t derive how they are doing it. Maybe they don’t have real knowledge, or maybe they do. Right now literally no one can definitively claim one way or the other, not even top of the field ML researchers. (They may have opinions though)
I think it’s perfectly justified to hate AI, but it’s better to have a less biased view of what it is.
Yeah you’re right, even in my cynicism I was still too hopeful for it LOL