this post was submitted on 21 Feb 2024
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I mean, surely the solution to that would be to use curated/vetted training data? Or at the very least, data from before LLMs became commonplace?
Yes but that only works if we can differentiate that data on a pretty big scale. The only way I can see it working at scale is by having meta data to declare if something is AI generated or not. But then we're relying on self reporting so a lot of people have to get on board with it and bad actors can poison the data anyway. Another way could be to hire humans to chatter about specific things you want to train it on which could guarantee better data but be quite expensive. Only training on data from before LLMs will turn it into an old people pretty quickly and it will be noticable when it doesn't know pop culture or modern slang.
Pretty sure this is why they keep training it on books, movies, etc. - it's already intended to make sense, so it doesn't need curated.
You mean like work? Can't I just have some AI do all that stuff? What could go wrong?
The funny thing is, children are similar. They just learn whatever you put in front of them. We have whole systems for educating children for decades of their lives.
With AI we literally just plopped them in front of the Internet, with no guidelines on what to learn. AI researchers say "it's a black box! We don't know why it's doing this!" You fed it everything you could and gave it few rules on what to do. You are the reason why it's nuts.
Humans come hardwired to be a certain way, do certain things. Maybe they need to start AI off like that, some basic programs that guide learning. "Learn everything" isn't working.
That's a good point. For real brains, size and intelligence are not linked. An elephant brain has 3 times the amount of neurons as a human brain, but a human brain is more intelligent. There is more to intelligence than just the amount of neutrons, real or virtual, so making larger and larger AI models may not be the right direction.
True. Maybe they just need more error correction. Like spend more energy questioning whether what you say is true. Right now LLMs seems to just vomit out whatever they thought up, with no consideration of whether it makes sense.
They're like an annoying friend who just can't shut up.
They aren’t thinking though. They’re making connection with the trained data that they’ve processed.
This is really clear when they are asked to write code worth to vague a prompt.
Maybe feeding them through primary school curriculum (including essays and tests) would be helpful, but I don’t think the language models really sort knowledge yet.