I also started wondering about the utility of spending most of the network memorizing infinite trivia (even excluding most of the content above, which is trash), when LLMs don't really excel at that anyway, and they need to Google it anyway to give you a source. (Aside: I've heard soke people have good luck with "hallucinate then verify" with RAG / Googling...)
i.e. what if we put those neurons to better use? Then I found the Phi-1 paper, which did exactly that. Instead of training the model on slop, they trained it on textbooks! And instead of starting with PhD level stuff, they started with kid level stuff and gradually increased the difficulty.
What will we think of next...
The reason LLMs work isn't because they learn the whole internet, it's because they try to learn it but then fail to, in a useful way.
If anything current models are overly optimized away from this; I get the feeling they mostly want to tell you things from Wikipedia. You don't get a lot of answers that look like they came from a book.
I would rather put it that humans can additionally specialize much more, but we usually have a pretty okay generic understanding/model of a thing we consider as 'known'. I would even wager that being generic enough (ergo, has been sufficiently abstracted) is possibly the most important "feature" human's have? (In the context of learning)
What is also a benefit for humans, I think, is that people are typically much more selective. LLMs train to predict anything on the internet, so for example for finance that includes clickbait articles which have a lifetime of about 2 hours. Experts would probably reject any information in these articles and instead try to focus on high quality sources only.
Similarly, a math researcher will probably have read a completely set of sources throughout the life than, say, a lawyer.
I’m not sure it’s a fundamental difference, but current models do seem to not specialize from the start unlike humans. And that might be in the way of learning the best representations. I know from ice hockey for example, that you can see within 3 seconds whether someone played ice hockey from young age or not. Same with language. People can usually hear an accent within seconds. Relatedly, I've used OpenAI's text to speech a while back and the Dutch voice had an American accent. What this means is that even if you ask LLMs about Buffett's strategy, maybe they have a "clickbait accent" too. So with the current approach to training, the models might never reach absolute expert performance.