> Is that a positive thing?

Neither. Both.

Depends what you want to measure.

It's perfectly legit to call these models "thick" because they *need* to read such a vast quantity of text that a human would literally spend two thousand lifetimes to go through it even if that was all the human did with their days.

It also remains the case that, unlike us, they can go through all of that in a few months.

> with no novelty whatsoever, even though a 2 years old with a not even matured brain can learn a human language from orderS of magnitude less and lower quality input from a couple of people only.

You're either grossly underestimating AI or overestimating 2 year olds, possibly both.

I just about remember being a toddler, somewhere between then and 5 was around the age I had the idea that everyone got an invisible extra brain floating next to them for every year they lived. Took me an embarrassingly long time (teens, IIRC) to realise that the witch-duck-weight-comparison scene in Monty Python and the Holy Grail wasn't a documentary, thanks to the part of the film captioned "Famous Historian". One time my dad fell ill, and he was talking to mum about "the tissue being damaged" while I was present, so I gave him a handkerchief (AKA "a tissue"). And while I don't remember this directly, my mum's anecdotes include me saying "fetrol fump", waving a spoon in a jam pan and calling this act "spelling", and when discovered running around with my pockets inside-out explaining myself as trying to fly because I apparently thought that the lining of a pocket was called a "wing".

When it comes to human novelty, I also quite often find there's a lot of remixing going on that just isn't immediately apparent. As Steve Jobs apparently once said, “Good artists copy; great artists steal.”, except Jobs stole that quote from Picasso.

It's easy to categorise different levels with AI, but which one of these counts as "novelty", and how often do humans ever achieve each of these grades?

0. Memorisation of the training set. Think: bunch of pictures, pick best fit.

1. Linear interpolation between any pair of elements in the training set. Think: simple cross-fade between any two pictures, but no tracking or distorting of features during that fade.

2. Let the training set form a basis vector space, and interpolate freely within the constraints of the examples. Think: if these pictures are faces, it would make any hair colour between the most extreme limits shown, etc.

3. Extrapolate beyond the examples. Think: Even if no black or white hair was visible, so long as several shades of grey were, it could reach the ideas of black or white hair.

4. Invent a new vector. Think: even if it had been trained only on black-and-white images, it could still invent green hair.

> But any Nobel-price winner has read significantly less than a basic LLM, and we see no LLM doing any tiny scientific achievement, let alone that high impact ones.

We do see them doing *tiny* scientific achievements, with extra emphasis on "tiny". Just like with using them in software, even the best "only" act like fresh graduates.

When any AI gets to high-impact… the following (fictional) quote comes to mind: "as soon as we started thinking for you, it really became our civilization."