Is human knowledge really derived in a similar manner though? That reduction of biological processes to compression algorithms seems like a huge oversimplification.
It's almost like saying that all of of human knowledge derives from Einstein's Field Equations, the Standard Model Lagrangian, and the Second Law of Thermodynamics (what else could human knowledge really derive from?) and all we have to do to create artificial intelligence is just to model these forces to a high enough fidelity and with enough computation.
The models we use nowadays operate on discrete tokens. To overly reduce the process of human learning, we take a constant stream of realtime information. It never ends and it’s never discrete. Nor do we learn in an isolated “learn” stage in which we’re not interacting with our environment.
If you try taking reality and breaking into discrete (ordered in the case of LLMs) parts, you lose information.
Having said that, I think this picture is missing something: when we teach each new generation what we know, part of that process involves recapitulating the steps by which we got to where we are. It is a highly selective (compressed?) history, however, focusing on the things that made a difference and putting aside most of the false starts, dead ends and mistaken notions (except when the topic is history, of course, and often even then.)
I do not know if this view has any significance for AI.
Not true. No amount of such iteration gets you from buffalo cave paintings to particle accelerators.
Humans generate knowledge by acting in the world, not by dwelling on our thoughts. The empiricists won a very long time ago.
When I pursued creative writing in my teens and early 20s, it became clear to me that originality is extremely difficult. I am not entirely sure I have ever had an original thought--every idea I've put to paper thinking it was original, I later realized was a recombination of ideas I had come across somewhere else. The only exceptions I've found were places where I had a fairly unusual experience which I was able to interpret and relate, i.e. a unique interaction with the world.
Perhaps more importantly, LLMs do not contain any mechanism which even attempts to perform pure abstract thought, so even if we accept the questionable assumption that humans can generate ideas ex nihilo, that doesn't mean that LLMs can.
Think about this: The sum total of the human-generated knowledge was derived in a similar manner, with each generation learning from the one before and expanding the pool of knowledge incrementally.
Simply adding a bit of noise and then selecting good outputs after each iteration based on a high-level heuristic such as "utility" and "self consistency" may be sufficient to reproduce the growth of human knowledge in a purely mathematical AI system.
Something that hasn't been tried yet because it's too expensive (for now) is to let a bunch of different AI models act as agents updating a central wikipedia-style database.
These could start off with "simply" reading every single text book and primary source on Earth, updating and correcting the Wikipedia in every language. Then cross-translate from every source in some language to every other language.
Then use the collected facts to find errors in the primary sources, then re-check the Wikipedia based on this.
Train a new generation of AIs on the updated content and mutate them slightly to obtain some variations.
Iterate again.
Etc...
This could go on for quite a while before it would run out of steam. Longer than anybody has budget for, at least for now!