I dare say that in some ways, we understand LLMs better than humans, or at least the interpretability tools are now superior. Awkward place to be, but an interesting one.
Are you surprised we understand them better than brains?
That's a bit of an overstatement.
The entire field of ML is aimed at problems where deterministic code would work just fine, but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design) AND there's a sufficient corpus of data that allows plausible enough models to be trained. So we accept the occasionally questionable precision of ML models over the huge time and money costs of engineering these kinds of systems the traditional way. LLMs are no different.
What you are saying is fantasy nonsense.
> but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design)
So it doesn't work.
You would be sorely mistaken to think I'm utterly uninformed about LLM-research, even if I would never dare to claim to be a domain expert.
LLMs draw origins from, both n-gram language models (ca. 1990s) and neural networks and deep learning (ca. 2000). So we've only had really good ones maybe 6-8 years or so, but the roots of the study go back 30 years at least.
Psychiatry, psychology, and neurology on the other hand, are really only roughly 150 years old. Before that, there wasn't enough information about the human body to be able to study it, let alone the resources or biochemical knowledge necessary to be able to understand it or do much of anything with it.
So, sure, we've studied it longer. But only 5 times longer. And, I mean, we've studied language, geometry, and reasoning for literally thousands of years. Markov chains are like 120 years old, so older than computer science, and you need those to make an LLM.
And if you think we went down some dead-end directions with language models in the last 30 years, boy, have I got some bad news for you about how badly we botched psychiatry, psychology, and neurology!
Very, monsieur Laplace.
We have tons of low-hanging fruits across all fields of science and engineering to be picked, in form of different ways to apply and chain the models we have, different ways to interact with them, etc. - enough to fuel a good decade of continued progress in everything.
> What is (not) here to stay are the techbros who think every problem can be solved with LLMs.
LLMs are in all likelyhood here to stay, but the scumbags doing business around them right now are hopefully going away eventually.
Much as Diogenes mocked Platos definition of a man with a plucked chicken, LLM's revealed what "real" ai would require: contigous learning. That isnt to diminish the power of LLM's (the are useful) but that limitation is a fairly hard one to over come if true AGI is your goal.
From what I understand, a living neural network learns several orders of magnitude more efficiently than an artificial one.
I'm not sure where that difference comes from. But my brain probably isn't doing back propagation, it's probably doing something very different.
(eg different kinds of learning for long-term memory, short-term memory, languages, faces and reflexes.)
The intersection of what with physics?
Sir Roger Penrose, on quantum consciousness (and there is some regret on his part here) -- OR -- Jacob Barandes for a much more current thinking on this sort of intersectional exploratory thinking.
> The earliest reference to the brain occurs in the Edwin Smith Surgical Papyrus, written in the 17th century BC.
I was actually thinking of ancient greeks when writing my comment, but I suppose Egyptians have even older records than them.