[gnramires]

> Language modelling and "AI", in its current form, uncovers only statistical correlations and barely scratches the surface of what "understanding" is

This is recurrent and somewhat unfair. Current architectures have long known to be universal, capable of reproducing any computational structure (of finite depth for NNs, and Turing complete for RNNs); they have significant structural flexibility and in principle their learning can converge to "ideal processing structures" (which supposedly our brains also approach) given good enough training conditions (data, regimen, etc.). The network scales, timescales and dataset scale to achieve what comparable human function are debatable and unknown, but I believe it's very safe to judge them on function (this particular example is indeed quite impressive), because given their performance it's likely a powerful structure has emerged under the hood -- you can think of it emerging similarly to intelligence emerges from evolution (and of course human learning). Internal recurrent evaluations of logic and representations of language can all emerge.

I wouldn't describe this process as simply statistical inference, since it has complex computational priors and structure involved. It's really algorithmic learning.

Of course, you can bake in structure to accelerate this process, and we've been discovering very useful structures (such as CNNs, LSTMs, Transformer arch) which bias the models in the desired direction but still have internal flexibility.