Hofstadter (GEB) and later Jeff Hawkins (Numenta) have long postulated this theory - and indeed it seems to be compelling - but the prediction-by-analogy paradigm has yet to deliver in a convincing way. Would love to see some progress on this front.
More amusing is a comparison with a 5yo human child: https://twitter.com/lacker/status/1294341796831477761
A pity that she didn't mention any of that, it's pretty interesting.
> Q: If axbxcx goes to abc, what does xpxqxr go to?
> A: s
I've gotta be missing something here?
It should be noted that predictive coding predates Hawkins' work, perhaps best exemplified by Rao and Ballard's hierarchical predictive coding model from 1999. Various researchers working from similar assumptions put their thoughts together in Bayesian Brain in 2006. Since then, predictive processing has grown to be a very popular framework in neuroscience and cognitive science in general. Friston's free energy principle and philosopher Andy Clark's popularization of it have a lot to do with that.
There's been a ton of progress so far, so there's plenty of academic material for you to dive into.
Anyway, Hofstadter's (and his student Cabrera's) aim actually hasn't been to deliver a convincing industrial scale system but produce models that illuminate our understanding of thought. I'm not actually sure if that is the right way of doing it but it's worth noting they're not trying to "deliver" on the terms akin to today's neural networks.