Sorry for the late reply - work is consuming everything :)

I suspect that we are (like LLMs) mostly “sophisticated pattern recognition systems trained on vast amounts of data.”

Considering the claim that LLMs have “no true understanding”, I think there isn’t a definition of “true understanding” that would cleanly separate humans and LLMs. It seems clear that LLMs are able to extract the information contained within language, and use that information to answer questions and inform decisions (with adequately tooled agents). I think that acquiring and using information is what’s relevant, and that’s solved.

Engaging with the real world is mostly a matter of tooling. Real-time learning and more comprehensive multi-modal architectures are just iterations on current systems.

I think it’s quite relevant that the Turing Test has essentially been passed by machines. It’s our instinct to gatekeep intellect, moving the goalposts as they’re passed in order to affirm our relevance and worth, but LLMs have our intellectual essence, and will continue to improve rapidly while we stagnate.

There is still progress to be made before we’re obsolete, but I think it will be just a few years, and then it’s just a question of cost efficiency.

Anyways, we’ll see! Thanks for the thoughtful reply