> Related to this topic, we still don't have a good, strong idea of how human thought works. Neural Networks/Perceptrons were built out of analogies of how huma

> Related to this topic, we still don't have a good, strong idea of how human thought works. Neural Networks/Perceptrons were built out of analogies of how human-like neurons seem to work (primarily in isolation studies in a petri dish or similar experiments), but it is very easy to forget that the model/analogy is not the reality when you work with those models a lot. I'm pretty sure that you were making a tangent rather than a rebuttal, but I did want to clarify that I'm not saying that these large models replicate the structural thinking of humans. Instead I'm saying that, given a ML model that is "sufficiently human like" in the mistakes it makes, using techniques that prevent humans from making those mistakes (e.g. showing work, having multiple agents calculate in parallel) could demonstrate an ability to solve problems that the ML model itself appears incapable of solving.