This person is wrong and doesn't understand how brains produce and interact with language, but other people have already said that, so what is the straw that th

This person is wrong and doesn't understand how brains produce and interact with language, but other people have already said that, so what is the straw that this person is grasping at? There is a representational / symbolic divide in much of AI. Unfortunately some people think symbolic processes are somehow more "true" or "real" than representational processes. This leads them to say things like "A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey." As if there were such a thing in a jello-like mass of briefly sparking cells. But symbols are super useful, you're processing them now to understand my thoughts. They are so useful that you see them constantly in human innovations. Math, logic, art, literature ... everywhere! So what is a more useful way to understand the relationship between our gooey protoplasm and the beauty of say the pythagorean theorem? It's the same way to understand the relationship between analog and digital computation. The latter is built on the former. Your digital (symbolic, supposedly pure) world is built on differences in ranges of voltages in physical circuits. (Or ranges of magnetic polarization, or ranges of light frequencies). We tame a continuously variable world by defining boundaries on continuous ranges and then pretend those are pure symbolic values. This is the problem the author is wrestling with. They haven't differentiated the computational substrate from the computation. Can we build systems that "understand" using probabilistic function approximation? Sure! That's how we work. But the program running on that messy substrate isn't there yet and that's what's got the author in a tizzy.