They are talking at a technical level only on one side of the comparison. It makes the entire discussion pointless. If you're going to compare the understanding of a neural network and the understanding of a human brain, you have to go into depth on both sides.

Mysticism? Lmao. Where? Do you know what the word means?

Of course the "understanding" of an LLM is limited. Because the entire technology is new, and it's far from being anywhere close to being able to understand to the level of a human.

But I disagree with your understanding of how an LLM works. At its lower level, it's a bunch on connected artifical neurons, not that different from a human brain. Now please don't read this as me saying it's as good as a human brain. It's definitely not, but its inner workings are not so far. As a matter of fact, there is active effort to make artificial neurons behave as close as possible to a human neuron.

If it was just statistics, it wouldn't be so difficult to look at the trained model and identify what does what. But just like the human brain, it is incredidbly difficult to understand that. We just have a general idea.

So it does understand, to a limited extent. Just like a human, it won't understand what it hasn't been exposed to. And unlike a human, it is exposed to a very limited set of data.

You're putting the difference between a human's "understanding" and an LLM's "understanding" in the meaning of the word "understanding", which is just a shortcut to say that they can't be compared. The actual difference is in the scope of understanding.

A lot of the efforts in the AI fields gravitate around imitating a human brain. Which makes sense, as it is the only thing we know that is capable of doing what we want an AI to do. LLMs are no different, but their scope is limited.

That doesn't answer the question you quoted.