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Statistical optimization is a known process; one where we understand every step, and can therefore instruct machines on how to perform it. Cognitive reasoning is still today not understood (in the Von Neumann sense) by anyone.

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Only the training process of statistical optimization can possibly be described as well understood, and not very much.

The steps are understood well enough to instruct machines to do it, even if the output is to complex for us to comprehend it completely. Not so with thought, intelligence, consciousness, etc.

again, no they aren't. In ML, nobody instructs machines to do anything other than train and find those steps themselves. We have no clue what steps GPT takes when it solves problems.

For a straightforward example, when GPT-4 adds, what algorithm does it perform ? I have no clue, you have no clue and neither does anyone at open ai.

What we do is instruct machines to train. What they get out of training, we have very little idea.

That's my point. How can you compare the two if one is not even well-defined, let alone understood?

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