this post was submitted on 22 Dec 2023
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This is an issue that has plagued the machine learning field since long before this latest generative AI craze. Decision trees you can understand, SVMs and Naive Bayes too, but the moment you get into automatic feature extraction and RBF kernels and stuff like that, it becomes difficult to understand how the verdicts issued by the model relate to the real world. Having said that, I'm pretty sure GPTs are even more inscrutable and made the problem worse.
This may be a dumb question, but why can't you set the debugger on and step thru the program to see why it branches the way it does?
I do exactly this kind of thing for my day job. In short: reading a syntactic description of an algorithm written in assembly language is not the equivalent of understanding what you've just read, which in turn is not the equivalent of having a concise and comprehensible logical representation of what you've just read, which in turn is not the equivalent of understanding the principles according to which the logical system thus described will behave when given various kinds of input.