Sounds like you haven’t done much programming. It’s hard enough to understand the code one wrote oneself six months ago. (Or indeed, why the thing I wrote five minutes ago isn’t behaving as expected.) Just because I wrote it, doesn’t mean I memorized it. Understanding what someone else wrote is usually much harder, especially if they wrote it poorly, or in an unfamiliar language.
A machine learning system is even harder to understand than that. I’m sure there are some who understand in great detail what the human-written parts of the algorithm do. But to get anything useful out of a machine learning system, it needs to learn. You apply it to an enormous amount of data, and in the end, what it’s learned amounts to possibly gigabytes of inscrutable matrices of floating-point numbers. On paper, a gigabyte is about 4 million pages of text. That is far larger than the human-written source code that generated it, which could typically fit in a small book. How that works is anyone’s guess.
Reading this would be like trying to read someone’s mind by examining their brain under a microscope. Maybe it’s possible in principle, but don’t expect a human to be able to do it. We’d need better tools. That’s “interpretability research”.
There are approaches to machine learning that are indeed closer to cross breeding than designing cars (genetic algorithms), but the current paradigm in vogue is based on neural networks, kind of an artificial brain made of virtual neurons.
Sounds like you haven’t done much programming. It’s hard enough to understand the code one wrote oneself six months ago. (Or indeed, why the thing I wrote five minutes ago isn’t behaving as expected.) Just because I wrote it, doesn’t mean I memorized it. Understanding what someone else wrote is usually much harder, especially if they wrote it poorly, or in an unfamiliar language.
A machine learning system is even harder to understand than that. I’m sure there are some who understand in great detail what the human-written parts of the algorithm do. But to get anything useful out of a machine learning system, it needs to learn. You apply it to an enormous amount of data, and in the end, what it’s learned amounts to possibly gigabytes of inscrutable matrices of floating-point numbers. On paper, a gigabyte is about 4 million pages of text. That is far larger than the human-written source code that generated it, which could typically fit in a small book. How that works is anyone’s guess.
Reading this would be like trying to read someone’s mind by examining their brain under a microscope. Maybe it’s possible in principle, but don’t expect a human to be able to do it. We’d need better tools. That’s “interpretability research”.
There are approaches to machine learning that are indeed closer to cross breeding than designing cars (genetic algorithms), but the current paradigm in vogue is based on neural networks, kind of an artificial brain made of virtual neurons.