> That's the popular story behind neural networks. Like any good popular explanation of a STEM concept, it's as useful as it is inaccurate.
To be fair, our own understanding of "how it works" in practice is not necessarily set in stone. Approximating our belief in how it works is the best case scenario, and expecting it to perfectly mirror human reasoning is unrealistic and frankly not all that appealing from a problem-solving stance.
Human reasoning has a lot of built-in buffers and fault tolerance, and it is, itself, a fairly narrow AI.
Sorry, I wasn't meaning to quote mine. I honestly didn't think that part was relevant to the point I was making.
Neural networks have taken some inspiration from biology, but that doesn't account for the vast bulk of the work. Biology analogies play a much bigger part in popular explanations than they did in the development of the technology.
(edit) Here, for example, take a look at the original paper on perceptrons. Whole lotta math. This having been released during one of the AI flaps, there was also a fair bit of psychology talk. But not much indication that he was just trying to copy a brain and otherwise didn't understand how it works.
Similar happens with deep learning. They are grounded in theory, most notably a mathematical proof that deep MLPs are, in principle, capable of learning any mathematical function. You just won't see much of that stuff if you aren't actually reading the papers, because most people aren't that interested in vector calculus. Much easier to explain things by analogy.
That's the popular story behind neural networks. Like any good popular explanation of a STEM concept, it's as useful as it is inaccurate.