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What’s Really Going On in Machine Learning? Some Minimal Models—Stephen Wolfram Writings

writings.stephenwolfram.com · 14,246 words · saved by 1 readers

It’s surprising how little is known about the foundations of machine learning. Yes, from an engineering point of view, an immense amount has been figured out about how to build neural nets that do all kinds of impressive and sometimes almost magical things. But at a fundamental level we still don’t really know why neural nets “work”—and we don’t have any kind of “scientific big picture” of what’s going on inside them. The basic structure of neural networks can be pretty simple. But by the time they’re trained up with all their weights, etc. it’s been hard to tell what’s going on—or even to get any good visualization of it. And indeed it’s far from clear even what aspects of the whole setup are actually essential, and what are just “details” that have perhaps been “grandfathered” all the way from when computational neural nets were first invented in the 1940s. Well, what I’m going to try to do here is to get “underneath” this—and to “strip things down” as much as possible. I’m going to

Contents Top The Mystery of Machine Learning Traditional Neural Nets Simplifying the Topology: Mesh Neural Nets Making Everything Discrete: A Biological Evolution Analog Machine Learning in Discrete Rule Arrays Multiway Mutation Graphs Optimizing the Learning Process What Can Be Learned? Other Kinds of Models and Setups So in the End, What’s Really Going On in Machine Learning? Historical & Personal Notes Thanks What’s Really Going On in Machine Learning? Some Minimal Models What’s Really Going On in Machine Learning? Some Minimal Models >> (VIDEO) What’s Really Going On in Machine

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