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>Also would a Perceptron with the original architecture do what an LLM does just given scale? Yes. The answer is yes. This is exactly what the universal approx

>Also would a Perceptron with the original architecture do what an LLM does just given scale? Yes. The answer is yes. This is exactly what the universal approximation theorem [1] says, which was proven decades ago. It is also just another line of reasoning that compute matters more than architecture, but fewer people outside of ML research know about it. And it's also less practical, since the formal theorem has few empirics to go by for really large models. But mathematically, a scaled perceptron is 100% able to replicate any LLM. [1] https://en.wikipedia.org/wiki/Universal_approximation_theorem https://en.wikipedia.org/wiki/Universal_approximation_theore...

wizzwizz4 3y ago
Given an LLM, you can construct a perceptron-based model that approximates it, but that doesn't mean there's any obvious way of training a perceptron model that would produce something like an LLM. What the universal approximation theorem tells you is that there's some algorithm you could use to train a (sufficiently-large) perceptron model to behave arbitrarily-closely to the equivalently-trained LLM – which, yeah, of course there is. There's a way of training an array of floating point numbers to do that, too. It's called the transformer architecture. (Your misunderstandings are similar to the sorts of misunderstandings I had before I got in the habit of trying to (abstractly) implement every CS concept I came across. Kudos for trying to reason about AI from first principles, but you have to do a lot of reasoning before you stop being much wronger than the empiricists.)