this post was submitted on 25 Nov 2023
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Machine Learning
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I mean, everyone is just sorta ignoring the fact that no ML technique has been shown to do anything more than just mimic statistical aspects of the training set. Is statistical mimicry AGI? On some performance benchmarks, it appears better statistical mimicry does approach capabilities we associate with AGI.
I personally am quite suspicious that the best lever to pull is just giving it more parameters. Our own brains have such complicated neural/psychological circuitry for executive function, long and short term memory, types I and II thinking, "internal" dialog and visual models, and more importantly, the ability to few-shot learn the logical underpinnings of an example set. Without a fundamental change in how we train NNs or even our conception of effective NNs to begin with, we're not going to see the paradigm shift everyone's been waiting for.
We need a name for the fallacy where people call highly nonlinear algorithms with billions of parameters "just statistics", as if all they're doing is linear regression.
ChatGPT isn't AGI yet, but it is a huge leap in modeling natural language. The fact that there's some statistics involved explains neither of those two points.
It’s not a fallacy at all. It is just statistics, combined with some very useful inductive biases. The fallacy is trying to smuggle some extra magic into the description of what it is.
Actual AGI would be able to explain something that no human has understood before. We aren’t really close to that at all. Falling back on “___ may not be AGI yet, but…” is a lot like saying “rocket ships may not be FTL yet, but…”
And the human brain is FTL then?