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.
ChatGPT predicts the most probable next token, or the next token that yields the highest probability of a thumbs up, depending on whether you're talking about the semi-supervised learning or the reinforcement learning stage of training. That is the conceptual underpinning of how the parameter updates are calculated. It only achieves the ability to communicate because it was trained on text that successfully communicates.