this post was submitted on 25 Nov 2023
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Machine Learning

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[–] El_Minadero@alien.top 0 points 10 months ago (43 children)

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.

[–] gebregl@alien.top 1 points 10 months ago (24 children)

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.

[–] Appropriate_Ant_4629@alien.top 1 points 10 months ago (2 children)

We need a name for the fallacy where people call highly nonlinear algorithms with billions of parameters "just statistics"

Well, thanks to quantum mechanics; pretty much all of existence is probably "just statistics".

as if all they're doing is linear regression.

Well, practically all interesting statistics are NONlinear regressions. Including ML. And your brain. And physics.

[–] KoalaNumber3@alien.top 1 points 10 months ago (1 children)

What a lot of people don’t understand is that linear regression can still handle non-linear relationships.

For a statistician, linear regression just means the coefficients are linear, it doesn’t mean the relationship itself is a straight line.

That’s why linear models are still incredibly powerful and are used so widely across so many fields.

[–] Appropriate_Ant_4629@alien.top 1 points 9 months ago

Yet still limited compared to even not-very-deep NNs. If the user wants to fit a parabola with a linear regression, he pretty much has to manually add a quadratic term himself.

I think they're widely used primarily because they're widely taught in school.

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