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Without non-linearity you are mathematically provable not able to get solutions for most contemporary problems.

Refusing the general AI hype bullshit and over-simplifying the hard foundations are quite different things, IMO.

I don't disagree with this. It was a misuse of terminology on my part, my broader point has absolutely nothing to do with the mathematical details of neural networks.

Reason for the nitpicking is, that for me - as a trained mathematician - and seemingly some other commenters, the gap between linearity and non-linearity is yet inside mathematics quite substantial. This is true also in general. Non-linearity might be not sufficient, but it is certainly necessary for any definition and/or serious discussion of AGI.

Just insert the words "these days" and "mostly", and you're pretty much covered. Also a honorable mention for calculus perhaps. Even decision trees use gradient boosting very commonly.

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