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You're conflating between "have important consequences" and "can be used as weapons in discourse"

What do you mean by example, here? That this is demonstrating a broader property, or that in this situation, there was a tribal dynamic?

Typo, thanks for pointing it out. Also, see here for the physics reference: https://en.m.wikipedia.org/wiki/1_%2B_2_%2B_3_%2B_4_%2B_⋯

I'm one of these professional mathematicians, and I'll say that this article completely fails to demonstrate it's central thesis that there is a valid intuitive argument for concluding that 1 + 2 + 3 + ... = -1/12 makes sense. What's worse, it only pretends to do so by what's essentially a swindle. In my understanding, it's relatively easy to reason that a given divergent series "should" take an arbitrary finite value by the kind of arguments employed here, so what is being done is taking a foregone conclusion and providing some false intuition for why it should be true.

On a less serious note, speaking to the real reason why 1 + 2 + 3 + ... = -1/12, that's actually what physicists will tell you, and we all know one should be careful around those.

Sure, but you're just claiming that, and I don't think it's actually true.

You run into the trouble of having to defend why your way to fit the divergent series into a pattern is the right one - other approaches may give different results.

I think it's quite unlikely that GPT 5 will destroy the world. That said, I think it's generally reasonable to doubt prediction markets on questions that can't be fairly evaluated both ways.

I think the possibility of compute overhang seems plausible given the technological realities, but generalizing from this to a second-order overhang etc. seems taking it too far.

If there is an argument that we should push compute due to the danger of another "overhang" down the line that should be made explicitly and not by generalisation from one (debatable!) example.

Sorry, low effort comment on my side. Still, I think the original link seems misleading in the point it's purportedly trying to make.

Doesn't have any bearing historically. Also seems more like a brute force search, where the component of studying the materials properties has been made more efficient (by partially replacing lab experiments with deep learning).

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