[epistemer]

I see it as the opposite. It is amazing what the model can do with poor quantitative reasoning.

I just can't imagine adding super human quantitative reasoning is going to be that big of a stumbling block over the next decade. If anything that is probably the low hanging fruit here for a huge jump into the unknown.

[Someone]

Looking at the state if the art in automated theorem provers, I don’t think that’s low hanging fruit.

We probably can make something that can calculate well and won’t make mistakes in combining various numbers found online, and can do rote evaluation of expressions not found anywhere online, but adding ‘reasoning’?

Even disregarding that it would have to, somehow, assign different trust levels to various online sources (for example, are, https://en.uncyclopedia.co/wiki/Wikipedia or https://en.wikipedia.org/wiki/Uncyclopedia trustworthy?), it, IMO, already would fall at the hurdle of doing ‘simple’ math.

For example “the sine of 100 factorial” has a well-defined value, but computing it in IEEE doubles doesn’t make sense because representable numbers are way too far apart around 100! (Google says it is about 0.68395718932, but it also thinks that sin(1+100!) ≈ 0.68395718932. I trust neither answer)

That’s solvable by using better software. Wolfram Alpha claims these are about -0.17, respectively -0.92, for example, but in my book, an AI wouldn’t be intelligent if it always used one; it would have to know when to fall back on the heavy guns. For the “what’s sin(100!)” question, I think the first response might be a counter-question “why do you want to know?”, but that depends on earlier discussion.