Is Humbali right that generic uncertainty about maybe being wrong, without other extra premises, should increase the entropy of one's probability distribution over AGI, thereby moving out its median further away in time?
I'll give the homework a shot.
Entropy is the amount of uncertainty inherent in your probability distribution, so generic uncertainty implies an increase in the entropy of one's probability distribution (whatever the eventual result is, it provides you more information than if would have if you were more certain beforehand). However, I do not think it follows that the median is therefore further in the future. Increasing one's generic uncertainty regarding the difficulty of creating AGI rules out knowing that an AGI requires more compute than Google can currently throw at the problem, then it requires ruling out knowing that an AGI can't be created using affordable 2021 consumer hardware, etc. High entropy probability distributions cannot rule out researchers having the final stroke of insight in 20 minutes, or the NSA having an airgapped AGI in their basement since 2017. Generic uncertainty means relying more heavily on your priors; it's not clear to me that this moves the estimate towards longer timelines.
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