One [methodology](https://www.overcomingbias.com/2012/08/ai-progress-estimate.html) for forecasting AI timelines is to ask experts how much progress they have made to human-level AI within their subfield over the last T years. You can then extrapolate linearly to see when 100% of the problem will be solved. The post linked above collects such estimates, with a typical estimate being 5% of a problem being solved in the twenty year period between 1992 and 2012. Overall these estimates imply a timeline of [372 years](https://aiimpacts.org/surveys-on-fractional-progress-towards-hlai/).
This post provides a reductio argument against this pair of methodology and estimate. The core argument is that if you linearly extrapolate, then you are effectively saying “assume that business continues as usual: then how long does it take”? But “business as usual” in the case of the last 20 years involves an increase in the amount of compute used by AI researchers by a factor of ~1000, so this effectively says that we’ll get to human-level AI after a 1000^{372/20} = 10^56 increase in the amount of available compute. (The authors do a somewhat more careful calculation that breaks apart improvements in price and growth of GDP, and get 10^53.)
This is a stupendously large amount of compute: it far dwarfs the amount of compute used by evolution, and even dwarfs the maximum amount of irreversible computing we could have done with all the energy that has ever hit the Earth over its lifetime (the bound comes from [Landauer’s principle](https://en.wikipedia.org/wiki/Landauer%27s_principle)).
Given that evolution _did_ produce intelligence (us), we should reject the argument. But what should we make of the expert estimates then? One interpretation is that “proportion of the problem solved” behaves more like an exponential, because the inputs are growing exponentially, and so the time taken to do the last 90% can be much less than 9x the time taken for the first 10%.
Planned opinion:
This seems like a pretty clear reductio to me, though it is possible to argue that this argument doesn’t apply because compute isn’t the bottleneck, i.e. even with infinite compute we wouldn’t know how to make AGI. (That being said, I mostly do think we could build AGI if only we had enough compute; see also <@last week’s highlight on the scaling hypothesis@>(@The Scaling Hypothesis@).)
Ah, fair point, looking back at this summary I probably should have clarified that the methodology could be applied with other samples and those look much less long.
Planned summary:
Planned opinion:
“Overall these estimates imply a timeline of [372 years](https://aiimpacts.org/surveys-on-fractional-progress-towards-hlai/).”
That was only for Hanson’s convenience sample, other surveys using the method gave much shorter timelines, as discussed in the post.
Ah, fair point, looking back at this summary I probably should have clarified that the methodology could be applied with other samples and those look much less long.