Because accurate prediction in a specialized domain requires expertise more than motivation. Forecasting is one relevant skill but knowledge of both current AI and knowledge of theoretical paths to AGI are also highly relevant.
Superforecasters can beat domain experts, as shown in Phil Tetlock’s work comparing superforecasters to intelligence analysts.
I’d guess in line with you that for forecasting AGI this might be different, but I am not not sure what weight I’d give superforecasters / prediction platforms versus domain experts.
Right. I think this is different in AGI timelines because standard human expertise/intuition doesn’t apply nearly as well as in the intelligence analyst predictions.
But outside of that speculation, what you really want is predictions from people who have both prediction expertise and deep domain expertise. Averaging in a lot of opinions is probably not helping in a domain so far outside of standard human intuitions.
I think predictions in this domain usually don’t have a specific end-point in mind. They define AGI by capabilities. But the path through mind-space is not at all linear; it probably has strong nonlinearities in both directions. The prediction end-point is in a totally different space than the one in which progress occurs.
Standard intuitions like “projects take a lot longer than their creators and advocates think they will” are useful. But in this case, most people doing the prediction have no gears-level model of the path to AGI, because they have no, or at most a limited, gears-level model of how AGI would work. That is a sharp contrast to thinking about political predictions and almost every other arena of prediction.
So I’d prefer a single prediction from an expert with some gears-level models of AGI for different paths, over all of the prediction experts in the world who lack that crucial cognitive tool.
Why not?
Because accurate prediction in a specialized domain requires expertise more than motivation. Forecasting is one relevant skill but knowledge of both current AI and knowledge of theoretical paths to AGI are also highly relevant.
Superforecasters can beat domain experts, as shown in Phil Tetlock’s work comparing superforecasters to intelligence analysts.
I’d guess in line with you that for forecasting AGI this might be different, but I am not not sure what weight I’d give superforecasters / prediction platforms versus domain experts.
This isn’t accurate, see this post: especially (3a), (3b), and https://docs.google.com/document/d/1ZEEaVP_HVSwyz8VApYJij5RjEiw3mI7d-j6vWAKaGQ8/edit?tab=t.0#heading=h.mma60cenrfmh Goldstein et al (2015)
Right. I think this is different in AGI timelines because standard human expertise/intuition doesn’t apply nearly as well as in the intelligence analyst predictions.
But outside of that speculation, what you really want is predictions from people who have both prediction expertise and deep domain expertise. Averaging in a lot of opinions is probably not helping in a domain so far outside of standard human intuitions.
I think predictions in this domain usually don’t have a specific end-point in mind. They define AGI by capabilities. But the path through mind-space is not at all linear; it probably has strong nonlinearities in both directions. The prediction end-point is in a totally different space than the one in which progress occurs.
Standard intuitions like “projects take a lot longer than their creators and advocates think they will” are useful. But in this case, most people doing the prediction have no gears-level model of the path to AGI, because they have no, or at most a limited, gears-level model of how AGI would work. That is a sharp contrast to thinking about political predictions and almost every other arena of prediction.
So I’d prefer a single prediction from an expert with some gears-level models of AGI for different paths, over all of the prediction experts in the world who lack that crucial cognitive tool.