I think this comment raises some valid and interesting points. But I’d push back a bit on some points.
(Note that this comment was written quickly, so I may say things a bit unclearly or be saying opinions I haven’t mulled over for a long time.)
More generally, on a strategic level there is very little difference between a genuinely incorrect forecast and one that is “correct”, but communicated so poorly as to create a wrong impression in the mind of the listener.
There’s at least some truth to this. But it’s also possible to ask experts to give a number, as Fermi was asked. If the problem is poor communication, then asking experts to give a number will resolve at least part of the problem (though substantial damage may have been done by planting the verbal estimate in people’s mind). If the problem is poor estimation, then asking for an explicit estimate might make things worse, as it could give a more precise incorrect answer for people to anchor on. (I don’t know of specific evidence that people anchor more on numerical than verbal probability statements, but it seems likely me. Also, to be clear, despite this, I think I’m generally in favour of explicit probability estimates in many cases.)
If the state of affairs is such that anyone who privately believes there is a 10% chance of AGI is incentivized to instead report their assessment as “remote”, the conclusion of Ord/Yudkowsky holds, and it remains impossible to discern whether AGI is imminent by listening to expert forecasts.
I think this is true if no one asks the experts for explicit numerical estimate, or if the incentives to avoid giving such estimates are strong enough that experts will refuse when asked. I think both of those conditions hold to a substantial extent in the real world and in relation to AGI, and that that is a reason why the Fermi case has substantial relevance to the AGI case. But it still seems useful to me to be aware of the distinction between failures of communication vs of estimation, as it seems we could sometimes get evidence that discriminates between which of those is occurring/common, and that which is occurring/common could sometimes be relevant.
Furthermore, and more importantly, however: I deny that Fermi’s 10% somehow detracts from the point that forecasting the future of novel technologies is hard.
I definitely wasn’t claiming that forecasting the future of novel technologies is easy, and I didn’t interpret ESRogs as doing so either. What I was exploring was merely whether this case is a clear case of an expert’s technology forecast being “wrong” (and, if so, “how wrong”), and what this reflects about the typical accuracy of expert technology forecasts. They could conceivably be typically accurate even if very very hard to make, if experts are really good at it and put in lots of effort. But I think more likely they’re often wrong. The important question is essentially “how often”, and this post bites off the smaller question “what does the Fermi case tell us about that”.
As for the rest of the comment, I think both the point estimates and the uncertainty are relevant, at least when judging estimates (rather than making decisions based on them). This is in line with my understanding from e.g. Tetlock’s work. I don’t think I’d read much into an expert saying 1% rather than 10% for something as hard to forecast as an unprecedented tech development, unless I had reason to believe the expert was decently calibrated. But if they have given one of those numbers, and then we see what happens, then which number they gave makes a difference to how calibrated vs uncalibrated I should see them as (which I might then generalise in a weak way to experts more widely).
That said, I do generally think uncertainty of estimates is very important, and think the paper you linked to makes that point very well. And I do think one could easily focus too much on point estimates; e.g., I wouldn’t plug Ord’s existential risk estimates into a model as point estimates without explicitly representing a lot of uncertainty too.
I think this comment raises some valid and interesting points. But I’d push back a bit on some points.
(Note that this comment was written quickly, so I may say things a bit unclearly or be saying opinions I haven’t mulled over for a long time.)
There’s at least some truth to this. But it’s also possible to ask experts to give a number, as Fermi was asked. If the problem is poor communication, then asking experts to give a number will resolve at least part of the problem (though substantial damage may have been done by planting the verbal estimate in people’s mind). If the problem is poor estimation, then asking for an explicit estimate might make things worse, as it could give a more precise incorrect answer for people to anchor on. (I don’t know of specific evidence that people anchor more on numerical than verbal probability statements, but it seems likely me. Also, to be clear, despite this, I think I’m generally in favour of explicit probability estimates in many cases.)
I think this is true if no one asks the experts for explicit numerical estimate, or if the incentives to avoid giving such estimates are strong enough that experts will refuse when asked. I think both of those conditions hold to a substantial extent in the real world and in relation to AGI, and that that is a reason why the Fermi case has substantial relevance to the AGI case. But it still seems useful to me to be aware of the distinction between failures of communication vs of estimation, as it seems we could sometimes get evidence that discriminates between which of those is occurring/common, and that which is occurring/common could sometimes be relevant.
I definitely wasn’t claiming that forecasting the future of novel technologies is easy, and I didn’t interpret ESRogs as doing so either. What I was exploring was merely whether this case is a clear case of an expert’s technology forecast being “wrong” (and, if so, “how wrong”), and what this reflects about the typical accuracy of expert technology forecasts. They could conceivably be typically accurate even if very very hard to make, if experts are really good at it and put in lots of effort. But I think more likely they’re often wrong. The important question is essentially “how often”, and this post bites off the smaller question “what does the Fermi case tell us about that”.
As for the rest of the comment, I think both the point estimates and the uncertainty are relevant, at least when judging estimates (rather than making decisions based on them). This is in line with my understanding from e.g. Tetlock’s work. I don’t think I’d read much into an expert saying 1% rather than 10% for something as hard to forecast as an unprecedented tech development, unless I had reason to believe the expert was decently calibrated. But if they have given one of those numbers, and then we see what happens, then which number they gave makes a difference to how calibrated vs uncalibrated I should see them as (which I might then generalise in a weak way to experts more widely).
That said, I do generally think uncertainty of estimates is very important, and think the paper you linked to makes that point very well. And I do think one could easily focus too much on point estimates; e.g., I wouldn’t plug Ord’s existential risk estimates into a model as point estimates without explicitly representing a lot of uncertainty too.