Just want to quickly flag that you seem to have far more faith in superforecasters’ long-range predictions than do most people who have worked full-time in forecasting, such as myself.
@MichaelDickens’ ‘Is It So Much to Ask?’ is the best public writeup I’ve seen on this (specifically, on the problems with Metaculus’ and FRI XPT’s x-risk/extinction forecasts, which are cited in the main post above). I also very much agree with:
Excellent forecasters and Superforecasters™ have an imperfect fit for long-term questions
Here are some reasons why we might expect longer-term predictions to be more difficult:
No fast feedback loops for long-term questions. You can’t get that many predict/check/improve cycles, because questions many years into the future, tautologically, take many years to resolve. There are shortcuts, like this past-casting app, but they are imperfect.
It’s possible that short-term forecasters might acquire habits and intuitions that are good for forecasting short-term events, but bad for forecasting longer-term outcomes. For example, “things will change more slowly than you think” is a good heuristic to acquire for short-term predictions, but might be a bad heuristic for longer-term predictions, in the same sense that “people overestimate what they can do in a week, but underestimate what they can do in ten years”. This might be particularly insidious to the extent that forecasters acquire intuitions which they can see are useful, but can’t tell where they come from. In general, it seems unclear to what extent short-term forecasting skills would generalize to skill at longer-term predictions.
“Predict no change” in particular might do well, until it doesn’t. Consider a world which has a 2% probability of seeing a worldwide pandemic, or some other large catastrophe. Then on average it will take 50 years for one to occur. But at that point, those predicting a 2% will have a poorer track record compared to those who are predicting a ~0%.
In general, we have been in a period of comparative technological stagnation, and forecasters might be adapted to that, in the same way that e.g., startups adapted to low interest rates.
Sub-sampling artifacts within good short-term forecasters are tricky. For example, my forecasting group Samotsvety is relatively bullish on transformative technological change from AI, whereas the Forecasting Research Institute’s pick of forecasters for their existential risk survey was more bearish.
How much weight should we give to these aggregates?
My personal tier list for how much weight I give to AI x-risk forecasts to the extent I defer:
Individual forecasts from people who seem to generally have great judgment, and have spent a ton of time thinking about AI x-risk forecasting e.g. Cotra, Carlsmith
Samotsvety aggregates presented here
A superforecaster aggregate (I’m biased re: quality of Samotsvety vs. superforecasters, but I’m pretty confident based on personal experience)
Individual forecasts from AI domain experts who seem to generally have great judgment, but haven’t spent a ton of time thinking about AI x-risk forecasting (this is the one I’m most uncertain about, could see anywhere from 2-4)
Everything else I can think of I would give little weight to.[1][2]
Separately, I think you’re wrong about UK AISI not putting much credence on extinction scenarios? I’ve seen jobadverts from AISI that talk about loss of control risk (i.e., AI takeover), and I know people working at AISI who—last I spoke to them—put ≫10% on extinction.
Why do I give little weight to Metaculus’s views on AI? Primarily because of the incentives to make very shallow forecasts on a ton of questions (e.g. probably <20% of Metaculus AI forecasters have done the equivalent work of reading the Carlsmith report), and secondarily that forecasts aren’t aggregated from a select group of high performers but instead from anyone who wants to make an account and predict on that question.
Why do I give little weight to AI expert surveys such as When Will AI Exceed Human Performance? Evidence from AI Experts? I think most AI experts have incoherent and poor views on this because they don’t think of it as their job to spend time thinking and forecasting about what will happen with very powerful AI, and many don’t have great judgment.
You might be right re forecasting (though someone willing in general to frequently bet on 2% scenarios manifesting should fairly quickly outperform someone who frequently bets against them—if their credences are actually more accurate).
I think you’re wrong about UK AISI not putting much credence on extinction scenarios? I’ve seen jobadverts from AISI talking about loss of control risk (i.e., AI takeover), and how ‘the risks from AI are not sci-fi, they are urgent.’ And I know people working at AISI who, last I spoke to them, put ≫10% on extinction.
The two jobs you mention only refer to ‘loss of control’ as a single concern among many - ‘risks with security implications, including the potential of AI to assist with the development of chemical and biological weapons, how it can be used to carry out cyber-attacks, enable crimes such as fraud, and the possibility of loss of control.’
I’m not claiming that these orgs don’t or shouldn’t take the lesser risks and extreme tail risks seriously (I think they should and do), but denying the claim that people who ‘think seriously’ about AI risks necessarily lean towards high extinction probabilities.
Just want to quickly flag that you seem to have far more faith in superforecasters’ long-range predictions than do most people who have worked full-time in forecasting, such as myself.
@MichaelDickens’ ‘Is It So Much to Ask?’ is the best public writeup I’ve seen on this (specifically, on the problems with Metaculus’ and FRI XPT’s x-risk/extinction forecasts, which are cited in the main post above). I also very much agree with:
Separately, I think you’re wrong about UK AISI not putting much credence on extinction scenarios? I’ve seen job adverts from AISI that talk about loss of control risk (i.e., AI takeover), and I know people working at AISI who—last I spoke to them—put ≫10% on extinction.
You might be right re forecasting (though someone willing in general to frequently bet on 2% scenarios manifesting should fairly quickly outperform someone who frequently bets against them—if their credences are actually more accurate).
The two jobs you mention only refer to ‘loss of control’ as a single concern among many - ‘risks with security implications, including the potential of AI to assist with the development of chemical and biological weapons, how it can be used to carry out cyber-attacks, enable crimes such as fraud, and the possibility of loss of control.’
I’m not claiming that these orgs don’t or shouldn’t take the lesser risks and extreme tail risks seriously (I think they should and do), but denying the claim that people who ‘think seriously’ about AI risks necessarily lean towards high extinction probabilities.