It’s not reverse stupidity—it’s “reference class forecasting”, which is a more specific instance of our generic “outside view” concept. I gather data about AI research as an instance, look at other cases with similar characteristics (hyped overpromised and underdelivered over a very long time span) and estimate based on that. It is proven to work better than inside view of estimating based on details of a particular case.
I agree that reference class forecasting is reasonable here. I disagree that you can get anything like the 99.999% probability you claim from applying reference class forecasting to AI projects. Since rare events happen, well, rarely, it would take an exceedingly large data-set before an “outside view” or frequency-based analysis would imply that our actual expected rate should be placed as low as your stated 0.001%. (If I flip a coin with unknown weighting 20 times, and get no heads, I should conclude that heads are probably rare, but my notion of “rare” here should be on the order of 1 in 20, not of 1 in 100,000.)
With more precision: let’s say that there’s a “true probability”, p, that any given project’s “AI will be created by us” claim is correct. And let’s model p as being identical for all projects and times. Then, if we assume a uniform prior over p, and if n AI projects that have been tried to date have failed to deliver, we should assign a probability of ((1+n)/n+2) to the chance that the next project from which AI is forecast will also fail to deliver. (You can work this out by an integral, or just plug into Laplace’s rule of succession).
If people have been forecasting AI since about 1950, and if the rate of forecasts or AI projects per decade has been more or less unchanged, the above reference class forecasting model leaves us with something like a 1/[number of decades since 1950 + 2] = 1⁄8 probability of some “our project will make AI” forecast being correct in the next decade.
That said, I still take issue with reference class forecasting as support for this statement:
I don’t believe in feasibility of any scenario like AGI foom.
Considering that the general question “is the foom scenario feasible?” doesn’t have any concrete timelines attached to it, the speed and direction of AI research don’t bear too heavily on it. All you can say about it based on reference class forecasting is that it’s a long way away if it’s both possible and requires much AI research progress.
Even if AGI happens, it is extraordinarily unlikely it will be any kind of foom, again based on outside view argument that virtually none of disruptive technologies were ever foom-like.
I’m not sure “disruptive technology” is the obvious category for AGI. The term basically dereferences to “engineered human-level intelligence”, easily suggesting comparisons to various humans, hominids, primates, etc.
It seems like you’re reversing stupidity here. What correlation does a failed prediction have with the future?
It’s not reverse stupidity—it’s “reference class forecasting”, which is a more specific instance of our generic “outside view” concept. I gather data about AI research as an instance, look at other cases with similar characteristics (hyped overpromised and underdelivered over a very long time span) and estimate based on that. It is proven to work better than inside view of estimating based on details of a particular case.
http://en.wikipedia.org/wiki/Reference_class_forecasting
I agree that reference class forecasting is reasonable here. I disagree that you can get anything like the 99.999% probability you claim from applying reference class forecasting to AI projects. Since rare events happen, well, rarely, it would take an exceedingly large data-set before an “outside view” or frequency-based analysis would imply that our actual expected rate should be placed as low as your stated 0.001%. (If I flip a coin with unknown weighting 20 times, and get no heads, I should conclude that heads are probably rare, but my notion of “rare” here should be on the order of 1 in 20, not of 1 in 100,000.)
With more precision: let’s say that there’s a “true probability”, p, that any given project’s “AI will be created by us” claim is correct. And let’s model p as being identical for all projects and times. Then, if we assume a uniform prior over p, and if n AI projects that have been tried to date have failed to deliver, we should assign a probability of ((1+n)/n+2) to the chance that the next project from which AI is forecast will also fail to deliver. (You can work this out by an integral, or just plug into Laplace’s rule of succession).
If people have been forecasting AI since about 1950, and if the rate of forecasts or AI projects per decade has been more or less unchanged, the above reference class forecasting model leaves us with something like a 1/[number of decades since 1950 + 2] = 1⁄8 probability of some “our project will make AI” forecast being correct in the next decade.
Oops. You’re totally right.
That said, I still take issue with reference class forecasting as support for this statement:
Considering that the general question “is the foom scenario feasible?” doesn’t have any concrete timelines attached to it, the speed and direction of AI research don’t bear too heavily on it. All you can say about it based on reference class forecasting is that it’s a long way away if it’s both possible and requires much AI research progress.
I’m not sure “disruptive technology” is the obvious category for AGI. The term basically dereferences to “engineered human-level intelligence”, easily suggesting comparisons to various humans, hominids, primates, etc.