it is has also been cited as an almost inevitable end point of the process of AGI development, rather than just a very-low-risk possibility with massive consequences.
I suspect this may be because of different traditions. I have a lot of experience in numerical optimization, and one of my favorite optimization stories is Dantzig’s attempt to design an optimal weight-loss diet, recounted here. The gap between a mathematical formulation of a problem, and the actual problem in reality, is one that I come across regularly, and I’ve spent many hours building bridges over those gaps.
As a result, I find it easy to imagine that I’ve expressed a complicated problem in a way that I hope is complete, but the optimization procedure returns a solution that is insane for reality but perfect for the problem as I expressed it. As the role of computers moves from coming up with plans that humans have time to verify (like delivering a recipe to Anne, who can laugh off the request for 500 gallons of vinegar) to executing actions that humans do not have time to verify (like various emergency features of cars, especially the self-driving variety, or high-frequency trading), this possibility becomes more and more worrying. (Even when humans do verify the system, the more trustworthy the system, the more the human operator will trust it—and thus, the more likely that the human will fail to catch a system error.)
Similarly, one might think of another technological field whose history is more mature, yet still recent: aircraft design. The statement “that airplanes will crash is almost inevitable” seems more wise than not to me—out of all of the possible designs that you or I would pattern-match to an “airplane,” almost all of them crash. Unfortunately, designs that their designer is sure will work still crash sometimes. Of course, some airplanes work, and we’ve found those designs, and a hundred years into commercial air travel the statement that crashes are almost inevitable seems perhaps silly.
So just like we might acknowledge that it’s difficult to get a plane that flies without crashing, and it’s also difficult to be sure that a design will fly without crashing without testing it, it seems reasonable to claim that it will also be difficult for AGI design to operate without unrecoverable mistakes—but even more so.
Everything really comes down to whether the AGI is going to be subject to bizarre/unexpected failures.
I agree with this, and further, I agree that concepts encoded by many weak constraints will be more robust than concepts encoded by few hard constraints, by intuitions gained from ensemble learners.
I might elaborate the issue further, by pointing out that there is both the engineering issue, of whether or not it fails gracefully in all edge cases, and the communication issue, of whether or not we are convinced that it will fail gracefully. Both false positives and false negatives are horrible.
Finally, to reply to your statement that you think the “logical consistency” idea does not go through, can I ask that you look at my reply to “misterbailey”, elsewhere in the comments? He asked a question, so I tried to clarify exactly where the logical inconsistency was located. Apparently he had misunderstood what the inconsistency was supposed to be. It might be that the way I phrased it there could shed some light on your disagreement with it. Let me know if it does.
I don’t think I agree with point 1 that you raise here:
1) Conclusions produced by my reasoning engine are always correct. [This is the Doctrine of Logical Infallibility]
I think that any active system has to implicitly follow a doctrine that I’ll state as “I did the best I could have, knowing what I did then.” That is, I restate your (2) as the system knowing that, living in an uncertain universe and not having logical omniscience, it will eventually make mistakes. Perhaps in response it will shut itself down, and become an inactive system (this is the inconsistency that I think you’re pointing at). Or perhaps it will run the numbers and say “it’s better to try something than do nothing, even after taking the risk of mistakes into account.”
Now, of course, this isn’t an imperative to always act immediately without consideration. Oftentimes, the thing to try is “wait and think of a better plan” or “ask others if this is a good idea or not,” but the problem of discernment shows up again. What logic is the system using to determine when to stop thinking about new plans? What logic is it using to determine whether or not to ask for advice? If it could predict what mistakes it would make ahead of time, it wouldn’t make them!
To go back to my analogy of aircraft design: suppose we talked about the “quality” of aircraft, which was some overall gestalt of how well they flew, and we eagerly looked forward to days when aircraft designs become better.
At one point, the worry is raised that future aircraft designs might be too high quality. On the face of it, this sounds ridiculous: how could it be that increasing the quality of the aircraft makes it less safe or desirable? Further elaboration reveals that there are two parts of aircraft design: engines and guidance systems. If engines grow much more powerful, but guidance systems remain the same, the aircraft might become much less safe—a tremble at the controls, and now the aircraft is spinning madly.
Relatedly, I found your “Because intelligence” section unsatisfying, because it seems like it’s resisting that sort of separation—separating ‘intelligence’ into, say, ‘cleverness’ (the ability to find a plan that achieves some consequence) and ‘wisdom’ (the ability to determine the consequences of a plan, and the desirability of those consequences) seems helpful when talking about designing intelligent agents.
I think Eliezer and others point out that systems that are very clever but not very wise are very dangerous and that cleverness and wisdom are potentially generated by different components. It seems to me that your models of intelligence have a deeper connection between cleverness and wisdom, and so you think it’s considerably less likely that we’ll get that sort of dangerous system that is clever but not wise.
I suspect this may be because of different traditions. I have a lot of experience in numerical optimization, and one of my favorite optimization stories is Dantzig’s attempt to design an optimal weight-loss diet, recounted here. The gap between a mathematical formulation of a problem, and the actual problem in reality, is one that I come across regularly, and I’ve spent many hours building bridges over those gaps.
As a result, I find it easy to imagine that I’ve expressed a complicated problem in a way that I hope is complete, but the optimization procedure returns a solution that is insane for reality but perfect for the problem as I expressed it. As the role of computers moves from coming up with plans that humans have time to verify (like delivering a recipe to Anne, who can laugh off the request for 500 gallons of vinegar) to executing actions that humans do not have time to verify (like various emergency features of cars, especially the self-driving variety, or high-frequency trading), this possibility becomes more and more worrying. (Even when humans do verify the system, the more trustworthy the system, the more the human operator will trust it—and thus, the more likely that the human will fail to catch a system error.)
Similarly, one might think of another technological field whose history is more mature, yet still recent: aircraft design. The statement “that airplanes will crash is almost inevitable” seems more wise than not to me—out of all of the possible designs that you or I would pattern-match to an “airplane,” almost all of them crash. Unfortunately, designs that their designer is sure will work still crash sometimes. Of course, some airplanes work, and we’ve found those designs, and a hundred years into commercial air travel the statement that crashes are almost inevitable seems perhaps silly.
So just like we might acknowledge that it’s difficult to get a plane that flies without crashing, and it’s also difficult to be sure that a design will fly without crashing without testing it, it seems reasonable to claim that it will also be difficult for AGI design to operate without unrecoverable mistakes—but even more so.
I agree with this, and further, I agree that concepts encoded by many weak constraints will be more robust than concepts encoded by few hard constraints, by intuitions gained from ensemble learners.
I might elaborate the issue further, by pointing out that there is both the engineering issue, of whether or not it fails gracefully in all edge cases, and the communication issue, of whether or not we are convinced that it will fail gracefully. Both false positives and false negatives are horrible.
I don’t think I agree with point 1 that you raise here:
I think that any active system has to implicitly follow a doctrine that I’ll state as “I did the best I could have, knowing what I did then.” That is, I restate your (2) as the system knowing that, living in an uncertain universe and not having logical omniscience, it will eventually make mistakes. Perhaps in response it will shut itself down, and become an inactive system (this is the inconsistency that I think you’re pointing at). Or perhaps it will run the numbers and say “it’s better to try something than do nothing, even after taking the risk of mistakes into account.”
Now, of course, this isn’t an imperative to always act immediately without consideration. Oftentimes, the thing to try is “wait and think of a better plan” or “ask others if this is a good idea or not,” but the problem of discernment shows up again. What logic is the system using to determine when to stop thinking about new plans? What logic is it using to determine whether or not to ask for advice? If it could predict what mistakes it would make ahead of time, it wouldn’t make them!
To go back to my analogy of aircraft design: suppose we talked about the “quality” of aircraft, which was some overall gestalt of how well they flew, and we eagerly looked forward to days when aircraft designs become better.
At one point, the worry is raised that future aircraft designs might be too high quality. On the face of it, this sounds ridiculous: how could it be that increasing the quality of the aircraft makes it less safe or desirable? Further elaboration reveals that there are two parts of aircraft design: engines and guidance systems. If engines grow much more powerful, but guidance systems remain the same, the aircraft might become much less safe—a tremble at the controls, and now the aircraft is spinning madly.
Relatedly, I found your “Because intelligence” section unsatisfying, because it seems like it’s resisting that sort of separation—separating ‘intelligence’ into, say, ‘cleverness’ (the ability to find a plan that achieves some consequence) and ‘wisdom’ (the ability to determine the consequences of a plan, and the desirability of those consequences) seems helpful when talking about designing intelligent agents.
I think Eliezer and others point out that systems that are very clever but not very wise are very dangerous and that cleverness and wisdom are potentially generated by different components. It seems to me that your models of intelligence have a deeper connection between cleverness and wisdom, and so you think it’s considerably less likely that we’ll get that sort of dangerous system that is clever but not wise.