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Vaniver comments on Debunking Fallacies in the Theory of AI Motivation - Less Wrong
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You make a valid point - and one worth discussing at length, sometime - but the most important thing right now is that you have misunderstood my position on the question.
First of all, there is a very big distinction between a few people (or even the whole population!) making a deliberate choice to wirehead, and the nanny AI deciding to force everyone to wirehead because that is its interpretation of "making humans happy" (and doing so in a context in which those humans do not want to do it).
You'll notice that in the above quote from my essay, I said that most people would consider it a sign of insanity if a human being were to suggest forcing ALL humans to wirehead, and doing so on the grounds that this was the best way to achieve universal human happiness. If that same human were to suggest that we should ALLOW some humans to wirehead if they believed it would make them happy, then I would not for a moment label that person insane, and quite a lot of people would react the same way.
So I want to be very clear: I very much do acknowledge that the questions regarding various forms of voluntary wireheading are serious and unanswered. I'm in complete agreement with you on that score. But in my paper I was talking only about the apparent contradiction between (a) forcing people to do something as they screamed their protests, while claiming that those people had asked for this to be done, and (b) an assessment that this behavior was both intelligent and sane. My claim in the above quote was that there is a prima facie case to be made, that the proper conclusion is that the behavior would indeed not be intelligent and sane.
(Bear in mind that the quote was just a statement of a prima facie. The point was not to declare that the AI really is insane and/or not intelligent, but to say that there are grounds for questioning. Then the paper goes on to look into the whole problem in more detail. And, most important of all, I am trying to suggest that responding to this my simply declaring that the AI has a 'different' type of intelligence, or even a superior type of 'intelligence' would be a glaring example of sweeping the prima facie case straight into the trashcan without even looking at it.)
You go on to make a different point:
Well, I hope I have reassured you that, even if I do do that, it would not be a generalization of my attitude.
But now, do I do that? I try really hard not to take anything for granted and simply make an appeal to the obviousness of any idea. So you will have to give me some case-by-case examples if you think I really have done that.
You mention only one, and it is a biggie.
The whole issue of proving safety is deeply problematic (a whole essay in itself). I tried to talk about it in the above, but there was not enough space to develop it fully.
The basic points are these:
1) Proof-of-correctness techniques can indeed help with some things, but as Selmer Bringsjord found out to his embarrassment at the 2009 AGI conference, there is a problem if anyone thinks that proofs can be had in high-complexity situations. As I explained at that time, there are two things that go into a proof-of-correctness machine: (a) the target whose correctness is to be proved, and (b) a specification of what qualifies as correctness. In those cases where the specification of what qualifies is simply a list of syntactic rules that must be followed, this approach is valid -- but when the specification of what qualifies as "correct" becomes huge (for example, it involves a massive, open-ended stipulation of the full meaning of "moral behavior" or "friendliness toward humanity"), the specification ITSELF will have bugs in it, and the specification will be of the same magnitude as the target! This means that the specification needs to be checked for correctness first ... and you can see that this quickly leads to an infinite regress.
What this means is that when you point to the security engended by type-checking in functional programming, you seem to be implying that we really need something like that for checking or proving the safety of AGI systems, and really that is never going to be possible. Yes, certain classes of errors can be guaranteed not to occur in some types of programming, but that will never generalize to systems in which the specification of what counts as correct is large. That is a dream that we need to let go of.
(I started writing a paper about that a few weeks ago, in fact, but I stopped doing it because someone pointed out that MIRI no longer even claims that a rigorous proof of Friendliness is even possible. My understanding is, then, that this point I just made is now generally accepted.)
2) However, statistically-oriented 'proofs' can be useful in systems where the overall behavior is caused by a large ensemble of interacting processes, and where the overall behavior cannot be hijacked by any one of the atoms in that ensemble. To wit: we can make dramatically precise statements about the ensemble properties of thermodynamic systems, even though the individual atoms can do whatever they like.
It is that second type of proof-of-friendliness (it is not really a 'proof' it is only a statistical argument) is what I was alluding to when I talked about Swarm Relaxation systems.
So, I don't think I was just assuming that Swarm Relaxation could lead to stronger claims about friendliness, I was basing the claim on a line of reasoning.
So, on rereading the paper I was able to pinpoint the first bit of text that made me think this (the quoted text and the bit before), but am having difficulties finding a second independent example, and so I apologize for the unfairness in generalizing based on one example.
The other examples I found looked like they all relied on the same argument. Consider the following section:
If I think the "logical consistency" argument does not go through, I shouldn't claim this is an independent argument that doesn't go through, because this argument holds given the premises (at least one of which I reject, but it's the same premise). I clearly had this line in mind also:
The 'principal-agent problem' is a fundamental problem in human institutional design: principals would like to be able to hire agents to perform tasks, but only have crude control over the incentives of the agents, and the agents often have control over what information makes it to the principals. One way to characterize the AI value alignment problem (as I hear MIRI is calling it these days) is that it's a principal agent problem where the agent has massive control over the information the principal sees, but the value difference between principals and agents is only due to communication problems, rather than any malice on the part of the agent. That is, the principal wants the agent to do "what I mean," but the agent only has access to "what I say," and cannot be assumed to have any mind-reading powers that we don't build into it.
It seems very difficult to get an AI to correctly classify the difference between programming error and programming intention, and even more difficult for the AI to communicate to us that it has correctly classified that issue. (We have both the illusion of transparency, and the double illusion of transparency to deal with!) Claiming that something is "clearly" a programming error strikes me as trivializing the underlying communication problem. But I agree with you that if we have that problem solved, then we're home free.
I just wanted to say that I will try to reply soon. Unfortunately :-) some of the comments have been intensely thoughtful, causing me to write enormous replies of my own and saturating my bandwidth. So, apologies for any delay....
Some thoughts in response to the above two comments.
First, don't forget that I was trying to debunk a very particular idea, rather than other cases. My target was the idea that a future superintelligent AGI could be programmed to have the very best of intentions, and it might claim to be exercising the most extreme diligence in pursuit of human happiness, while at the same time it might think up a scheme that causes most of humanity to scream with horror while it forces the scheme on those humans. That general idea has been promoted countless times (and has been used to persuade people like Elon Musk and Stephen Hawking to declare that AI could be cause the doom of the human race), and 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.
So, with that in mind, I can say that there are many points of agreement between us on the subject of all those cases that you brought up, above, where there are ethical dilemmas of a lesser sort. There is a lot of scope for us having a detailed discussion about all of those dilemmas -- and I would love to get into the meat of that discussion, at some point -- but that wasn't really what I was trying to tackle in the paper itself.
(One thing I could say about all those cases is that if the AGI were to "only" have the same dilemmas that we have, when trying to figure out the various ethical condundrums of that sort, then we are no worse off than we are now. Some people use the inability of the AGI to come up with optimal solutions in (e.g.) Trolley problems as a way to conclude that said AGIs would be unethical and dangerous. I strongly disagree with those who take that stance).
Here is a more important comment on that, though. Everything really comes down to whether the AGI is going to be subject to bizarre/unexpected failures. In other words, it goes along perfectly well for a long time, apparently staying consistent with what we'd expect of an ethically robust robot, and then one day it suddenly does something totally drastic that turns out to have been caused by a peculiar "edge case" that we never considered when we programmed it. (I am reminded of IBM's Watson answering so many questions correctly on Jeopardy, and then suddenly answering the question "What do grasshoppers eat?" with the utterly stupid reply "Kosher.").
That issue has been at the core of the discussion I have been having with Jessicat, above. I won't try to repeat all of what I said there, but my basic position is that, yes, that is the core question, and what i have tried to do is to explain that there is a feasible way to address precisely that issue. That was what all the "Swarm Relaxation" stuff was about.
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 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.