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Wei_Dai comments on AlphaGo versus Lee Sedol - Less Wrong
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When I started hearing about the latest wave of results from neural networks, I thought to myself that Eliezer was probably wrong to bet against them. Should MIRI rethink its approach to friendliness?
Compared to its competition in the AGI race, MIRI was always going to be disadvantaged by both lack of resources and the need to choose an AI design that can predictably be made Friendly as opposed to optimizing mainly for capability. For this reason, I was against MIRI (or rather the Singularity Institute as it was known back then) going into AI research at all, as opposed to pursuing some other way of pushing for a positive Singularity.
In any case, what other approaches to Friendliness would you like MIRI to consider? The only other approach that I'm aware of that's somewhat developed is Paul Christiano's current approach (see for example https://medium.com/ai-control/alba-an-explicit-proposal-for-aligned-ai-17a55f60bbcf), which I understand is meant to be largely agnostic about the underlying AI technology. Personally I'm pretty skeptical but then I may be overly skeptical about everything. What are your thoughts? I don't recall seeing you having commented on them much.
Are you aware of any other ideas that MIRI should be considering?
Do you have a concise explanation of skepticism about the overall approach, e.g. a statement of the difficulty or difficulties you think will be hardest to overcome by this route?
Or is your view more like "most things don't work, and there isn't much reason to think this would work"?
In discussion you most often push on the difficulty of doing reflection / philosophy. Would you say this is your main concern?
My take has been that we just need to meet the lower bar of "wants to defer to human views about philosophy, and has a rough understanding of how humans want to reflect and want to manage their uncertainty in the interim."
Regarding philosophy/metaphilosophy, is it fair to describe your concern as one of:
My hope is that thinking and talking more about bootstrapping procedures would go a long way to resolving the disagreements between us (either leaving you more optimistic or me more pessimistic). I think this is most plausible if #1 is the main disagreement. If our disagreement is somewhere else, it may be worth also spending some time focusing somewhere else. Or it may be necessary to better define my lower bar in order to tell where the disagreement is.
It seems to be a combination of all of these.
Re 1:
For a working scheme, I would expect it to be usable by a significant fraction of humans (say, comparable to the fraction that can learn to write a compiler).
That said, I would not expect almost anyone to actually play the role of the overseer, even if a scheme like this one ended up being used widely. An existing analogy would be the human trainers who drive facebook's M (at least in theory, I don't know how that actually plays out). The trainers are responsible for getting M to do what the trainers want, and the user trusts the trainers to do what the user wants. From the user's perspective, this is no different from delegating to the trainers directly, and allowing them to use whatever tools they like.
I don't yet see why "defer to human judgments and handle uncertainty in a way that they would endorse" requires evaluating complex philosophical arguments or having a correct understanding of metaphilosophy. If the case is unclear, you can punt it to the actual humans.
If I imagine an employee who sucks at philosophy but thinks 100x faster than me, I don't feel like they are going to fail to understand how to defer to me on philosophical questions. I might run into trouble because now it is comparatively much harder to answer philosophical questions, so to save costs I will often have to do things based on rough guesses about my philosophical views. But the damage from using such guesses depends on the importance of having answers to philosophical questions in the short-term.
It really feels to me like there are two distinct issues:
These seem like separate issues to me. I am convinced that #2 is very important, since it seems like the largest existential risk by a fair margin and also relatively tractable. I think that #1 does add some value, but am not at all convinced that it is a maximally important problem to work on. As I see it, the value of #1 depends on the importance of the ethical questions we face in the short term (and on how long-lasting are the effects of differential technological progress that accelerates our philosophical ability).
Moreover, it seems like we should evaluate solutions to these two problems separately. You seem to be making an implicit argument that they are linked, such that a solution to #2 should only be considered satisfactory if it also substantially addresses #1. But from my perspective, that seems like a relatively minor consideration when evaluating the goodness of a solution to #2. In my view, solving both problems at once would be at most 2x as good as solving the more important of the two problems. (Neither of them is necessarily a crisp problem rather than an axis along which to measure differential technological development.)
I can see several ways in which #1 and #2 are linked, but none of them seem very compelling to me. Do you have something in particular in mind? Does my position seem somehow more fundamentally mistaken to you?
(This comment was in response to point 1, but it feels like the same underlying disagreement is central to points 2 and 3. Point 4 seems like a different concern, about how the availability of AI would itself change philosophical deliberation. I don't really see much reason to think that the availability of powerful AI would make the endpoint of deliberation worse rather than better, but probably this is a separate discussion.)
In that case, there would be severe principle-agent problems, given the disparity between power/intelligence of the trainer/AI systems and the users. If I was someone who couldn't directly control an AI using your scheme, I'd be very concerned about getting uneven trades or having my property expropriated outright by individual AIs or AI conspiracies, or just ignored and left behind in the race to capture the cosmic commons. I would be really tempted to try another AI design that does purport to have the AI serve my interests directly, even if that scheme is not as "safe".
If an employee sucks at philosophy, how does he even recognize philosophical problems as problems that he needs to consult you for? Most people have little idea that they should feel confused and uncertain about things like epistemology, decision theory, and ethics. I suppose it might be relatively easy to teach an AI to recognize the specific problems that we currently consider to be philosophical, but what about new problems that we don't yet recognize as problems today?
Aside from that, a bigger concern for me is that if I was supervising your AI, I would be constantly bombarded with philosophical questions that I'd have to answer under time pressure, and afraid that one wrong move would cause me to lose control, or lock in some wrong idea.
Consider this scenario. Your AI prompts you for guidance because it has received a message from a trading partner with a proposal to merge your AI systems and share resources for greater efficiency and economy of scale. The proposal contains a new AI design and control scheme and arguments that the new design is safer, more efficient, and divides control of the joint AI fairly between the human owners according to your current bargaining power. The message also claims that every second you take to consider the issue has large costs to you because your AI is falling behind the state of the art in both technology and scale, becoming uncompetitive, so your bargaining power for joining the merger is dropping (slowly in the AI's time-frame, but quickly in yours). Your AI says it can't find any obvious flaws in the proposal, but it's not sure that you'd consider the proposal to really be fair under reflective equilibrium or that the new design would preserve your real values in the long run. There are several arguments in the proposal that it doesn't know how to evaluate, hence the request for guidance. But it also reminds you not to read those arguments directly since they were written by a superintelligent AI and you risk getting mind-hacked if you do.
What do you do? This story ignores the recursive structure in ALBA. I think that would only make the problem even harder, but I could be wrong. If you don't think it would go like this, let me know how you think this kind of scenario would go.
In terms of your #1, I would divide the decisions requiring philosophical understanding into two main categories. One is decisions involved in designing/improving AI systems, like in the scenario above. The other, which I talked about in an earlier comment, is ethical disasters directly caused by people who are not uncertain, but just wrong. You didn't reply to that comment, so I'm not sure why you're unconcerned about this category either.
A general note: I'm not really taking a stand on the importance of a singleton, and I'm open to the possibility that the only way to achieve a good outcome even in the medium-term is to have very good coordination.
A would-be singleton will also need to solve the AI control problem, and I am just as happy to help with that problem as with the version of the AI control problem faced by a whole economy of actors each using their own AI systems.
The main way in which this affects my work is that I don't want to count on the formation of a singleton to solve the control problem itself.
You could try to work on AI in a way that helps facilitate the formation of a singleton. I don't think that is really helpful, but moreover it again seems like a separate problem from AI control. (Also don't think that e.g. MIRI is doing this with their current research, although they are open to solving AI control in a way that only works if there is a singleton.)
In general I think that counterfactual oversight has problems in really low-latency environments. I think the most natural way to avoid them is synthesizing training data in advance. It's not clear whether that proposal will work.
If your most powerful learners are strong enough to learn good-enough answers to these kinds of philosophical questions, then you only need to provide philosophical input during training and so synthesizing training data can take off time pressure. If your most powerful AI is not able to learn how to answer these philosophical questions, then the time pressure seems harder to avoid. In that case though, it seems quite hard to avoid the time pressure by any mechanism. (Especially if we are better at learning than we would be at hand-coding an algorithm for philosophical deliberation---if we are better at learning and our learner can't handle philosophy, then we simply aren't going to be able to build an AI that can handle philosophy.)
I replied to your earlier comment.
My overall feeling is still that these are separate problems. We can evaluate a solution to AI control, and we can evaluate philosophical work that improves our understanding of potentially-relevant issues (or metaphilosophical work to automate philosophy).
I am both less pessimistic about philosophical errors doing damage, and more optimistic about my scheme's ability to do philosophy, but it's not clear to me that either of those is the real disagreement (since if I imagining caring a lot about philosophy and thinking this scheme didn't help automate philosophy, I would still feel like we were facing two distinct problems).
Is this your reaction if you imagine delegating your affairs to an employee today? Are you making some claim about the projected increase in the importance of these philosophical decisions? Or do you think that a brilliant employees' lack of metaphilosophical understanding would in fact cause great damage right now?
I agree that AI may increase the stakes for philosophical decisions. One of my points is that a natural argument that it might increase the stakes---by forcing us to lock in an answer to philosophical questions---doesn't seem to go through if you pursue this approach to AI control. There might be other arguments that building AI systems force us to lock in important philosophical views, but I am not familiar with those arguments.
I agree there may be other ways in which AI systems increase the stakes for philosophical decisions.
I like the bargaining example. I hadn't thought about bargaining as competitive advantage before, and instead had just been thinking about the possible upside (so that the cost of philosophical error was bounded by the damage of using a weaker bargaining scheme). I still don't feel like this is a big cost, but it's something I want to think about somewhat more.
If you think there are other examples like this that might help move my view. On my current model, these are just facts that increase my estimates for the importance of philosophical work, I don't really see it as relevant to AI control per se. (See the sibling, which is the better place to discuss that.)
I don't see cases where a philosophical error causes you to lose control, unless you would have some reason to cede control based on philosophical arguments (e.g. in the bargaining case). Failing that, it seems like there is a philosophically simple, apparently adequate notion of "remaining in control" and I would expect to remain in control at least in that sense.
Are these worse than the principal-agent problems that exist in any industrialized society? Most humans lack effective control over many important technologies, both in terms of economic productivity and especially military might. (They can't understand the design of a car they use, they can't understand the programs they use, they don't understand what is actually going on with their investments...) It seems like the situation is quite analogous.
Moreover, even if we could build AI in a different way, it doesn't seem to do anything to address the problem, since it is equally opaque to an end user who isn't involved in the AI development process. In any case, they are in some sense at the mercy of the AI developer. I guess this is probably the key point---I don't understand the qualitative difference between being at the mercy of the software developer on the one hand, and being at the mercy of the software developer + the engineers who help the software run day-to-day on the other. There is a slightly different set of issues for monitoring/law enforcement/compliance/etc., but it doesn't seem like a huge change.
(Probably the rest of this comment is irrelevant.)
To talk more concretely about mechanisms in a simple example, you might imagine a handful of companies who provide AI software. The people who use this software are essentially at the mercy of the software providers (since for all they know the software they are using will subvert their interests in arbitrary ways, whether or not there is a human involved in the process). In the most extreme case an AI provider could effectively steal all of their users' wealth. They would presumably then face legal consequences, which are not qualitatively changed by the development of AI if the AI control problem is solved. If anything we expect the legal system and government to better serve human interests.
We could talk about monitoring/enforcement/etc., but again I don't see these issues as interestingly different from the current set of issues, or as interestingly dependent on the nature of our AI control techniques. The most interesting change is probably the irrelevance of human labor, which I think is a very interesting issue economically/politically/legally/etc.
I agree with the general point that as technology improves a singleton becomes more likely. I'm agnostic on whether the control mechanisms I describe would be used by a singleton or by a bunch of actors, and as far as I can tell the character of the control problem is essentially the same in either case.
I do think that a singleton is likely eventually. From the perspective of human observers, a singleton will probably be established relatively shortly after wages fall below subsistence (at the latest). This prediction is mostly based on my expectation that political change will accelerate alongside technological change.
I wonder -- are you also relatively indifferent between a hard and slow takeoff, given sufficient time before the takeoff to develop ai control theory?
(One of the reasons a hard takeoff seems scarier to me is that it is more likely to lead to a singleton, with a higher probability of locking in bad values.)
As far as I can tell, Paul's current proposal might still suffer from blackmail, like his earlier proposal which I commented on. I vaguely remember discussing the problem with you as well.
One big lesson for me is that AI research seems to be more incremental and predictable than we thought, and garage FOOM probably isn't the main danger. It might be helpful to study the strengths and weaknesses of modern neural networks and get a feel for their generalization performance. Then we could try to predict which areas will see big gains from neural networks in the next few years, and which parts of Friendliness become easy or hard as a result. Is anyone at MIRI working on that?
If they did that, then what? Try to convince NN researchers to attack the parts of Friendliness that look hard? That seems difficult for MIRI to do given where they've invested in building their reputation (i.e., among decision theorists and mathematicians instead of in the ML community). (It would really depend on people trusting their experience and judgment since it's hard to see how much one could offer in the form of either mathematical proof or clearly relevant empirical evidence.) You'd have a better chance if the work was carried out by some other organization. But even if that organization got NN researchers to take its results seriously, what incentives do they have to attack parts of Friendliness that seem especially hard, instead of doing what they've been doing, i.e., racing as fast as they can for the next milestone in capability?
Or is the idea to bet on the off chance that building an FAI with NN turns out to be easy enough that MIRI and like-minded researchers can solve the associated Friendliness problems themselves and then hand the solutions to whoever ends up leading the AGI race, and they can just plug the solutions in at little cost to their winning the race?
Or you're suggesting aiming/hoping for some feasible combination of both, I guess. It seems pretty similar to what Paul Christiano is doing, except he has "generic AI technology" in place of "NN" above. To me, the chance of success of this approach seems low enough that it's not obviously superior to what MIRI is doing (namely, in my view, betting on the off chance that the contrarian AI approach they're taking ends up being much easier/better than the mainstream approach, which is looking increasingly unlikely but still not impossible).
That may be true but that is hindsight bias. MIRIs (or EYs for that matter) approach to hedge against that being true was nonetheless a very (and maybe given the knowledge at the time only) reasonable approach.
What problem do you have in mind here?
I thought that the previous problem was mostly psychological, i.e. that if humans were rational agents then this AI would be roughly as vulnerable to blackmail as its designers. So I thought the issue was the psychological strangeness (and great length) of the weird hypothetical.
Here we have no such hypothetical, and the system's behavior only depends on the predicted behavior of humans in the real world. That seems to address the narrow version of your concern.
I can see two analogous problems:
Did you have in mind 1, 2, or something else?
I mostly had in mind 2. Not sure how predicting humans is different from putting humans in hypotheticals. It seems like the same problems could happen.
I agree that the same problem appears for ALBA. I was originally working with proposals where the improbability of the human's situation was bounded, but the recursive structure can lead to arbitrarily large improbability. I hadn't thought about this explicitly.
Predicting humans is different from putting humans in hypotheticals, in the sense that in principle you can actually sample from the situations that cause humans to think they are in a simulation or whatever.
For example, suppose the human had access to a button that said "This is weird, I'm probably in a simulation," and suppose that we expected the human to press it in any case where they would start behaving weirdly. Then we could potentially sample from the subset of situations where the human presses the button. And if we manage to do that, then the human isn't right to suspect they are in a simulation (any more than they already should believe they are in a simulation, prior to even building the AI).
I agree that you shouldn't expect the current version of the scheme to cope with this problem. And it's certainly not obvious that the approach sketched above can work, there are many important questions. But I do think that there is a clear path to fixing this kind of problem.
(Interestingly, this is exactly analogous to what the active learning scheme has to do, in order to get the prediction algorithms to predict well.)
ETA: although I hadn't thought about this explicitly in the context of ALBA, I have expected to need some way to overweight "weird" situations in order to stop them from being problematic, ever since here.
Is MIRI even in the AGI race? It certainly doesn't look like it.
They're working on figuring out what we want the AGI to do, not building one. (I believe Nate has stated this in previous LW comments.)
Yes, and the point is that MIRI is pondering the situation at the finish line, but is not running in the race.
A different analogy would be that MIRI is looking at the map and the compass to figure out what's the right way to go, while others are just running in any random direction.
Not quite. The others are not running around in random directions, they are all running in a particular direction and MIRI is saying "Hold on, guys, there may be bears and tigers and pits of hell at your destination". Which is all fine, but it still is not running.
Still better than running into all the bears and tigers and getting eaten, particularly if it lets you figure out the correct route eventually.
The question was not what is better, the question was whether MIRI is competing in the AGI race.
Sure. I wasn't objecting to the "MIRI isn't competing in the AGI race" point, but to the negative connotations that one might read into your original analogy.
Which unfortunately presumes that an AGI would be tasked with doing something and given free reign to do so, a truly naïve and unlikely outcome.
How does it presume that?
Aka friendliness research. But why does that matter? If the machine has no real effectors and lots of human oversight, then why should there even be concern over friendliness? It wouldn't matter in that context. Tell a machine to do something, and it finds an evil-stupid way of doing it, and human intervention prevents any harm.
Why is it a going concern at all whether we can assure ahead of time that the actions recommended by a machine are human-friendly unless the machine is enabled to independently take those actions without human intervention? Just don't do that and it stops being a concern.
Humanity is having trouble coordinating and enforcing even global restrictions in greenhouse gasses. Try ensuring that nobody does anything risky or short-sighted with a technology that has no clearly-cut threshold between a "safe" and "dangerous" level of capability, and which can be beneficial for performing in pretty much any competitive and financially lucrative domain.
Restricting the AI's capabilities may work for a short while, assuming that only a small group of pioneers manages to develop the initial AIs and they're responsible with their use of the technology - but as Bruce Schneier says, today's top-secret programs become tomorrow's PhD theses and the next day's common applications. If we want to survive in the long term, we need to figure out how to make the free-acting AIs safe, too - otherwise it's just a ticking time bomb before the first guys accidentally or intentionally release theirs.
Humanity has done more than zero and less that optimality about things like climate change. Importantly, the situation isbelow the immanent existential threat level.
If you are going to complain that alternative proposals face coordination problems, you need to show that yours dont, or you are committing the fallacy of the dangling comparision. If people aren't going to refrain from building dangerously powerful superintellugences, assuming is possible, why would they have the sense to fit MIRIs safety features, assuming they are possible? If the law can make people fit safety features, why cant it prevent them building dangerous AIs ITFP?
I would suggest a combination of generality and agency. And what problem domain requires both?
If you allow for autonomously acting AIs, then you could have Friendly autonomous AIs tracking down and stopping Unfriendly / unauthorized AIs.
This of course depends on people developing the Friendly AIs first, but ideally it'd be enough for only the first people to get the design right, rather than depending on everyone being responsible.
It's unclear whether AI risk will become obviously imminent, either. Goertzel & Pitt 2012 argue in section 3 of their paper that this is unlikely.
Business (which by nature covers just about every domain in which you can make a profit, which is to say just about every domain relevant for human lives), warfare, military intelligence, governance... (see also my response to Mark)
I think you very much misunderstand my suggestion. I'm saying that there is no reason to presume AI will be given the keys to the kingdom from day one, not advocating for some sort of regulatory regime.
So what do you see as the mechanism that will prevent anyone from handing the AI those keys, given the tremendous economic pressure towards doing exactly that?
As we discussed in Responses to AGI Risk:
I suspect that this dates back to a time when MIRI believed the answer to AI safety was to both build an agentive, maximal supeintelligence and align its values with ours, and put it in charge of all the other AIs.
The first idea has been effectively shelved, since MIRI had produced about zero lines of code,..but the idea that AI safety is value alignment continues with considerable momentum. And value alignment only makes sense if you are building an agentive AI (and have given up on corrigibility).
Briefly skimming Christiano's post, this is actually one of the few/first proposals from someone MIRI related that actually seems to be on the right track (and similar to my own loose plans). Basically it just boils down to learning human utility functions with layers of meta-learning, with generalized RL and IRL.