LessWrong 2.0 Reader

anything that outputs decisions implies a utility function

I think this is only true in a boring sense and isn't true in more natural senses. For example, in an MDP, it's not true that every policy maximises a non-constant utility function over states.

Yes, virtue ethics implies a utility function, because anything that outputs decisions implies a utility function. In this case, I'm noting that for virtue ethics, the derivative of that utility with respect to intelligence is positive. 

Any chance we could get Ghibli Mode back? I miss my little blue monster :(

My response was about your original PS, which was about this, not taboos.

I think the arguments you made there, and here, are confused, mixing up unrelated claims. The idea that some tasks will necessarily remain harder for AI than humans in the future is simply hopium.

Can you please send the new fooming shoggoth album to spotify, I was really enjoying that music! 

edit: Ah I see this question has been answered, but I like to note that I'm impressed by the ai music and I'm going to look into making some myself. Perhaps songs about cognitive bias's could be a good way to learn them deep enough in your brain that you can avoid them in non-theroetic situations. 

Great post. I think some of your frames add a lot of clarity and I really appreciated the diagrams.

One subset of AI for AI safety that I believe to be underrated is wise AI advisors [LW · GW]^[1]. Some of the areas you've listed (coordination, helping with communication, improving epistemics) intersect with this, but I don't believe that this exhausts the wisdom frame.

You write: "If efforts to expand the safety range can’t benefit from this kind of labor in a comparable way... then absent large amounts of sustained capability restraint, it seems likely that we’ll quickly end up with AI systems too capable for us to control".

I agree. In fact, a key reason why I think this is important is that we can't afford to leave anything on the table.

One of the things I like about the approach of training AI advisors is that humans can compensate for weaknesses in the AI system. In other words, I'm introducing a third category of labour human-AI cybernetic systems/centaur labour. I think that it's likely that this might widen the sweet spot, however, we have to make sure that we do this in a way that differentially benefits safety.

1. ^{^}

   More broadly, I think there's merit to the cyborgism approach [LW · GW] even if some of the arguments is less compelling in light of recent capabilities advances.

Scott's own reaction to / improvement upon Graham's hierarchy of disagreement (which I just noticed you commented on back in the day, so I guess this is more for others' curiosity) is 

Graham’s hierarchy is useful for its intended purpose, but it isn’t really a hierarchy of disagreements. It’s a hierarchy of types of response, within a disagreement. Sometimes things are refutations of other people’s points, but the points should never have been made at all, and refuting them doesn’t help. Sometimes it’s unclear how the argument even connects to the sorts of things that in principle could be proven or refuted.

If we were to classify disagreements themselves – talk about what people are doing when they’re even having an argument – I think it would look something like this:

 

 

Most people are either meta-debating – debating whether some parties in the debate are violating norms – or they’re just shaming, trying to push one side of the debate outside the bounds of respectability.

If you can get past that level, you end up discussing facts (blue column on the left) and/or philosophizing about how the argument has to fit together before one side is “right” or “wrong” (red column on the right). Either of these can be anywhere from throwing out a one-line claim and adding “Checkmate, atheists” at the end of it, to cooperating with the other person to try to figure out exactly what considerations are relevant and which sources best resolve them.

If you can get past that level, you run into really high-level disagreements about overall moral systems, or which goods are more valuable than others, or what “freedom” means, or stuff like that. These are basically unresolvable with anything less than a lifetime of philosophical work, but they usually allow mutual understanding and respect.

Seems like yours and Scott's are complementary: I read you as suggesting how to improve one's own argumentation techniques, while Scott is being more sociologically descriptive, mainly in explaining why online discourse so often degenerates into social shaming and meta-debate.

In [Intro to brain-like-AGI safety] 10. The alignment problem [LW · GW] and elsewhere, I’ve been using “outer alignment” and “inner alignment” in a model-based actor-critic RL context to refer to:

“Outer alignment” entails having a ground-truth reward function that spits out rewards that agree with what we want. “Inner alignment” is having a learned value function that estimates the value of a plan in a way that agrees with its eventual reward.

For some reason it took me until now to notice that:

• my “outer misalignment” is more-or-less synonymous with “specification gaming”,
• my “inner misalignment” is more-or-less synonymous with “goal misgeneralization”.

(I’ve been regularly using all four terms for years … I just hadn’t explicitly considered how they related to each other, I guess!)

I updated that post to note the correspondence, but also wanted to signal-boost this, in case other people missed it too.

~~

[You can stop reading here—the rest is less important]

If everybody agrees with that part, there’s a further question of “…now what?”. What terminology should I use going forward? If we have redundant terminology, should we try to settle on one?

One obvious option is that I could just stop using the terms “inner alignment” and “outer alignment” in the actor-critic RL context as above. I could even go back and edit them out of that post, in favor of “specification gaming” and “goal misgeneralization”. Or I could leave it. Or I could even advocate that other people switch in the opposite direction!

One consideration is: Pretty much everyone using the terms “inner alignment” and “outer alignment” are not using them in quite the way I am—I’m using them in the actor-critic model-based RL context, they’re almost always using them in the model-free policy optimization context (e.g. evolution) (see §10.2.2 [LW · GW]). So that’s a cause for confusion, and point in favor of my dropping those terms. On the other hand, I think people using the term “goal misgeneralization” are also almost always using them in a model-free policy optimization context. So actually, maybe that’s a wash? Either way, my usage is not a perfect match to how other people are using the terms, just pretty close in spirit. I’m usually the only one on Earth talking explicitly about actor-critic model-based RL AGI safety, so I kinda have no choice but to stretch existing terms sometimes.

Hmm, aesthetically, I think I prefer the “outer alignment” and “inner alignment” terminology that I’ve traditionally used. I think it’s a better mental picture. But in the context of current broader usage in the field … I’m not sure what’s best.

(Nate Soares dislikes the term “misgeneralization” [LW · GW], on the grounds that “misgeneralization” has a misleading connotation that “the AI is making a mistake by its own lights”, rather than “something is bad by the lights of the programmer”. I’ve noticed a few people trying to get the variation “goal malgeneralization” to catch on instead. That does seem like an improvement, maybe I'll start doing that too.)

I unironically love Table 2. 

A shower thought I once had, intuition-pumped by MIRI's / Luke's old post [LW · GW] on turning philosophy to math to engineering, was that if metaethicists really were serious about resolving their disputes they should contract a software engineer (or something) to help implement on GitHub a metaethics version of Table 2, where rows would be moral dilemmas like the trolley problem and columns ethical theories, and then accept that real-world engineering solutions tend to be "dirty" and inelegant remixes plus kludgy optimisations to handle edge cases, but would clarify what the SOTA was and guide "metaethical innovation" much better, like a qualitative multi-criteria version of AI benchmarks.  

I gave up on this shower thought for various reasons, including that I was obviously naive and hadn't really engaged with the metaethical literature in any depth, but also because I ended up thinking that disagreements on doing good might run ~irreconcilably deep, plus noticing that Rethink Priorities had done the sophisticated v1 of a subset of what I had in mind and nobody really cared enough to change what they did. (In my more pessimistic moments I'd also invoke the diseased discipline [LW · GW] accusation, but that may be unfair and outdated.)

The idea is interesting, but I'm somewhat skeptical that it'll pan out.

• RG doesn't help much going backwards - the same coarse-grained laws might correspond to many different micro-scale laws, especially when you don't expect the micro scale to be simple.
• Singular learning theory provides a nice picture of phase-transition-like-phenomena, but it's likely that large neural networks undergo lots and lots of phase transitions, and that there's not just going to be one phase transition from "naive" to "naughty" that we can model simply.
• Conversely, lots of important changes might not show up as phase transitions.
• Some AI architectures are basically designed to be hard to analyze with RG because they want to mix information from a wide variety of scales together. Full attention layers might be such an example.