If the Thought Assessors converge to 100% accuracy in predicting the reward that will result from a plan, then a plan to wirehead (hack into the Steering Subsystem and set reward to infinity) would seem very appealing, and the agent would do it.
If the Thought Assessors don’t converge to 100% accuracy in predicting the reward that will result from a plan, then that’s the very definition of inner misalignment!
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The thought “I will secretly hack into my own Steering Subsystem” is almost certainly not aligned with the designer’s intention. So a credit-assignment update that assigns more positive valence to “I will secretly hack into my own Steering Subsystem” is a bad update. We don’t want it. Does it increase “inner alignment”? I think we have to say “yes it does”, because it leads to better reward predictions! But I don’t care. I still don’t want it. It’s bad bad bad. We need to figure out how to prevent that particular credit-assignment Thought Assessor update from happening.
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I think there’s a broader lesson here. I think “outer alignment versus inner alignment” is an excellent starting point for thinking about the alignment problem. But that doesn’t mean we should expect one solution to outer alignment, and a different unrelated solution to inner alignment. Some things—particularly interpretability—cut through both outer and inner layers, creating a direct bridge from the designer’s intentions to the AGI’s goals. We should be eagerly searching for things like that.
Yeah, there definitely seems to be something off about that categorization. I’ve thought a bit about how this stuff works in humans, particularly in this post of my moral anti-realism sequence. To give some quotes from that:
One of many takeaways I got from reading Kaj Sotala’s multi-agent models of mind sequence (as well as comments by him) is that we can model people as pursuers of deep-seated needs. In particular, we have subsystems (or “subagents”) in our minds devoted to various needs-meeting strategies. The subsystems contribute behavioral strategies and responses to help maneuver us toward states where our brain predicts our needs will be satisfied. We can view many of our beliefs, emotional reactions, and even our self-concept/identity as part of this set of strategies. Like life plans, life goals are “merely” components of people’s needs-meeting machinery.[8]
Still, as far as components of needs-meeting machinery go, life goals are pretty unusual. Having life goals means to care about an objective enough to (do one’s best to) disentangle success on it from the reasons we adopted said objective in the first place. The objective takes on a life of its own, and the two aims (meeting one’s needs vs. progressing toward the objective) come apart. Having a life goal means having a particular kind of mental organization so that “we” – particularly the rational, planning parts of our brain – come to identify with the goal more so than with our human needs.[9]
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There’s a normative component to something as mundane as choosing leisure activities. [E.g., going skiing in the cold, or spending the weekend cozily at home.] In the weekend example, I’m not just trying to assess the answer to empirical questions like “Which activity would contain fewer seconds of suffering/happiness” or “Which activity would provide me with lasting happy memories.” I probably already know the answer to those questions. What’s difficult about deciding is that some of my internal motivations conflict. For example, is it more important to be comfortable, or do I want to lead an active life? When I make up my mind in these dilemma situations, I tend to reframe my options until the decision seems straightforward. I know I’ve found the right decision when there’s no lingering fear that the currently-favored option wouldn’t be mine, no fear that I’m caving to social pressures or acting (too much) out of akrasia, impulsivity or some other perceived weakness of character.[21]
We tend to have a lot of freedom in how we frame our decision options. We use this freedom, this reframing capacity, to become comfortable with the choices we are about to make. In case skiing wins out, then “warm and cozy” becomes “lazy and boring,” and “cold and tired” becomes “an opportunity to train resilience / apply Stoicism.” This reframing ability is a double-edged sword: it enables rationalizing, but it also allows us to stick to our beliefs and values when we’re facing temptations and other difficulties.
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Visualizing the future with one life goal vs. another
Whether a given motivational pull – such as the need for adventure, or (e.g.,) the desire to have children – is a bias or a fundamental value is not set in stone; it depends on our other motivational pulls and the overarching self-concept we’ve formed.
Lastly, we also use “planning mode” to choose between life goals. A life goal is a part of our identity – just like one’s career or lifestyle (but it’s even more serious).
We can frame choosing between life goals as choosing between “My future with life goal A” and “My future with life goal B” (or “My future without a life goal”). (Note how this is relevantly similar to “My future on career path A” and “My future on career path B.”)
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It’s important to note that choosing a life goal doesn’t necessarily mean that we predict ourselves to have the highest life satisfaction (let alone the most increased moment-to-moment well-being) with that life goal in the future. Instead, it means that we feel the most satisfied about the particular decision (to adopt the life goal) in the present, when we commit to the given plan, thinking about our future. Life goals inspired by moral considerations (e.g., altruism inspired by Peter Singer’s drowning child argument) are appealing despite their demandingness – they can provide a sense of purpose and responsibility.
So, it seems like we don’t want “perfect inner alignment,” at least not if inner alignment is about accurately predicting reward and then forming the plan of doing what gives you most reward. Also, there’s a concept of “lock in” or “identifying more with the long-term planning part of your brain than with the underlying needs-meeting machinery.” Lock in can be dangerous (if you lock in something that isn’t automatically corrigible), but it might also be dangerous not to lock in anything (because this means you don’t know what other goals form later on).
Idk, the whole thing seems to me like brewing a potion in Harry Potter, except that you don’t have a recipe book and there’s luck involved, too. “Outer alignment,” a minimally sufficient degree thereof (as in: the agent tends to gets rewards when it takes actions towards the intended goal), increases the likelihood that you get broadly pointed you in the right direction, so the intended goal maybe gets considered among things the internal planner considers reinforcing itself around / orienting itself towards. But then, whether the intended gets picked over other alternatives (instrumental requirements for general intelligence, or alien motivations the AI might initially have), who knows. Like with raising a child, sometimes they turn out the way the parents intend, sometimes not at all. There’s probably a science to finding out how outcomes become more likely, but even if we could do that with human children developing into adults with fixed identities, there’s then still the question of how to find analogous patterns in (brain-like) AI. Tough job.
I read that sequence a couple months ago (in preparation for writing §2.7 here), and found it helpful, thanks.
To give some quotes from that…
I agree that we’re probably on basically the same page.
So, it seems like we don’t want “perfect inner alignment,”
FYI Alex also has this post making a similar point.
Idk, the whole thing seems to me like brewing a potion in Harry Potter
I think I agree, in that I’m somewhat pessimistic about plans wherein we want the “adult AI” to have object-level goal X, and so we find a reward function and training environment where that winds up happening.
Not that such a plan would definitely fail (e.g. lots of human adults are trying to take care of their children), just that it doesn’t seem like the kind of approach that passes the higher bar of having a strong reason to expect success (e.g. lots of human adults are not trying to take care of their children). (See here for someone trying to flesh out this kind of approach.)
So anyway, my take right now is basically:
If we want the “adult AGI” to be trying to do a particular thing (‘make nanobots’, or ‘be helpful towards its supervisor’, or whatever), we should replace (or at least supplement) a well-chosen reward function with a more interpretability-based approach; for example, see Plan for mediocre alignment of brain-like [model-based RL] AGI (which is a simplified version of Post 14 of this series)
Or we can have a similar relation to AGIs that we have to the next generation of humans: We don’t know exactly at the object level what they will be trying to do and why, but they basically have “good hearts” and so we trust their judgment.
These two bullet points correspond to the “two paths forward” of Post 12 of this series.
Yeah, there definitely seems to be something off about that categorization. I’ve thought a bit about how this stuff works in humans, particularly in this post of my moral anti-realism sequence. To give some quotes from that:
So, it seems like we don’t want “perfect inner alignment,” at least not if inner alignment is about accurately predicting reward and then forming the plan of doing what gives you most reward. Also, there’s a concept of “lock in” or “identifying more with the long-term planning part of your brain than with the underlying needs-meeting machinery.” Lock in can be dangerous (if you lock in something that isn’t automatically corrigible), but it might also be dangerous not to lock in anything (because this means you don’t know what other goals form later on).
Idk, the whole thing seems to me like brewing a potion in Harry Potter, except that you don’t have a recipe book and there’s luck involved, too. “Outer alignment,” a minimally sufficient degree thereof (as in: the agent tends to gets rewards when it takes actions towards the intended goal), increases the likelihood that you get broadly pointed you in the right direction, so the intended goal maybe gets considered among things the internal planner considers reinforcing itself around / orienting itself towards. But then, whether the intended gets picked over other alternatives (instrumental requirements for general intelligence, or alien motivations the AI might initially have), who knows. Like with raising a child, sometimes they turn out the way the parents intend, sometimes not at all. There’s probably a science to finding out how outcomes become more likely, but even if we could do that with human children developing into adults with fixed identities, there’s then still the question of how to find analogous patterns in (brain-like) AI. Tough job.
I read that sequence a couple months ago (in preparation for writing §2.7 here), and found it helpful, thanks.
I agree that we’re probably on basically the same page.
FYI Alex also has this post making a similar point.
I think I agree, in that I’m somewhat pessimistic about plans wherein we want the “adult AI” to have object-level goal X, and so we find a reward function and training environment where that winds up happening.
Not that such a plan would definitely fail (e.g. lots of human adults are trying to take care of their children), just that it doesn’t seem like the kind of approach that passes the higher bar of having a strong reason to expect success (e.g. lots of human adults are not trying to take care of their children). (See here for someone trying to flesh out this kind of approach.)
So anyway, my take right now is basically:
If we want the “adult AGI” to be trying to do a particular thing (‘make nanobots’, or ‘be helpful towards its supervisor’, or whatever), we should replace (or at least supplement) a well-chosen reward function with a more interpretability-based approach; for example, see Plan for mediocre alignment of brain-like [model-based RL] AGI (which is a simplified version of Post 14 of this series)
Or we can have a similar relation to AGIs that we have to the next generation of humans: We don’t know exactly at the object level what they will be trying to do and why, but they basically have “good hearts” and so we trust their judgment.
These two bullet points correspond to the “two paths forward” of Post 12 of this series.