All of Stuart_Armstrong's Comments + Replies

Not at all what I'm angling at. There's a mechanistic generator for why humans navigate ontology shifts well (on my view). Learn about the generators, don't copy the algorithm.

I agree that humans navigate "model splinterings" quite well. But I actually think the algorithm might be more important than the generators. The generators comes from evolution and human experience in our actual world; this doesn't seem like it would generalise. The algorithm itself, though, may very generalisable (potential analogy: humans have instinctive grasp of all numbers u... (read more)

2Alex Turner4mo
Yes and no. I think most of our disagreements are probably like "what is instinctual?" and "what is the type signature of human values?" etc. And not on "should we understand what people are doing?". By "generators", I mean "the principles by which the algorithm operates", which means the generators are found by studying the within-lifetime human learning process. Dubious to me due to information inaccessibility [] & random initialization of neocortex (which is a thing I am reasonably confident in). I think it's more likely that our architecture&compute&learning process makes it convergent to learn this quick <= 5 number-sense.

Do you predict that if I had access to a range of pills which changed my values to whatever I wanted, and I could somehow understand the consequences of each pill (the paperclip pill, the yay-killing pill, ...), I would choose a pill such that my new values would be almost completely unaligned with my old values?

This is the wrong angle, I feel (though it's the angle I introduced, so apologies!). The following should better articulate my thoughts:

We have an AI-CEO money maximiser, rewarded by the stock price ticker as a reward function. As long as the AI... (read more)

3Alex Turner4mo
Hm, thanks for the additional comment, but I mostly think we are using words and frames differently, and disagree with my understanding of what you think values are. Reward is not the optimization target [] . I think this is not what happened. Those desires are likely downstream of past reinforcement of different kinds; I do not think there is a "wireheading" mechanism here. Wireheading is a very specific kind of antecedent-computation-reinforcement chasing behavior, on my ontology. Not at all what I'm angling at. There's a mechanistic generator for why humans navigate ontology shifts well (on my view). Learn about the generators, don't copy the algorithm.

It is not that human values are particularly stable. It's that humans themselves are pretty limited. Within that context, we identify the stable parts of ourselves as "our human values".

If we lift that stability - if we allow humans arbitrary self-modification and intelligence increase - the parts of us that are stable will change, and will likely not include much of our current values. New entities, new attractors.

2Alex Turner4mo
I might agree or disagree with this statement, depending on what "particularly stable" means. (Also, is there a portion of my post which seems to hinge on "stability"?) I don't see why you think this. Do you predict that if I had access to a range of pills which changed my values to whatever I wanted, and I could somehow understand the consequences of each pill (the paperclip pill, the yay-killing pill, ...), I would choose a pill such that my new values would be almost completely unaligned with my old values?

Hey, thanks for posting this!

And I apologise - I seem to have again failed to communicate what we're doing here :-(

"Get the AI to ask for labels on ambiguous data"

Having the AI ask is a minor aspect of our current methods, that I've repeatedly tried to de-emphasise (though it does turn it to have an unexpected connection with interpretability). What we're trying to do is:

  1. Get the AI to generate candidate extrapolations of its reward data, that include human-survivable candidates.
  2. Select among these candidates to get a human-survivable ultimate reward
... (read more)

The aim of this post is not to catch out GPT-3; it's to see what concept extrapolation could look like for a language model.

3Daniel Kokotajlo7mo
OK, cool. I think I was confused.

To see this, imagine the AUP agent builds a subagent to make for all future , in order to neutralize the penalty term. This means it can't make the penalty vanish without destroying its ability to better optimize its primary reward, as the (potentially catastrophically) powerful subagent makes sure the penalty term stays neutralized.

I believe this is incorrect. The and are the actions of the AUP agent. The subagent just needs to cripple the AUP agent so that all actions are equivalent, then go about maximising to the upmost.

Hey there! Sorry for the delay. $50 awarded to you for fastest good reference. PM me your bank details.

I'm not sure why you picked .

Because it's the first case I thought of where the probability numbers work out, and I just needed one example to round off the post :-)

It's worth you write up your point and post it - that tends to clarify the issue, for yourself as well as for others.

I've posted on the theoretical difficulties of aggregating the utilities of different agents. But doing it in practice is much more feasible (scale the utilities to some not-too-unreasonable scale, add them, maximise sum).

But value extrapolation is different from human value aggregation; for example, low power (or low impact) AIs can be defined with value extrapolation, and that doesn't need human value aggregation.

4David Manheim9mo
I'm skeptical that many of the problems with aggregation don't both apply to actual individual human values once extrapolated, and generalize to AIs with closely related values, but I'd need to lay out the case for that more clearly. (I did discuss the difficulty of cooperation even given compatible goals a bit in this paper [] , but it's nowhere near complete in addressing this issue.)

Yes, those are important to provide, and we will.

I do not put too much weight on that intuition, except as an avenue to investigate (how do humans do it, exactly? If it depends on the social environment, can the conditions of that be replicated?).

We're aiming to solve the problem in a way that is acceptable to one given human, and then generalise from that.

3David Manheim9mo
This seems fragile in ways that make me less optimistic about the approach overall. We have strong reasons to think that value aggregation is intractable, and (by analogy,) in some ways the problem of coherence in CEV is the tricky part. That is, the problem of making sure that we're not Dutch book-able is, IIRC, NP-complete, and even worse, the problem of aggregating preferences has several impossibility results. Edit: To clarify, I'm excited about the approach overall, and think it's likely to be valuable, but this part seems like a big problem.

CEV is based on extrapolating the person; the values are what the person would have had, had they been smarter, known more, had more self-control, etc... Once you have defined the idealised person, the values emerge as a consequence. I've criticised this idea in the past, mainly because the process to generate the idealised person seems vulnerable to negative attractors (Eliezer's most recent version of CEV has less of this problem).

Value extrapolation and model splintering are based on extrapolating features and concepts in models, to other models. This c... (read more)

UK based currently, Rebecca Gorman other co-founder.

Firstly, because the problem feels central to AI alignment, in the way that other approaches didn't. So making progress in this is making general AI alignment progress; there won't be such a "one error detected and all the work is useless" problem. Secondly, we've had success generating some key concepts, implying the problem is ripe for further progress.

It's an interesting question as to whether aAlice is actually overconfident. Her predictions about human behaviour may be spot on, at this point - much better than human predictions about ourselves. So her confidence depends on whether she has the right kind of philosophical uncertainty.

I actually don't think that Alice could help a (sufficiently alien) alien. She needs an alien theory of mind to understand what the alien wants, how they would extrapolate, how to help that extrapolation without manipulating it, and so on. Without that, she's just projecting human assumptions in alien behaviour and statements.

2Rohin Shah1y
Absolutely, I would think that the first order of business would be to learn that alien theory of mind (and be very conservative until that's done). Maybe you're saying that this alien theory of mind is unlearnable, even for a very intelligent Alice? That seems pretty surprising, and I don't feel the force of that intuition (despite the Occam's razor impossibility result).

Yes, but we would be mostly indifferent to shifts in the distribution that preserve most of the features - eg if the weather was the same but delayed or advanced by six days.

I have some draft posts explaining some of this stuff better, I can share them privately, or hang on another month or two. :)

I'd like to see them. I'll wait for the final (posted) versions, I think.

Because our preferences are inconsistent, and if an AI says "your true preferences are ", we're likely to react by saying "no! No machine will tell me what my preferences are. My true preferences are , which are different in subtle ways".

1Evan R. Murphy1y
So the subtle manipulation is to compensate for those rebellious impulses making UHunstable? Why not just let the human have those moments and alter theirUHif that's what they think they want? Over time, then they may learn that being capricious with their AI doesn't ultimately serve them very well. But if they find out the AI is trying to manipulate them, that could make them want to rebel even more and have less trust for the AI.

Thanks for developing the argument. This is very useful.

The key point seems to be whether we can develop an AI that can successfully behave as a low impact AI - not as a "on balance, things are ok", but a genuinely low impact AI that ensure that we don't move towards a world where our preference might be ambiguous or underdefined.

But consider the following scenario: the AGI knows that, as a consequence of its actions, one AGI design will be deployed rather than another. Both of these designs will push the world into uncharted territory. How should it deal with that situation?

2Steve Byrnes1y
Hmm, 1. I want the AI to have criteria that qualifies actions as acceptable, e.g. "it pattern-matches less than 1% to 'I'm causing destruction', and it pattern-matches less than 1% to 'the supervisor wouldn't like this', and it pattern-matches less than 1% to 'I'm changing my own motivation and control systems', and … etc. etc." 2. If no action is acceptable, I want NOOP to be hardcoded as an always-acceptable default—a.k.a. "being paralyzed by indecision" in the face of a situation where all the options seem problematic. And then we humans are responsible for not putting the AI in situations where fast decisions are necessary and inaction is dangerous, like running the electric grid or driving a car. (At some point we do want an AI that can run the electric grid and drive a car etc. But maybe we can bootstrap our way there, and/or use less-powerful narrow AIs in the meantime.) 3. A failure mode of (2) is that we could get an AI that is paralyzed by indecision always, and never does anything. To avoid this failure mode, we want the AI to be able to (and motivated to) gather evidence that might show that a course of action deemed problematic is in fact acceptable after all. This would probably involve asking questions to the human supervisor. 4. A failure mode of (3) is that the AI frames the questions in order to get an answer that it wants. To avoid this failure mode, we would set things up such that the AI's normal motivation system is not in charge of choosing what words to say when querying the human [] . For example, maybe the AI is not really "asking a question" at all, at least not in the normal sense; instead it's sending a data-dump to the human, and the human then inspects this data-dump with interpretability tools, and makes an edit to the AI's motivation parameter

The successor problem is important, but it assumes we have the values already.

I'm imagining algorithms designing successors with imperfect values (that they know to be imperfect). It's a somewhat different problem (though solving the classical successor problem is also important).

I agree there are superintelligent unconstrained AIs that can accomplish tasks (making a cup of tea) without destroying the world. But I feel it would have to have so much of human preferences already (to compute what is and what isn't an acceptable tradeoff in making you your cup of tea) that it may as well be fully aligned anyway - very little remains to define full alignment.

Ah, so you are arguing against (3)? (And what's your stance on (1)?)

Let's say you are assigned to be Alice's personal assistant.

  • Suppose Alice says "Try to help me as much as you can, while being VERY sure to avoid actions that I would regard as catastrophically bad. When in doubt, just don't do anything at all, that's always OK with me." I feel like Alice is not asking too much of you here. You'll observe her a lot, and ask her a lot of questions especially early on, and sometimes you'll fail to be useful, because helping her would require choosing among o
... (read more)

Those are very relevant to this project, thanks. I want to see how far we can push these approaches; maybe some people you know would like to take part?

3Rohin Shah1y
Hmm, you might want to reach out to CHAI folks, though I don't have a specific person in mind at the moment. (I myself am working on different things now.)

Vertigo, lust, pain reactions, some fear responses, and so on, don't involve a model. Some versions of "learning that it's cold outside" don't involve a model, just looking out and shivering; the model aspect comes in when you start reasoning about what to do about it. People often drive to work without consciously modelling anything on the way.

Think model-based learning versus Q-learning. Anything that's more Q-learning is not model based.

I think the question of whether any particular plastic synapse is or is not part of the information content of the model will have a straightforward yes-or-no answer.

I don't think it has an easy yes or no answer (at least without some thought as to what constitutes a model within the mess of human reasoning) and I'm sure that even if it does, it's not straightforward.

since we probably won't have those kinds of real-time-brain-scanning technologies, right?

One hope would be that, by the time we have those technologies, we'd know what to look for.

1Steve Byrnes1y
I was writing a kinda long reply but maybe I should first clarify: what do you mean by "model"? Can you give examples of ways that I could learn something (or otherwise change my synapses within a lifetime) that you wouldn't characterize as "changes to my mental model"? For example, which of the following would be "changes to my mental model"? 1. I learn that Brussels is the capital of Belgium 2. I learn that it's cold outside right now 3. I taste a new brand of soup and find that I really like it 4. I learn to ride a bicycle, including 1. maintaining balance via fast hard-to-describe responses where I shift my body in certain ways in response to different sensations and perceptions 2. being able to predict how the bicycle and me would move if I swung my arm around 5. I didn't sleep well so now I'm grumpy FWIW my inclination is to say that 1-4 are all "changes to my mental model". And 5 involves both changes to my mental model (knowing that I'm grumpy), and changes to the inputs to my mental model (I feel different "feelings" than I otherwise would—I think of those as inputs going into the model, just like visual inputs go into the model). Is there anything wrong / missing / suboptimal about that definition?

I have only very limited access to GPT-3; it would be interesting if others played around with my instructions, making them easier for humans to follow, while still checking that GPT-3 failed.

More SIAish for conventional anthropic problems. Other theories are more applicable for more specific situations, specific questions, and for duplicate issues.

Cheers, these are useful classifications.

The idea that maximising the proxy will inevitably end up reducing the true utility seems a strong implicit part of Goodharting the way it's used in practice.

After all, if the deviation is upwards, Goodharting is far less of a problem. It's "suboptimal improvement" rather than "inevitable disaster".

3G Gordon Worley III1y
Ah, yeah, that's true, there's not much concern about getting too much of a good thing and that actually being good, which does seem like a reasonable category for anti-Goodharting. It's a bit hard to think when this would actually happen, though, since usually you have to give something up, even if it's just the opportunity to have done less. For example, maybe I'm trying to get a B on a test because that will let me pass the class and graduate, but I accidentally get an A. The A is actually better and I don't mind getting it, but then I'm potentially left with regret that I put in too much effort. Most examples I can think of that look like potential anti-Goodharting seem the same: I don't mind that I overshot the target, but I do mind that I wasn't as efficient as I could have been.

I want a formalism capable of modelling and imitating how humans handle these situations, and we don't usually have dynamic consistency (nor do boundedly rational agents).

Now, I don't want to weaken requirements "just because", but it may be that dynamic consistency is too strong a requirement to properly model what's going on. It's also useful to have AIs model human changes of morality, to figure out what humans count as values, so getting closer to human reasoning would be necessary.

1Vanessa Kosoy2y
Boundedly rational agents definitely can have dynamic consistency, I guess it depends on just how bounded you want them to be. IIUC what you're looking for is a model that can formalize "approximately rational but doesn't necessary satisfy any crisp desideratum". In this case, I would use something like my quantitative AIT definition of intelligence [] .

Hum... how about seeing enforcement of dynamic consistency as having a complexity/computation cost, and Dutch books (by other agents or by the environment) providing incentives to pay the cost? And hence the absence of these Dutch books meaning there is little incentive to pay that cost?

Desideratum 1: There should be a sensible notion of what it means to update a set of environments or a set of distributions, which should also give us dynamic consistency.

I'm not sure how important dynamic consistency should be. When I talk about model splintering, I'm thinking of a bounded agent making fundamental changes to their model (though possibly gradually), a process that is essentially irreversible and contingent the circumstance of discovering new scenarios. The strongest arguments for dynamic consistency are the Dutch-book type arguments, wh... (read more)

1Vanessa Kosoy2y
I'm not sure why would we need a weaker requirement if the formalism already satisfies a stronger requirement? Certainly when designing concrete learning algorithms we might want to use some kind of simplified update rule, but I expect that to be contingent on the type of algorithm and design constraints. We do have some speculations in that vein, for example I suspect that, for communicating infra-MDPs, an update rule that forgets everything except the current state would only lose something like O(1−γ) expected utility.
I don't know, we're hunting for it, relaxations of dynamic consistency would be extremely interesting if found, and I'll let you know if we turn up with anything nifty.

For real humans, I think this is a more gradual process - they learn and use some distinctions, and forget others, until their mental models are quite different a few years down the line.

The splintering can happen when a single feature splinters; it doesn't have to be dramatic.

Thanks. I think we mainly agree here.

Look at the paper linked for more details ( ).

Basically "humans are always fully rational and always take the action they want to" is a full explanation of all of human behaviour, that is strictly simpler than any explanation which includes human biases and bounded rationality.

But if you are expecting a 100% guarantee that the uncertainty metrics will detect every possible bad situation

I'm more thinking of how we could automate the navigating of these situations. The detection will be part of this process, and it's not a Boolean yes/no, but a matter of degree.

I agree that once you have landed in the bad situation, mitigation options might be much the same, e.g. switch off the agent.

I'm most interested in mitigation options the agent can take itself, when it suspects it's out-of-distribution (and without being turned off, ideally).

1Koen Holtman2y
OK. Reading the post originally, my impression was that you were trying to model ontological crisis problems that might happen by themselves inside the ML system when it learns of self-improves. This is a subcase that can be expressed in by your model, but after the Q&A in your SSC talk yesterday, my feeling is that your main point of interest and reason for optimisim with this work is different. It is in the problem of the agent handling ontological shifts that happen in human models of what their goals and values are. I might phrase this question as: If the humans start to splinter their idea of what a certain kind morality-related word they have been using for ages really means, how is the agent supposed to find out about this, and what should it do next to remain aligned? The ML literature is full of uncertainty metrics that might be used to measure such splits (this paper [] comes to mind as a memorable lava-based example). It is also full of proposals for mitigation like 'ask the supervisor' or 'slow down' or 'avoid going into that part of the state space'. The general feeling I have, which I think is also the feeling in the ML community, is that such uncertainty metrics are great for suppressing all kinds of failure scenarios. But if you are expecting a 100% guarantee that the uncertainty metrics will detect every possible bad situation (that the agent will see every unknown unknown coming before it can hurt you), you will be disappointed. So I'd like to ask you: what is your sense of optimism or pessimism in this area?

Thanks! Lots of useful insights in there.

So I might classify moving out-of-distribution as something that happens to a classifier or agent, and model splintering as something that the machine learning system does to itself.

Why do you think it's important to distinguish these two situations? It seems that the insights for dealing with one situation may apply to the other, and vice versa.

3Koen Holtman2y
The distinction is important if you want to design countermeasures that lower the probability that you land in the bad situation in the first place. For the first case, you might look at improving the agent's environment, or in making the agent detect when its environment moves off the training distribution. For the second case, you might look at adding features to the machine learning system itself. so that dangerous types of splintering become less likely. I agree that once you have landed in the bad situation, mitigation options might be much the same, e.g. switch off the agent.

Cheers! My opinion on category theory has changed a bit, because of this post; by making things fit into the category formulation, I developed insights into how general relations could be used to connect different generalised models.

3Koen Holtman2y
Definitely, it has also been my experience that you can often get new insights by constructing mappings to different models or notations.
Load More