I’m curious what you mean by “anti-natural.” You write:
Importantly, that is the aspect of corrigibility that is anti-natural, meaning that it can’t be straightforwardly captured in a ranking of end states.
My understanding of anti-naturality used to resemble this, before I had an in-depth conversation with Nate Soares and updated to see anti-naturality to be more like “opposed to instrumental convergence.” My understanding is plausibly still confused and I’m not trying to be authoritative here.
If you mean “not straightforwardly captured in a ranking of end states” what does “straightforwardly” do in that definition?
Follow-up question, assuming anti-naturality goals are “not straightforwardly captured in a ranking of end states”: Suppose I have a gridworld and I want to train an AI to avoid walking within 5 spaces (manhattan distance) from a flag, and to (less importantly) eat all the apples in a level. Is this goal anti-natural? I can’t think of any way to reflect it as a straightforward ranking of end states, since it involves tracking historical facts rather than end-state facts. My guess is that it’s pretty easy to build an agent that does this (via ML/RL approaches or just plain programming). Do you agree? If this goal is anti-natural, why is the anti-naturality a problem or otherwise noteworthy?
When I say straightforwardly, I mean when using end states that only include the information available at the time. If we define the end state to also include the history that lead to it, then there exists a set of preferences over them that ranks all end states with histories that include manipulation below the ones that don’t. The issue, of course, is that we don’t know how to specify all the types of manipulation that a superintelligent AI could conceive of.
The gridworld example is a great demonstration of this, because while we can’t reflect the preferences as a ranking of just the end states, the environment is simple enough that you can specify all the paths you don’t want to take to them. I don’t think it really matters whether you call that “anti-naturality that can be overcome with brute force in a simple environment” or just “not anti-naturality”.
Cool. Thanks for the clarification. I think what you call “anti-naturality” you should be calling “non-end-state consequentialism,” but I’m not very interested in linguistic turf-wars.
It seems to me that while the gridworld is very simple, the ability to train agents to optimize for historical facts is not restricted to simple environments. For example, I think one can train an AI to cause a robot to do backflips by rewarding it every time it completes a backflip. In this context the environment and goal are significantly more complex[1] than the gridworld and cannot be solved by brute-force. But number of backflips performed is certainly not something that can be measured at any given timeslice, including the “end-state.”
If caring about historical facts is easy and common, why is it important to split this off and distinguish it?
Though admittedly this situation is still selected for being simple enough to reason about. If needed I believe this point holds through AGI-level complexity, but things tend to get more muddled as things get more complex, and I’d prefer sticking to the minimal demonstration.
The backflip example does not strike me as very complex, but the crucial difference and the answer to your question is that training procedures do not teach a robot to do every kind of backflip, just a subset. This is important because when we reverse it, we want non-manipulation to cover the entire set of manipulations. I think it’s probably feasible to have AI not manipulate us using one particular type of manipulation.
On a separate note, could you clarify what you mean by “anti-natural”? I’ll keep in mind your previous caveat that it’s not definitive.
More directly, I would say that general competence/intelligence is connected with certain ways of thinking. For example, modes of thinking that focus on tracking scarce resources and bottlenecks are generally useful. If we think about processes that select for intelligence, those processes are naturally[1] going to select these ways of thinking. Some properties we might imagine a mind having, such as only thinking locally, are the opposite of this—if we select for them, we are fighting the intelligence gradient. To say that a goal is anti-natural means that accomplishing that goal involves learning to think in anti-natural ways, and thus training a mind to have that goal is like swimming against the current, and we should expect it to potentially break if the training processes puts too much weight on competence compared to alignment. Minds with anti-natural goals are possible, but harder to produce using known methods, for the most part.
(AFAIK this is the way that Nate Soares uses the term, and I assume the way Eliezer Yudkowsky thinks about it as well, but I’m also probably missing big parts of their perspectives, and generally don’t trust myself to pass their ITT.)
The term “anti-natural” is bad in that it seems to be the opposite of “natural,” but is not a general opposite of natural. While I do believe that the ways-of-thinking-that-are-generally-useful are the sorts of things that naturally emerge when selecting for intelligence, there are clearly plenty of things which the word “natural” describes besides these ways of thinking. The more complete version of “anti-natural” according to me would be “anti-the-useful-cognitive-strategies-that-naturally-emerge-when-selecting-for-intelligence” but obviously we need a shorthand term, and ideally one that doesn’t breed confusion.
I’m curious what you mean by “anti-natural.” You write:
My understanding of anti-naturality used to resemble this, before I had an in-depth conversation with Nate Soares and updated to see anti-naturality to be more like “opposed to instrumental convergence.” My understanding is plausibly still confused and I’m not trying to be authoritative here.
If you mean “not straightforwardly captured in a ranking of end states” what does “straightforwardly” do in that definition?
Follow-up question, assuming anti-naturality goals are “not straightforwardly captured in a ranking of end states”: Suppose I have a gridworld and I want to train an AI to avoid walking within 5 spaces (manhattan distance) from a flag, and to (less importantly) eat all the apples in a level. Is this goal anti-natural? I can’t think of any way to reflect it as a straightforward ranking of end states, since it involves tracking historical facts rather than end-state facts. My guess is that it’s pretty easy to build an agent that does this (via ML/RL approaches or just plain programming). Do you agree? If this goal is anti-natural, why is the anti-naturality a problem or otherwise noteworthy?
Great questions!
When I say straightforwardly, I mean when using end states that only include the information available at the time. If we define the end state to also include the history that lead to it, then there exists a set of preferences over them that ranks all end states with histories that include manipulation below the ones that don’t. The issue, of course, is that we don’t know how to specify all the types of manipulation that a superintelligent AI could conceive of.
The gridworld example is a great demonstration of this, because while we can’t reflect the preferences as a ranking of just the end states, the environment is simple enough that you can specify all the paths you don’t want to take to them. I don’t think it really matters whether you call that “anti-naturality that can be overcome with brute force in a simple environment” or just “not anti-naturality”.
Cool. Thanks for the clarification. I think what you call “anti-naturality” you should be calling “non-end-state consequentialism,” but I’m not very interested in linguistic turf-wars.
It seems to me that while the gridworld is very simple, the ability to train agents to optimize for historical facts is not restricted to simple environments. For example, I think one can train an AI to cause a robot to do backflips by rewarding it every time it completes a backflip. In this context the environment and goal are significantly more complex[1] than the gridworld and cannot be solved by brute-force. But number of backflips performed is certainly not something that can be measured at any given timeslice, including the “end-state.”
If caring about historical facts is easy and common, why is it important to split this off and distinguish it?
Though admittedly this situation is still selected for being simple enough to reason about. If needed I believe this point holds through AGI-level complexity, but things tend to get more muddled as things get more complex, and I’d prefer sticking to the minimal demonstration.
The backflip example does not strike me as very complex, but the crucial difference and the answer to your question is that training procedures do not teach a robot to do every kind of backflip, just a subset. This is important because when we reverse it, we want non-manipulation to cover the entire set of manipulations. I think it’s probably feasible to have AI not manipulate us using one particular type of manipulation.
On a separate note, could you clarify what you mean by “anti-natural”? I’ll keep in mind your previous caveat that it’s not definitive.
Sure, let’s talk about anti-naturality. I wrote some about my perspective on it here: https://www.alignmentforum.org/s/KfCjeconYRdFbMxsy/p/3HMh7ES4ACpeDKtsW#_Anti_Naturality__and_Hardness
More directly, I would say that general competence/intelligence is connected with certain ways of thinking. For example, modes of thinking that focus on tracking scarce resources and bottlenecks are generally useful. If we think about processes that select for intelligence, those processes are naturally[1] going to select these ways of thinking. Some properties we might imagine a mind having, such as only thinking locally, are the opposite of this—if we select for them, we are fighting the intelligence gradient. To say that a goal is anti-natural means that accomplishing that goal involves learning to think in anti-natural ways, and thus training a mind to have that goal is like swimming against the current, and we should expect it to potentially break if the training processes puts too much weight on competence compared to alignment. Minds with anti-natural goals are possible, but harder to produce using known methods, for the most part.
(AFAIK this is the way that Nate Soares uses the term, and I assume the way Eliezer Yudkowsky thinks about it as well, but I’m also probably missing big parts of their perspectives, and generally don’t trust myself to pass their ITT.)
The term “anti-natural” is bad in that it seems to be the opposite of “natural,” but is not a general opposite of natural. While I do believe that the ways-of-thinking-that-are-generally-useful are the sorts of things that naturally emerge when selecting for intelligence, there are clearly plenty of things which the word “natural” describes besides these ways of thinking. The more complete version of “anti-natural” according to me would be “anti-the-useful-cognitive-strategies-that-naturally-emerge-when-selecting-for-intelligence” but obviously we need a shorthand term, and ideally one that doesn’t breed confusion.
Thanks for the clarification, I’ll think more about it that way and how it relates to corrigibility