Thanks for the clarification, I'll think more about it that way and how it relates to corrigibility

I don't think we have the right tools to make an AI take actions that are low impact and reversible, but if we can develop them the plan as I see it would be to implement those properties to avoid manipulation in the short term and use that time to go from a corrigible AI to a fully aligned one.

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.

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".

I was using the list of desiderate in Section 2 of the paper, which are slightly more minimal.

However, it seems clear to me that an AI manipulating it's programmers falls under safe exploration, since the impact of doing so would be drastic and permanent. If we have an AI that is corrigible in the sense that it is indifferent to having its goals changed, then a preference to avoid manipulation is not anti-natural.

I agree that goals as pointers could have some advantages, but I don't see how it addresses corrigibility concerns. The system optimizing for whatever is being pointed at would still have incentives to manipulate which objective is being pointed at. It seems like you need an extra piece to make the optimizer indifferent to having it's goal switched. 

I agree that in theory uncertainty about the goal is helpful. However, the true main goal has to be under consideration, otherwise resisting modification to add it is beneficial for all goals that are. How to ensure the true goal is included seems like a very difficult open problem.

Hi Max,

I just published the post I mentioned here, which is about half-related to your post. The main thrust of it is that only the resistance to being modified is anti-natural, and that aspect can be targeted directly.

Thanks for pre-empting the responses, that makes it easy to reply! 

I would basically agree with the third option. Semantically, I would argue that rather than thinking of that agent as semi-corrigible, we should just think of it as corrigible, and "writes useful self critiques" as a separate property we would like the AI to have. I'm writing a post about this that should be up shortly, I'll notify you when it's out.

When I say corrigibility as a modifier, I mean it as a transformation that could be applied to a wide range of utility functions. To use an example from the 2015 MIRI paper, you can take most utility functions and add a term that says "if you shut down when the button is pressed, you get utility equal to the expected value of not shutting down". Alternatively, it could be an optimization constraint that takes a utility function from "Maximize X" to something like "Maximize X s.t. you always shut down when the shutdown button is pushed". While I'm not advocating for those specific changes, I hope they illustrate what I'm trying to point at as a modifier that is distinct from the optimization goal.