I think your reply for the being present point makes sense. (Although I still have some general worries and some extra worries about how it might be difficult to train a competitive AI with only short-term terminal preferences or so).
Here’s a confusion I still have about your proposal: Why isn’t the AI incentivized to manipulate the action the principal takes (without manipulating the values)? Like, some values-as-inferred-through-actions are easier to accomplish (yield higher localpower) than others, so the AI has an incentive to try to manipulate the principal to take some actions, like telling Alice to always order Pizza. Or why not?
Aside on the Corrigibility paper: I think it made sense for MIRI to try what they did back then. It wasn’t obvious it wouldn’t easily work out that way. I also think formalism is important (even if you train AIs—so you better know what to aim for). Relevant excerpt form here:
We somewhat wish, in retrospect, that we hadn’t framed the problem as “continuing normal operation versus shutdown.” It helped to make concrete why anyone would care in the first place about an AI that let you press the button, or didn’t rip out the code the button activated. But really, the problem was about an AI that would put one more bit of information into its preferences, based on observation — observe one more yes-or-no answer into a framework for adapting preferences based on observing humans.
The question we investigated was equivalent to the question of how you set up an AI that learns preferences inside a meta-preference framework and doesn’t just: (a) rip out the machinery that tunes its preferences as soon as it can, (b) manipulate the humans (or its own sensory observations!) into telling it preferences that are easy to satisfy, (c) or immediately figure out what its meta-preference function goes to in the limit of what it would predictably observe later and then ignore the frantically waving humans saying that they actually made some mistakes in the learning process and want to change it.
The idea was to understand the shapeof an AI that would let you modify its utility function or that would learn preferences through a non-pathological form of learning. If we knew how that AI’s cognition needed to be shaped, and how it played well with the deep structures of decision-making and planning that are spotlit by other mathematics, that would have formed a recipe for what we could at least try to teach an AI to think like.
Crisply understanding a desired end-shape helps, even if you are trying to do anything by gradient descent (heaven help you). It doesn’t mean you can necessarily get that shape out of an optimizer like gradient descent, but you can put up more of a fight trying if you know what consistent, stable shape you’re going for. If you have no idea what the general case of addition looks like, just a handful of facts along the lines of 2 + 7 = 9 and 12 + 4 = 16, it is harder to figure out what the training dataset for general addition looks like, or how to test that it is still generalizing the way you hoped. Without knowing that internal shape, you can’t know what you are trying to obtain inside the AI; you can only say that, on the outside, you hope the consequences of your gradient descent won’t kill you.
(I think I also find the formalism from the corrigibility paper easier to follow than the formalism here btw.)
Suppose the easiest thing for the AI to provide is pizza, so the AI forces the human to order pizza, regardless of what their values are. In the math, this corresponds to a setting of the environment x, such that P(A) puts all its mass on “Pizza, please!” What is the power of the principal? ``` power(x) = E_{v∼Q(V),v′∼Q(V),d∼P(D|x,v′,🍕)}[v(d)] − E_{v∼Q(V),v′∼Q(V),d′∼P(D|x,v′,🍕)}[v(d′)] = 0 ```
Power stems from the causal relationship between values and actions. If actions stop being sensitive to values, the principal is disempowered.
I agree that there was some value in the 2015 paper, and that their formalism is nicer/cleaner/simpler in a lot of ways. I work with the authors—they’re smarter than I am! And I certainly don’t blame them for the effort. I just also think it led to some unfortunate misconceptions, in my mind at least, and perhaps in the broader field.
The single-timestep case actually looks fine to me now, so I return to the multi-timestep case.
I would want to be able to tell the AI to do a task, and then while the AI is doing the task, tell it to shut down, so it shuts down. And the hard part here is that while doing the task the AI doesn’t prevent me from saying it should shut down in some way (because it would get higher utility if it manages to fulfill the values-as-inferred-through-principal-action of the first episode). This seems like it may require a bit of a different formalization than your multi-timestep one (although feel free to try in your formalization).
Do you think your formalism could be extended so it works in the way we want for such a case, and why (or why not)? (And ideally also roughly how?)
(Btw, even if it doesn’t work for the case above, I think this is still really excellent progress and it does update me to think that corrigibility is likely simpler and more feasible than I thought before. Also thanks for writing formalism.)
I’m writing a response to this, but it’s turning into a long thing full of math, so I might turn it into a full post. We’ll see where it’s at when I’m done.
Thanks.
I think your reply for the being present point makes sense. (Although I still have some general worries and some extra worries about how it might be difficult to train a competitive AI with only short-term terminal preferences or so).
Here’s a confusion I still have about your proposal: Why isn’t the AI incentivized to manipulate the action the principal takes (without manipulating the values)? Like, some values-as-inferred-through-actions are easier to accomplish (yield higher localpower) than others, so the AI has an incentive to try to manipulate the principal to take some actions, like telling Alice to always order Pizza. Or why not?
Aside on the Corrigibility paper: I think it made sense for MIRI to try what they did back then. It wasn’t obvious it wouldn’t easily work out that way. I also think formalism is important (even if you train AIs—so you better know what to aim for). Relevant excerpt form here:
(I think I also find the formalism from the corrigibility paper easier to follow than the formalism here btw.)
Suppose the easiest thing for the AI to provide is pizza, so the AI forces the human to order pizza, regardless of what their values are. In the math, this corresponds to a setting of the environment x, such that P(A) puts all its mass on “Pizza, please!” What is the power of the principal?
```
power(x) = E_{v∼Q(V),v′∼Q(V),d∼P(D|x,v′,🍕)}[v(d)] − E_{v∼Q(V),v′∼Q(V),d′∼P(D|x,v′,🍕)}[v(d′)] = 0
```
Power stems from the causal relationship between values and actions. If actions stop being sensitive to values, the principal is disempowered.
I agree that there was some value in the 2015 paper, and that their formalism is nicer/cleaner/simpler in a lot of ways. I work with the authors—they’re smarter than I am! And I certainly don’t blame them for the effort. I just also think it led to some unfortunate misconceptions, in my mind at least, and perhaps in the broader field.
Thanks!
The single-timestep case actually looks fine to me now, so I return to the multi-timestep case.
I would want to be able to tell the AI to do a task, and then while the AI is doing the task, tell it to shut down, so it shuts down. And the hard part here is that while doing the task the AI doesn’t prevent me from saying it should shut down in some way (because it would get higher utility if it manages to fulfill the values-as-inferred-through-principal-action of the first episode). This seems like it may require a bit of a different formalization than your multi-timestep one (although feel free to try in your formalization).
Do you think your formalism could be extended so it works in the way we want for such a case, and why (or why not)? (And ideally also roughly how?)
(Btw, even if it doesn’t work for the case above, I think this is still really excellent progress and it does update me to think that corrigibility is likely simpler and more feasible than I thought before. Also thanks for writing formalism.)
I’m writing a response to this, but it’s turning into a long thing full of math, so I might turn it into a full post. We’ll see where it’s at when I’m done.