by Jessica Taylor 1194 days ago | Patrick LaVictoire likes this | link | parent I think the main problem with using a pre-specified distribution over actions is that, since it doesn’t reflect the AI’s actual behavior, you can’t say much about $$P(B= b | e)$$ in relation to real life. For example, maybe the implicit policy is to take random actions, which results in humans not pressing the shutdown button; therefore, in real life the AI is confident that the button will not be pressed, so it spends no resources looking at the shutdown button to see if it was pressed.

 by Patrick LaVictoire 1191 days ago | link Maybe if the pre-specified distribution is a reasonably well-calibrated predictor of the AI (given that distribution)? Like, maybe this is a way that an Oracle AI could help ensure the safety of a somewhat weaker Tool AI. reply
 by Stuart Armstrong 1190 days ago | link How about an iterated process? If you have a series of actions and observations $$e_n=a_no_na_{n-1}o_{n-1}\ldots$$, and $$A$$ is the set of possible actions, have the AI pick $$a$$ such that: $$a_{n} = \operatorname{argmax}_{a} \frac{1}{|A|}\sum_{a'\in A,b} U(ao_ne_{n-1}|B=b) P(B=b|a'o_ne_{n-1})$$ (the $$\frac{1}{|A|}$$ is not necessary, just a reminder that we are choosing a uniform distribution over the possible actions). Notice that the AI conditions correctly on all its past actions, but not its current hypothetical. I wonder how this would work if we set $$A={0,1}$$ and had the AI type out messages bit by bit, each time thinking the probability of $$B$$ was independent of its bit-choice? EDIT: What this means is that, as soon as it’s done any action that might cause the shutdown button to be pressed, it will then look at the shutdown button (but maybe doesn’t expect, ahead of time, that it will be looking at the button). reply
 by Jessica Taylor 1190 days ago | link I think, to get $$P(B = b)$$, you have to have an implicit policy for the entire rest of the game (not just the next action $$a'$$). I like the idea of using the evidence you have so far to inform the $$P(b)$$, so you spend more effort on looking at the shutdown button if you expect shutdown might be imminent based on your evidence. Of course, you can combine this with the fixed point thing, so the distribution of $$a'$$ is the same as the distribution of $$a$$. My main concern is that this isn’t reflectively stable. If at an early time step the AI has a certain $$P(b)$$ distribution, it may want to modify into an agent that fixes this as the correct $$P(b)$$ rather than changing $$P(b)$$ in response to new evidence; this is because it is modelling $$B$$ as coming independently from $$P(b)$$. reply

NEW DISCUSSION POSTS

[Note: This comment is three
 by Ryan Carey on A brief note on factoring out certain variables | 0 likes

There should be a chat icon
 by Alex Mennen on Meta: IAFF vs LessWrong | 0 likes

Apparently "You must be
 by Jessica Taylor on Meta: IAFF vs LessWrong | 1 like

There is a replacement for
 by Alex Mennen on Meta: IAFF vs LessWrong | 1 like

Regarding the physical
 by Vanessa Kosoy on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

I think that we should expect
 by Vanessa Kosoy on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

I think I understand your
 by Jessica Taylor on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

This seems like a hack. The
 by Jessica Taylor on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

After thinking some more,
 by Vanessa Kosoy on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

Yes, I think that we're
 by Vanessa Kosoy on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

My intuition is that it must
 by Vanessa Kosoy on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

To first approximation, a
 by Vanessa Kosoy on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

Actually, I *am* including
 by Vanessa Kosoy on The Learning-Theoretic AI Alignment Research Agend... | 0 likes

Yeah, when I went back and
 by Alex Appel on Optimal and Causal Counterfactual Worlds | 0 likes

> Well, we could give up on
 by Jessica Taylor on The Learning-Theoretic AI Alignment Research Agend... | 0 likes