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?
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.”
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.)
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.
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 parameters. (In this case, maybe the AI’s normal motivation system is choosing to “press the button” that sends the data-dump, but it does not have direct control over the contents of the data-dump.) Separately, we would also set up the AI such that it’s motivated to not manipulate the human, and also motivated to not sabotage its own motivation and control systems.
(BTW a lot of my thinking here came straight out of reading your model splintering posts. But maybe I’ve kinda wandered off in a different direction.)
So then in the scenario you mentioned, let’s assume thatwe’ve set up the AI such that actions that pattern-match to “push the world into uncharted territory” are treated as unacceptable (which I guess seems like a plausibly good idea). But the AI is also motivated to get something done—say, solve global warming. And it finds a possible course of action which pattern-matches very well to “solve global warming”, but alas, it also pattern-matches to “push the world into uncharted territory”. The AI could reason that, if it queries the human (by “pressing the button” to send the data-dump), there’s at least a chance that the human would edit its systems such that this course of action would no longer be unacceptable. So it would presumably do so.
In other words, this is a situation where the AI’s motivational system is sending it mixed signals—it does want to “solve global warming”, but it doesn’t want to “push the world into uncharted territory”, but this course of action is both. And let’s assume that the AI can’t easily come up with an alternative course of action that would solve global warming without any problematic aspects. So the AI asks the human what they think about this plan. Seems reasonable, I guess.
I haven’t thought this through very much and look forward to you picking holes in it :)
My take is that if you gave an optimization process access to some handwritten acceptability criteria and searched for the nearest acceptable points to random starting points, you would get adversarial examples that violate unstated criteria. In order for the handwritten acceptability criteria to be useful, they can’t be how the AI generates its ideas in the first place.
So: what is the base level that we would find if we peeled away the value learning scheme that you lay out? Is it a very general, human-agnostic AI with some human-value constraints on top? Or will we peel away a layer that gets information from humans just to reveal another layer that gets information from humans (e.g. learning a “human distribution”)?
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?
Hmm,
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.”
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.)
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.
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 parameters. (In this case, maybe the AI’s normal motivation system is choosing to “press the button” that sends the data-dump, but it does not have direct control over the contents of the data-dump.) Separately, we would also set up the AI such that it’s motivated to not manipulate the human, and also motivated to not sabotage its own motivation and control systems.
(BTW a lot of my thinking here came straight out of reading your model splintering posts. But maybe I’ve kinda wandered off in a different direction.)
So then in the scenario you mentioned, let’s assume that we’ve set up the AI such that actions that pattern-match to “push the world into uncharted territory” are treated as unacceptable (which I guess seems like a plausibly good idea). But the AI is also motivated to get something done—say, solve global warming. And it finds a possible course of action which pattern-matches very well to “solve global warming”, but alas, it also pattern-matches to “push the world into uncharted territory”. The AI could reason that, if it queries the human (by “pressing the button” to send the data-dump), there’s at least a chance that the human would edit its systems such that this course of action would no longer be unacceptable. So it would presumably do so.
In other words, this is a situation where the AI’s motivational system is sending it mixed signals—it does want to “solve global warming”, but it doesn’t want to “push the world into uncharted territory”, but this course of action is both. And let’s assume that the AI can’t easily come up with an alternative course of action that would solve global warming without any problematic aspects. So the AI asks the human what they think about this plan. Seems reasonable, I guess.
I haven’t thought this through very much and look forward to you picking holes in it :)
My take is that if you gave an optimization process access to some handwritten acceptability criteria and searched for the nearest acceptable points to random starting points, you would get adversarial examples that violate unstated criteria. In order for the handwritten acceptability criteria to be useful, they can’t be how the AI generates its ideas in the first place.
So: what is the base level that we would find if we peeled away the value learning scheme that you lay out? Is it a very general, human-agnostic AI with some human-value constraints on top? Or will we peel away a layer that gets information from humans just to reveal another layer that gets information from humans (e.g. learning a “human distribution”)?