I don’t see that as likely, because at low capabilities levels, researchers can notice that the reward isn’t working and just it, without needing to rely on the AI asking them.
Consider a task like asking a generally-intelligent chatbot to buy you furniture you like. The only reasonable way to model the reward involves asking 20-questions about your sub-preferences for sofa styles. This seems like the nature of most service sector tasks?
I have a hard time inferring the specifics of that scenario, and I think the specifics probably matter a lot. So I need to ask some further questions.
Why exactly would a generally-intelligent chatbot be useful for buying furniture (over, say, an expert system)? If I try to come up with reasons, I could imagine it would make sense if it has to find the best deal over unstructured data including all sorts of arbitrary settings, such as people who set their couch for sale. Or if it has to go out and get the furniture. Is that what you have in mind?
Furthermore, let’s repeat that the hard part isn’t in manually specifying a distinction when you have that distinction in mind, it’s in spontaneously recognizing a need for a distinction, accurately conveying the options for the distinctions to the humans, and interpreting that to pick the appropriate distinction. When it comes to something like a firm that sells a chatbot for furniture preferences, I don’t really follow how this latter part is needed. Because it seems like the people who make the furniture-buying chatbot could sit down and enumerate whatever preferences are needed to be clarified, and then code that into the chatbot directly. The best explanation I can come up with is that you imagine it being much more general than this, being more like a sort of servant bot which can handle many tasks, not just buying furniture?
Finally, I’m unsure of what capabilities you imagine the chatbot to have. For instance, a possible “ground truth” you could use for training would be to have humans rate the furniture after they’ve received and used it, on a scale from bad to good. For bots that are not very capable, perhaps the best way to optimize their ratings would be to just get good furniture. But for bots that are highly capable, there are many other ways to get good reviews, e.g. hacking into the system and overriding them. I’m not sure if you imagine the low-capability end or the high-capability end here.
The chatbot is “generally intelligent”, so buying furniture is just one of many tasks it may be asked to execute; another task it could be asked to do is “order me some food”.
The hard part is indeed in spontaneously recognizing distinctions—but we already reward RL agents for curiosity, i.e. taking an action for which your world model fails to predict the consequences. Predicting which new distinctions are salient-to-humans is a thing you can optimize, because you can cleanly label it.
Also to clarify, we’re only arguing here about whether this capability will be naturally invested-in, so I don’t think it matters if highly capable bots have other strategies.
I think the capabilities of the AI matters a lot for alignment strategies, and that’s why I’m asking you about it and why I need you to answer that question.
A subhuman intelligence would rely on humans to make most of the decisions. It would order human-designed furniture types through human-created interfaces and receive human-fabricated furniture. At each of those steps, it delgates an enormous number of decisions to humans, which makes those decisions automatically end up reasonably aligned, but also prevents the AI from doing optimization over them. In the particular case of human-designed interfaces, they tend to automatically expose information about the things that humans care about, and eliciting human preferences can be shortcut be focusing on these dimensions.
But a superhuman intelligence would solve tasks through taking actions independently of humans, as that can allow it to more highly optimize the outcomes. And a solution for alignment that relies on humans making most of the decisions would presumably not generalize to this case, where the AI makes most of the decisions.
I don’t see that as likely, because at low capabilities levels, researchers can notice that the reward isn’t working and just it, without needing to rely on the AI asking them.
Consider a task like asking a generally-intelligent chatbot to buy you furniture you like. The only reasonable way to model the reward involves asking 20-questions about your sub-preferences for sofa styles. This seems like the nature of most service sector tasks?
I have a hard time inferring the specifics of that scenario, and I think the specifics probably matter a lot. So I need to ask some further questions.
Why exactly would a generally-intelligent chatbot be useful for buying furniture (over, say, an expert system)? If I try to come up with reasons, I could imagine it would make sense if it has to find the best deal over unstructured data including all sorts of arbitrary settings, such as people who set their couch for sale. Or if it has to go out and get the furniture. Is that what you have in mind?
Furthermore, let’s repeat that the hard part isn’t in manually specifying a distinction when you have that distinction in mind, it’s in spontaneously recognizing a need for a distinction, accurately conveying the options for the distinctions to the humans, and interpreting that to pick the appropriate distinction. When it comes to something like a firm that sells a chatbot for furniture preferences, I don’t really follow how this latter part is needed. Because it seems like the people who make the furniture-buying chatbot could sit down and enumerate whatever preferences are needed to be clarified, and then code that into the chatbot directly. The best explanation I can come up with is that you imagine it being much more general than this, being more like a sort of servant bot which can handle many tasks, not just buying furniture?
Finally, I’m unsure of what capabilities you imagine the chatbot to have. For instance, a possible “ground truth” you could use for training would be to have humans rate the furniture after they’ve received and used it, on a scale from bad to good. For bots that are not very capable, perhaps the best way to optimize their ratings would be to just get good furniture. But for bots that are highly capable, there are many other ways to get good reviews, e.g. hacking into the system and overriding them. I’m not sure if you imagine the low-capability end or the high-capability end here.
The chatbot is “generally intelligent”, so buying furniture is just one of many tasks it may be asked to execute; another task it could be asked to do is “order me some food”.
The hard part is indeed in spontaneously recognizing distinctions—but we already reward RL agents for curiosity, i.e. taking an action for which your world model fails to predict the consequences. Predicting which new distinctions are salient-to-humans is a thing you can optimize, because you can cleanly label it.
Also to clarify, we’re only arguing here about whether this capability will be naturally invested-in, so I don’t think it matters if highly capable bots have other strategies.
I think the capabilities of the AI matters a lot for alignment strategies, and that’s why I’m asking you about it and why I need you to answer that question.
A subhuman intelligence would rely on humans to make most of the decisions. It would order human-designed furniture types through human-created interfaces and receive human-fabricated furniture. At each of those steps, it delgates an enormous number of decisions to humans, which makes those decisions automatically end up reasonably aligned, but also prevents the AI from doing optimization over them. In the particular case of human-designed interfaces, they tend to automatically expose information about the things that humans care about, and eliciting human preferences can be shortcut be focusing on these dimensions.
But a superhuman intelligence would solve tasks through taking actions independently of humans, as that can allow it to more highly optimize the outcomes. And a solution for alignment that relies on humans making most of the decisions would presumably not generalize to this case, where the AI makes most of the decisions.
I think there are intermediate cases—delegating some but not all decisions—that require this sort of tooling. See Eg this paper from today: http://ai.googleblog.com/2022/04/simple-and-effective-zero-shot-task.html that focuses on how to learn intent.