In my view, in order to be dangerous in a particularly direct way (instead of just misuse risk etc.), an AI’s decision to give output X depends on the fact that output X has some specific effects in the future.
Agreed.
Whereas, if you train it on a problem where solutions don’t need to depend on the effects of the outputs on the future, I think it much more likely to learn to find the solution without routing that through the future, because that’s simpler.
The “problem where solutions don’t need to depend on effects” is where we disagree. I agree such problems exist (e.g. formal proof search), but those aren’t the kind of useful tasks we’re talking about in the post. For actual concrete scientific problems, like outputting designs for a fusion rocket, the “simplest” approach is to be considering the consequences of those outputs on the world. Otherwise, how would it internally define “good fusion rocket design that works when built”? How would it know not to use a design that fails because of weaknesses in the metal that will be manufactured into a particular shape for your rocket? A solution to building a rocket is defined by its effects on the future (not all of its effects, just some of them, i.e. it doesn’t explode, among many others).
I think there’s a (kind of) loophole here, where we use an “abstract hypothetical” model of a hypothetical future, and optimize for consequences our actions for that hypothetical. Is this what you mean by “understood in abstract terms”? So the AI has defined “good fusion rocket design” as “fusion rocket that is built by not-real hypothetical humans based on my design and functions in a not-real hypothetical universe and has properties and consequences XYZ” (but the hypothetical universe isn’t the actual future, it’s just similar enough to define this one task, but dissimilar enough that misaligned goals in this hypothetical world don’t lead to coherent misaligned real-world actions). Is this what you mean? Rereading your comment, I think this matches what you’re saying, especially the chess game part.
The part I don’t understand is why you’re saying that this is “simpler”? It seems equally complex in kolmogorov complexity and computational complexity.
I think there’s a (kind of) loophole here, where we use an “abstract hypothetical” model of a hypothetical future, and optimize for consequences our actions for that hypothetical. Is this what you mean by “understood in abstract terms”?
More or less, yes (in the case of engineering problems specifically, which I think is more real-world-oriented than most science AI).
The part I don’t understand is why you’re saying that this is “simpler”? It seems equally complex in kolmogorov complexity and computational complexity.
What I’m saying is “simpler” is that, given a problem that doesn’t need to depend on the actual effects of the outputs on the future of the real world (where operating in a simulation is an example, though one that could become riskily close to the real world depending on the information taken into account by the simulation—it might not be a good idea to include highly detailed political risks of other humans thwarting construction in a fusion reactor construction simulation for example), it is simpler for the AI to solve that problem without taking into consideration the effects of the output on the future of the real world than it is to take into account the effects of the output on the future of the real world anyway.
I feel like you’re proposing two different types of AI and I want to disambiguate them. The first one, exemplified in your response to Peter (and maybe referenced in your first sentence above), is a kind of research assistant that proposes theories (after having looked at data that a scientist is gathering?), but doesn’t propose experiments and doesn’t think about the usefulness of its suggestions/theories. Like a Solomonoff inductor that just computes the simplest explanation for some data? And maybe some automated approach to interpreting theories?
The second one, exemplified by the chess analogy and last paragraph above, is a bit like a consequentialist agent that is a little detached from reality (can’t learn anything, has a world model that we designed such that it can’t consider new obstacles).
Do you agree with this characterization?
What I’m saying is “simpler” is that, given a problem that doesn’t need to depend on the actual effects of the outputs on the future of the real world […], it is simpler for the AI to solve that problem without taking into consideration the effects of the output on the future of the real world than it is to take into account the effects of the output on the future of the real world anyway.
I accept chess and formal theorem-proving as examples of problem where we can define the solution without using facts about the real-world future (because we can easily write down formally a definition of what the solution looks like).
For a more useful problem (e.g. curing a type of cancer) we (the designers) only know how to define a solution in terms of real world future states (patient is alive, healthy, non traumatized, etc). I’m not saying there doesn’t exist a definition of success that doesn’t involve referencing real-world future states. But the AI designers don’t know it (and I expect it would be relatively complicated).
My understanding of your simplicity argument is that it is saying that it is computationally cheaper for a trained AI to discover during training a non-consequence definition of the task, despite a consequentialist definition being the criterion used to train it? If so, I disagree that computation cost is very relevant here, generalization (to novel obstacles) is the dominant factor determining how useful this AI is.
Agreed.
The “problem where solutions don’t need to depend on effects” is where we disagree. I agree such problems exist (e.g. formal proof search), but those aren’t the kind of useful tasks we’re talking about in the post. For actual concrete scientific problems, like outputting designs for a fusion rocket, the “simplest” approach is to be considering the consequences of those outputs on the world. Otherwise, how would it internally define “good fusion rocket design that works when built”? How would it know not to use a design that fails because of weaknesses in the metal that will be manufactured into a particular shape for your rocket? A solution to building a rocket is defined by its effects on the future (not all of its effects, just some of them, i.e. it doesn’t explode, among many others).
I think there’s a (kind of) loophole here, where we use an “abstract hypothetical” model of a hypothetical future, and optimize for consequences our actions for that hypothetical. Is this what you mean by “understood in abstract terms”? So the AI has defined “good fusion rocket design” as “fusion rocket that is built by not-real hypothetical humans based on my design and functions in a not-real hypothetical universe and has properties and consequences XYZ” (but the hypothetical universe isn’t the actual future, it’s just similar enough to define this one task, but dissimilar enough that misaligned goals in this hypothetical world don’t lead to coherent misaligned real-world actions). Is this what you mean? Rereading your comment, I think this matches what you’re saying, especially the chess game part.
The part I don’t understand is why you’re saying that this is “simpler”? It seems equally complex in kolmogorov complexity and computational complexity.
More or less, yes (in the case of engineering problems specifically, which I think is more real-world-oriented than most science AI).
What I’m saying is “simpler” is that, given a problem that doesn’t need to depend on the actual effects of the outputs on the future of the real world (where operating in a simulation is an example, though one that could become riskily close to the real world depending on the information taken into account by the simulation—it might not be a good idea to include highly detailed political risks of other humans thwarting construction in a fusion reactor construction simulation for example), it is simpler for the AI to solve that problem without taking into consideration the effects of the output on the future of the real world than it is to take into account the effects of the output on the future of the real world anyway.
I feel like you’re proposing two different types of AI and I want to disambiguate them. The first one, exemplified in your response to Peter (and maybe referenced in your first sentence above), is a kind of research assistant that proposes theories (after having looked at data that a scientist is gathering?), but doesn’t propose experiments and doesn’t think about the usefulness of its suggestions/theories. Like a Solomonoff inductor that just computes the simplest explanation for some data? And maybe some automated approach to interpreting theories?
The second one, exemplified by the chess analogy and last paragraph above, is a bit like a consequentialist agent that is a little detached from reality (can’t learn anything, has a world model that we designed such that it can’t consider new obstacles).
Do you agree with this characterization?
I accept chess and formal theorem-proving as examples of problem where we can define the solution without using facts about the real-world future (because we can easily write down formally a definition of what the solution looks like).
For a more useful problem (e.g. curing a type of cancer) we (the designers) only know how to define a solution in terms of real world future states (patient is alive, healthy, non traumatized, etc). I’m not saying there doesn’t exist a definition of success that doesn’t involve referencing real-world future states. But the AI designers don’t know it (and I expect it would be relatively complicated).
My understanding of your simplicity argument is that it is saying that it is computationally cheaper for a trained AI to discover during training a non-consequence definition of the task, despite a consequentialist definition being the criterion used to train it? If so, I disagree that computation cost is very relevant here, generalization (to novel obstacles) is the dominant factor determining how useful this AI is.