This is an idea that has been raised before. There are a variety of difficulties with it: 1) I can’t precommit to simulating every single possible RAI (there are lots of things an RAI might want to calculate.) 2) Many unFriendly AIs will have goals that are just unobtainable if they are in a simulation. For example, a paperclip maximizer might not see paperclips made in a simulation as actual paperclips. Thus it will reason “either I’m not in a simulation so I will be fine destroying humans to make paperclips or I’m in a simulation in which case nothing I do is likely to alter the number of paperclips at all.) 3) This assumes that highly accurate simulations of reality can occur with not much resources. If that’s not the case then this fails.
1) I can’t precommit to simulating every single possible RAI (there are lots of things an RAI might want to calculate.)
It’s not necessary to do so, just to simulate enough randomly drawn ones (from an approximation of the distribution of UFAIs that might have been created) that any particular deterrable UFAI assigns sufficient subjective probability to being in a simulation.
2) Many unFriendly AIs will have goals that are just unobtainable if they are in a simulation. For example, a paperclip maximizer might not see paperclips made in a simulation as actual paperclips. Thus it will reason “either I’m not in a simulation so I will be fine destroying humans to make paperclips or I’m in a simulation in which case nothing I do is likely to alter the number of paperclips at all.)
This is true. The UFAI must also be satiable, e.g. wanting to perform some finite calculation, rather than maximize paperclips.
It’s not necessary to do so, just to simulate enough randomly drawn ones (from an approximation of the distribution of UFAIs that might have been created) that any particular deterrable UFAI assigns sufficient subjective probability to being in a simulation.
If one is restricted to even just finite calculations this is a very large set such that the probability that a UFAI should assign to being in a simulation should always be low. For example, off the top of my head it might be interested in 1) calculating large Mersenne primes 2) digits of Pi, 3) digits of Euler’s contstant (gamma, not e), 3) L(2,X) where X is the quadratic Dirchlet character mod 7 (in this case there’s a weird empirical identity between this and a certain integral that has been checked out to 20,000 places).And those are all the more well known options. Then one considers all the things specific people want as individuals. I can think of at least three relevant constants that I’d want calculated that are related more narrowly to my own research. And I’m only a grad student. Given how many mathematicians there are in the world, there are going to be a lot of examples in total. Sure, some of them, like digits of Pi, are obvious. But after that...
This assumes that highly accurate simulations of reality can occur with not much resources. If that’s not the case then this fails.
How sure are you that you are not in an approximate simulation of a more precisely detailed reality, with the precision of your expectations scaled down proportionally with the precision of your observations?
(Of course, I am only responding to 1 of your 3 independant arguments)
How sure are you that you are not in an approximate simulation of a more precisely detailed reality, with the precision of your expectations scaled down proportionally with the precision of your observations?
I don’t know if I am or am not in a simulation. But if one has a reasonably FOOMed AI it becomes much more plausible that it would be able to tell. It might be able to detect minor discrepancies. Also, I’d assign a much higher probability to the possibility that I’m in a simulation if I knew that detailed simulations are possible in our universe. If the smart AI determines that it is in a universe that doesn’t allow detailed simulations for at all plausible resource levels then the chance that it is in a simulation should be low.
My point is that the simulation does not have to be as detailed as reality, in part because the agents within the simulation don’t have any reliable experience of being in reality, being themselves less detailed than “real” agents, and so don’t know what level of detail to expect. A simulation could even have simplified reality plus a global rule that manipulates any agent’s working memory to remove any realization it might have that it is in a simulation.
That requires very detailed rules about manipulating agents within the system rather than doing a straight physics simulation (otherwise what do you do when it modifies its memory system). I’m not arguing that it isn’t possibly doable, just that it doesn’t seem necessarily to be likely.
This is an idea that has been raised before. There are a variety of difficulties with it: 1) I can’t precommit to simulating every single possible RAI (there are lots of things an RAI might want to calculate.) 2) Many unFriendly AIs will have goals that are just unobtainable if they are in a simulation. For example, a paperclip maximizer might not see paperclips made in a simulation as actual paperclips. Thus it will reason “either I’m not in a simulation so I will be fine destroying humans to make paperclips or I’m in a simulation in which case nothing I do is likely to alter the number of paperclips at all.) 3) This assumes that highly accurate simulations of reality can occur with not much resources. If that’s not the case then this fails.
Edit: Curious for reason for downvote.
It’s not necessary to do so, just to simulate enough randomly drawn ones (from an approximation of the distribution of UFAIs that might have been created) that any particular deterrable UFAI assigns sufficient subjective probability to being in a simulation.
This is true. The UFAI must also be satiable, e.g. wanting to perform some finite calculation, rather than maximize paperclips.
If one is restricted to even just finite calculations this is a very large set such that the probability that a UFAI should assign to being in a simulation should always be low. For example, off the top of my head it might be interested in 1) calculating large Mersenne primes 2) digits of Pi, 3) digits of Euler’s contstant (gamma, not e), 3) L(2,X) where X is the quadratic Dirchlet character mod 7 (in this case there’s a weird empirical identity between this and a certain integral that has been checked out to 20,000 places).And those are all the more well known options. Then one considers all the things specific people want as individuals. I can think of at least three relevant constants that I’d want calculated that are related more narrowly to my own research. And I’m only a grad student. Given how many mathematicians there are in the world, there are going to be a lot of examples in total. Sure, some of them, like digits of Pi, are obvious. But after that...
How sure are you that you are not in an approximate simulation of a more precisely detailed reality, with the precision of your expectations scaled down proportionally with the precision of your observations?
(Of course, I am only responding to 1 of your 3 independant arguments)
I don’t know if I am or am not in a simulation. But if one has a reasonably FOOMed AI it becomes much more plausible that it would be able to tell. It might be able to detect minor discrepancies. Also, I’d assign a much higher probability to the possibility that I’m in a simulation if I knew that detailed simulations are possible in our universe. If the smart AI determines that it is in a universe that doesn’t allow detailed simulations for at all plausible resource levels then the chance that it is in a simulation should be low.
My point is that the simulation does not have to be as detailed as reality, in part because the agents within the simulation don’t have any reliable experience of being in reality, being themselves less detailed than “real” agents, and so don’t know what level of detail to expect. A simulation could even have simplified reality plus a global rule that manipulates any agent’s working memory to remove any realization it might have that it is in a simulation.
That requires very detailed rules about manipulating agents within the system rather than doing a straight physics simulation (otherwise what do you do when it modifies its memory system). I’m not arguing that it isn’t possibly doable, just that it doesn’t seem necessarily to be likely.