I do concede that in theory it should be possible to do this on standard computers, and that it might actually be a good way to test this hypothesis out and gather empirical data today.
Where I'm not so sure is whether even "slow" standard computers think slower than humans do. In other words, imagine some future AI architecture that is OOM more power and compute efficient. It may not be entirely unreasonable to suppose that we could get an algorithmic/architectural innovation that would enable GPT-4 level performance on old legacy hardware (eg, from the 80s). Indeed, at the unit level, we have calculators from the 60s that can out-multiply the fastest mental mathematicians today.
I thought of using virtualization/simulation to emulate a slow sandbox environment for the AI to run in, but as you mention, it does lower the barrier for an "escape", creating risk that might be unacceptable when dealing with ASI.
With that said, being able to simulate this on current hardware does suggest a possible way to test this whole hypothesis out today. For example, we could have two different models: Model A, with higher parameter count and ELO score running on a slower environment, and Model B, with less parameters/lower ELO score running on a faster environment, and see if Model B is able to sufficiently supervise Model A on some task that would be difficult (or impossible) if both were running at the same speed.
I do concede that in theory it should be possible to do this on standard computers, and that it might actually be a good way to test this hypothesis out and gather empirical data today.
Where I'm not so sure is whether even "slow" standard computers think slower than humans do. In other words, imagine some future AI architecture that is OOM more power and compute efficient. It may not be entirely unreasonable to suppose that we could get an algorithmic/architectural innovation that would enable GPT-4 level performance on old legacy hardware (eg, from the 80s). Indeed, at the unit level, we have calculators from the 60s that can out-multiply the fastest mental mathematicians today.
I thought of using virtualization/simulation to emulate a slow sandbox environment for the AI to run in, but as you mention, it does lower the barrier for an "escape", creating risk that might be unacceptable when dealing with ASI.
With that said, being able to simulate this on current hardware does suggest a possible way to test this whole hypothesis out today. For example, we could have two different models: Model A, with higher parameter count and ELO score running on a slower environment, and Model B, with less parameters/lower ELO score running on a faster environment, and see if Model B is able to sufficiently supervise Model A on some task that would be difficult (or impossible) if both were running at the same speed.