Much of my hope is that by the time we reach a superintelligence level where we need to instill reflectively endorsed values to optimize towards in a very hands-off way rather than just constitutions, behaviors, or goals, we’ll have figured something else out. I’m not claiming the optimizer advantage alone is enough to be decisive in saving the world.
To the point about tighter feedback loops, I see the main benefit as being in conjunction with adapting to new problems. Suppose that we notice AIs take some bad but non-world-ending action like murdering people; then we can add a big dataset of situations in which AIs shouldn’t murder people to the training data. If we were instead breeding animals, we would have to wait dozens of generations for mutations that reduce murder rate to appear and reach fixation. Since these mutations affect behavior through brain architecture, they would have a higher chance of deleterious effects. And if we’re also selecting for intelligence, they would be competing against mutations that increase intelligence, producing a higher alignment tax. All this means that we have less chance to detect whether our proxies hold up (capabilities researchers have many of these advantages too, but the AGI would be able to automate capabilities training anyway).
If we expect problems to get worse at some rate until an accumulation of unsolved alignment issues culminates in disempowerment, it seems to me there is a large band of rates where we can stay ahead of them with AI training but evolution wouldn’t be able to.
Much of my hope is that by the time we reach a superintelligence level where we need to instill reflectively endorsed values to optimize towards in a very hands-off way rather than just constitutions, behaviors, or goals, we’ll have figured something else out. I’m not claiming the optimizer advantage alone is enough to be decisive in saving the world.
To the point about tighter feedback loops, I see the main benefit as being in conjunction with adapting to new problems. Suppose that we notice AIs take some bad but non-world-ending action like murdering people; then we can add a big dataset of situations in which AIs shouldn’t murder people to the training data. If we were instead breeding animals, we would have to wait dozens of generations for mutations that reduce murder rate to appear and reach fixation. Since these mutations affect behavior through brain architecture, they would have a higher chance of deleterious effects. And if we’re also selecting for intelligence, they would be competing against mutations that increase intelligence, producing a higher alignment tax. All this means that we have less chance to detect whether our proxies hold up (capabilities researchers have many of these advantages too, but the AGI would be able to automate capabilities training anyway).
If we expect problems to get worse at some rate until an accumulation of unsolved alignment issues culminates in disempowerment, it seems to me there is a large band of rates where we can stay ahead of them with AI training but evolution wouldn’t be able to.