Are you proposing applying this to something potentially prepotent? Or does this come with corrigibility guarantees? If you applied it to a prepotence, I’m pretty sure this would be an extremely bad idea. The actual human utility function (the rules of the game as intended) supports important glitch-like behavior, where cheap tricks can extract enormous amounts of utility, which means that applying this to general alignment has the potential of foreclosing most value that could have existed.
Example 1: Virtual worlds are a weird out-of-distribution part of the human utility function that allows the AI to “cheat” and create impossibly good experiences by cutting the human’s senses off from the real world and showing them an illusion. As far as I’m concerned, creating non-deceptive virtual worlds (like, very good video games) is correct behavior and the future would be immeasurably devalued if it were disallowed.
Example 2: I am not a hedonist, but I can’t say conclusively that I wouldn’t become one (turn out to be one) if I had full knowledge of my preferences, and the ability to self-modify, as well as lots of time and safety to reflect, settle my affairs in the world, set aside my pride, and then wirehead. This is a glitchy looking behavior that allows the AI to extract a much higher yield of utility from each subject by gradually warping them into a shape where they lose touch with most of what we currently call “values”, where one value dominates all of the others. If it is incorrect behavior, then sure, it shouldn’t be allowed to do that, but humans don’t have the kind of self-reflection that is required to tell whether it’s incorrect behavior or not, today, and if it’s correct behavior, forever forbidding it is actually a far more horrifying outcome, what you’d be doing is, in some sense of ‘suffering’, forever prolonging some amount of suffering. That’s fine if humans tolerate and prefer some amount of suffering, but we aren’t sure of that yet.
Wouldn’t really need reward modelling for narrow optimizers. Weak general real-world optimizers, I find difficult to imagine, and I’d expect them to be continuous with strong ones, the projects to make weak ones wouldn’t be easily distinguishable from the projects to make strong ones.
Oh, are you thinking of applying it to say, simulation training.
Are you proposing applying this to something potentially prepotent? Or does this come with corrigibility guarantees? If you applied it to a prepotence, I’m pretty sure this would be an extremely bad idea. The actual human utility function (the rules of the game as intended) supports important glitch-like behavior, where cheap tricks can extract enormous amounts of utility, which means that applying this to general alignment has the potential of foreclosing most value that could have existed.
Example 1: Virtual worlds are a weird out-of-distribution part of the human utility function that allows the AI to “cheat” and create impossibly good experiences by cutting the human’s senses off from the real world and showing them an illusion. As far as I’m concerned, creating non-deceptive virtual worlds (like, very good video games) is correct behavior and the future would be immeasurably devalued if it were disallowed.
Example 2: I am not a hedonist, but I can’t say conclusively that I wouldn’t become one (turn out to be one) if I had full knowledge of my preferences, and the ability to self-modify, as well as lots of time and safety to reflect, settle my affairs in the world, set aside my pride, and then wirehead. This is a glitchy looking behavior that allows the AI to extract a much higher yield of utility from each subject by gradually warping them into a shape where they lose touch with most of what we currently call “values”, where one value dominates all of the others. If it is incorrect behavior, then sure, it shouldn’t be allowed to do that, but humans don’t have the kind of self-reflection that is required to tell whether it’s incorrect behavior or not, today, and if it’s correct behavior, forever forbidding it is actually a far more horrifying outcome, what you’d be doing is, in some sense of ‘suffering’, forever prolonging some amount of suffering. That’s fine if humans tolerate and prefer some amount of suffering, but we aren’t sure of that yet.
I do not propose one applies this method to a prepotence
Cool then.
Are you aware that prepotence is the default for strong optimizers though?
What about mediocre optimizers? Are they not worth fooling with?
Wouldn’t really need reward modelling for narrow optimizers. Weak general real-world optimizers, I find difficult to imagine, and I’d expect them to be continuous with strong ones, the projects to make weak ones wouldn’t be easily distinguishable from the projects to make strong ones.
Oh, are you thinking of applying it to say, simulation training.