Thanks! Edits made accordingly. Two notes on the stuff you mentioned that isn’t just my embarrassing lack of proofreading:
The definition of optimization used in Risks From Learned Optimization is actually quite different from the definition I’m using here. They say:
“a system is an optimizer if it is internally searching through a search space (consisting of possible outputs, policies, plans, strategies, or similar) looking for those elements that score high according to some objective function that is explicitly represented within the system.”
I personally don’t really like this definition, since it leans quite hard on reifying certain kinds of algorithms—when is there “really” explicit search going on? Where is the search space? When does a configuration of atoms consitute an objective function? Using this definition strictly, humans aren’t *really* optimizers, we don’t have an explicit objective function written down anywhere. Balls rolling down hills aren’t optimizers either.
But by the definition of optimization I’ve been using here, I think pretty much all evolved organisms have to be at least weak optimizers, because survival is hard. You have manage constraints from food and water and temperature and predation etc… the window of action-sequences that lead to successful reproduction are really quite narrow compared to the whole space. Maintaining homeostasis requires ongoing optimization pressure.
Agree that not all optimization processes fundamentally have to be produced by other optimization processes, and that they can crop up anywhere you have the necessary negentropy resevoir. I think my claim is that optimization processes are by default rare (maybe this is exactly because they require negentropy?). But since optimizers beget other optimizers at a rate much higher than background, we should expect the majority of optimization to arise from other optimization. Existing hereditary trees of optimizers grow deeper much faster than new roots spawn, so we should expect roots to occupy a negligible fraction of the nodes as time goes on.
Thanks! Edits made accordingly. Two notes on the stuff you mentioned that isn’t just my embarrassing lack of proofreading:
The definition of optimization used in Risks From Learned Optimization is actually quite different from the definition I’m using here. They say:
“a system is an optimizer if it is internally searching through a search space (consisting of possible outputs, policies, plans, strategies, or similar) looking for those elements that score high according to some objective function that is explicitly represented within the system.”
I personally don’t really like this definition, since it leans quite hard on reifying certain kinds of algorithms—when is there “really” explicit search going on? Where is the search space? When does a configuration of atoms consitute an objective function? Using this definition strictly, humans aren’t *really* optimizers, we don’t have an explicit objective function written down anywhere. Balls rolling down hills aren’t optimizers either.
But by the definition of optimization I’ve been using here, I think pretty much all evolved organisms have to be at least weak optimizers, because survival is hard. You have manage constraints from food and water and temperature and predation etc… the window of action-sequences that lead to successful reproduction are really quite narrow compared to the whole space. Maintaining homeostasis requires ongoing optimization pressure.
Agree that not all optimization processes fundamentally have to be produced by other optimization processes, and that they can crop up anywhere you have the necessary negentropy resevoir. I think my claim is that optimization processes are by default rare (maybe this is exactly because they require negentropy?). But since optimizers beget other optimizers at a rate much higher than background, we should expect the majority of optimization to arise from other optimization. Existing hereditary trees of optimizers grow deeper much faster than new roots spawn, so we should expect roots to occupy a negligible fraction of the nodes as time goes on.