For claim 3, I think we just want to assume that the process we are trying to predict doesn’t have time requirements that are too large for us to make a prediction we are happy with. I think this has to be an assumption about the data we make because it is just genuinely not true of many processes we can conceive of, and I don’t think deep learning would work to predict those processes. Many parts of the real world we care about just turn out to be the efficiently predictable.
“Many parts of the real world we care about just turn out to be the efficiently predictable.”
I had a dicussion about exactly these ‘pockets of computational reducibility’ today. Whether they are the same as the more vague ‘natural abstractions’, and if there is some observation selection effect going on here.
For claim 3, I think we just want to assume that the process we are trying to predict doesn’t have time requirements that are too large for us to make a prediction we are happy with. I think this has to be an assumption about the data we make because it is just genuinely not true of many processes we can conceive of, and I don’t think deep learning would work to predict those processes. Many parts of the real world we care about just turn out to be the efficiently predictable.
“Many parts of the real world we care about just turn out to be the efficiently predictable.”
I had a dicussion about exactly these ‘pockets of computational reducibility’ today. Whether they are the same as the more vague ‘natural abstractions’, and if there is some observation selection effect going on here.