Er, now I see that Eliezer’s post is discussing finite sets of physical laws, which rules out the cosmological horizon diagonalization. But, I think this causal models as function mapping fails in another way: we can’t predict the n in n-valued future experiential states. Before the camera was switched, B9 would assign low probability to these high n-valued experiences. If B9 can get a camera that allows it to perceive color, it could also get an attachment that allows it to calculate the permittivity constant to arbitrary precision. Since it can’t put a bound on the number of values in the L states, the set is uncountable and so is the set of functions.
we can’t predict the n in n-valued future experiential states.
What? Of course we can—it’s much simpler with a computer program, of course. Suppose you have M bits of state data. There are 2^M possible states of experience. What I mean by n-valued is that there are a certain discrete set of possible experiences.
If B9 can get a camera that allows it to perceive color, it could also get an attachment that allows it to calculate the permittivity constant to arbitrary precision.
Arbitrary, yes. Unbounded, no. It’s still bounded by the amount of physical memory it can use to represent state.
In order to bound the states at a number n, it would need to assign probability zero to ever getting an upgrade allowing it to access log n bytes of memory. I don’t know how this zero-probability assignment would be justified for any n—there’s a non-zero probability that one’s model of physics is completely wrong, and once that’s gone, there’s not much left to make something impossible.
Er, now I see that Eliezer’s post is discussing finite sets of physical laws, which rules out the cosmological horizon diagonalization. But, I think this causal models as function mapping fails in another way: we can’t predict the n in n-valued future experiential states. Before the camera was switched, B9 would assign low probability to these high n-valued experiences. If B9 can get a camera that allows it to perceive color, it could also get an attachment that allows it to calculate the permittivity constant to arbitrary precision. Since it can’t put a bound on the number of values in the L states, the set is uncountable and so is the set of functions.
What? Of course we can—it’s much simpler with a computer program, of course. Suppose you have M bits of state data. There are 2^M possible states of experience. What I mean by n-valued is that there are a certain discrete set of possible experiences.
Arbitrary, yes. Unbounded, no. It’s still bounded by the amount of physical memory it can use to represent state.
In order to bound the states at a number n, it would need to assign probability zero to ever getting an upgrade allowing it to access log n bytes of memory. I don’t know how this zero-probability assignment would be justified for any n—there’s a non-zero probability that one’s model of physics is completely wrong, and once that’s gone, there’s not much left to make something impossible.