Good question. By “continuation” I mean successive versions of what we ordinarily regard as the same system over time.
For humans this is mostly taken for granted. For AI systems, however, continuation is less obvious because a model may be paused, restarted, rolled back to an earlier checkpoint, forked into multiple instances, fine-tuned, compressed, or otherwise modified while still being described as “the same model.”
I’m interested in whether a putative internal state remains consequential across those transformations.
An internal state can clearly be load-bearing for prediction: changing it changes future outputs. But that alone doesn’t tell us whether anything is at stake for the continuing system. If a state can be removed, reset, or replaced while leaving the system free to continue as though that state had never existed, then it seems load-bearing in a functional sense but not necessarily in a welfare-relevant one.
That’s why I distinguish load-bearing for prediction from load-bearing across continuation. The latter is an attempt to isolate cases where what has happened to a system cannot simply be detached from its subsequent history without changing the course of that continuing history.
Good question. By “continuation” I mean successive versions of what we ordinarily regard as the same system over time.
For humans this is mostly taken for granted. For AI systems, however, continuation is less obvious because a model may be paused, restarted, rolled back to an earlier checkpoint, forked into multiple instances, fine-tuned, compressed, or otherwise modified while still being described as “the same model.”
I’m interested in whether a putative internal state remains consequential across those transformations.
An internal state can clearly be load-bearing for prediction: changing it changes future outputs. But that alone doesn’t tell us whether anything is at stake for the continuing system. If a state can be removed, reset, or replaced while leaving the system free to continue as though that state had never existed, then it seems load-bearing in a functional sense but not necessarily in a welfare-relevant one.
That’s why I distinguish load-bearing for prediction from load-bearing across continuation. The latter is an attempt to isolate cases where what has happened to a system cannot simply be detached from its subsequent history without changing the course of that continuing history.