If you didn’t know about humans, then the max entropy prior would have given terrible predictions about how humans would actually generalize their values. Most utility functions are, indeed, inconsistent with human survival, so you’d have (very confidently) predicted that humans would not want to survive.
The universe is low entropy. Most things relate to each other via approximately low rank interactions. The speed of a sled going down a hill is mostly determined by simple aggregate factors of friction and gravity, rather than complex interactions between all the parts of the sled and all the parts of the surrounding landscape (or the oceans, or distant stars, etc). Most likely, the factors that determine how values generalize are also low rank and low entropy.
We know that neural nets already have a non-uniform prior over hypotheses. They’d not be able to generalize at all if they did. This almost surely extends to their prior over values generalizations.
Note that a max entropy prior over L’ - which you minimally constrain to match the behavior of L on the training distribution—still gives you totally random seeming behavior off distribution.
So the problem with this entire comment thread is that I think I generally agree with you. We’re just talking past each other a little bit.
I know that maximum entropy principle is a bad place to end, but it is a good place to start. Given no other knowledge about something, we should adopt the maximum entropy principle.
So I wanted to start at the baseline of zero knowledge about L’, and then build on top of that.
There are three main reasons:
If you didn’t know about humans, then the max entropy prior would have given terrible predictions about how humans would actually generalize their values. Most utility functions are, indeed, inconsistent with human survival, so you’d have (very confidently) predicted that humans would not want to survive.
The universe is low entropy. Most things relate to each other via approximately low rank interactions. The speed of a sled going down a hill is mostly determined by simple aggregate factors of friction and gravity, rather than complex interactions between all the parts of the sled and all the parts of the surrounding landscape (or the oceans, or distant stars, etc). Most likely, the factors that determine how values generalize are also low rank and low entropy.
We know that neural nets already have a non-uniform prior over hypotheses. They’d not be able to generalize at all if they did. This almost surely extends to their prior over values generalizations.
Note that a max entropy prior over L’ - which you minimally constrain to match the behavior of L on the training distribution—still gives you totally random seeming behavior off distribution.
So the problem with this entire comment thread is that I think I generally agree with you. We’re just talking past each other a little bit.
I know that maximum entropy principle is a bad place to end, but it is a good place to start. Given no other knowledge about something, we should adopt the maximum entropy principle.
So I wanted to start at the baseline of zero knowledge about L’, and then build on top of that.
Edit: deleted last bit