Gotcha. I agree with 1-4, but I’m not sure I agree with 5, at least I don’t agree that 5 is separate from 4.
In particular:
If we can make an explanation for some AI while we’re training it and this is actually a small increase in cost, then we can apply this to input distribution generator. This doesn’t make training uncompetitive with just the agent as it only adds a small factor to some AI (the generator) that we needed to train anyway. So, we shouldn’t need to waste a bunch of resources on the formal input distribution.
I agree this implies that you have to handle making explanations for AIs trained to predict the input distribution, which causes you to hit issues with 4 again.
I agree 4 and 5 are not really separate. The main point is that using formal input distributions for explanations just passes the buck to explain things about the generative AI that defines the formal input distribution, and at some point something needs to have been trained n real data, and we need to explain behavior there.
Gotcha. I agree with 1-4, but I’m not sure I agree with 5, at least I don’t agree that 5 is separate from 4.
In particular:
If we can make an explanation for some AI while we’re training it and this is actually a small increase in cost, then we can apply this to input distribution generator. This doesn’t make training uncompetitive with just the agent as it only adds a small factor to some AI (the generator) that we needed to train anyway. So, we shouldn’t need to waste a bunch of resources on the formal input distribution.
I agree this implies that you have to handle making explanations for AIs trained to predict the input distribution, which causes you to hit issues with 4 again.
I agree 4 and 5 are not really separate. The main point is that using formal input distributions for explanations just passes the buck to explain things about the generative AI that defines the formal input distribution, and at some point something needs to have been trained n real data, and we need to explain behavior there.