And in fact, we do still have approximately zero idea how large neural nets do what they do, interpretability research notwithstanding, as evinced by the fact that not a single person on this planet could code by hand whatever internal algorithms the models have learned.
I now see where the problem lies. The basic issues I see with this argument are as follows:
The implied argument is if you can’t create something by yourself by hand in the field, you know nothing at all about what you are focusing on. This is straightforwardly not true for a lot of fields.
For example, I’d probably know quite a lot about borderlands 3, not perfectly, but I actually have quite a bit of knowledge, and I even could use save editors or cheatware with video tutorials, but under nearly 0 circumstances could I actually create borderlands 3 even if the game with it’s code already existed, even with a team.
This likely generalizes: while neuroscience has some knowledge of the brain, it’s not nearly at the point where it could reliably create a human brain from scratch, knowing some things about what cars do is not enough to create a working car, and so on.
In general, I think the error is that you and Eliezer have too high expectations of what some knowledge will bring you. It helps, but in virtually no cases will the knowledge alone allow you to create the thing you are focusing on.
It’s possible that our knowledge of the AI’s internal work isn’t enough, and that progress is too slow. I might agree or disagree, but at least this would be rational. Right now, I’m seeing basic locally invalid arguments here, and I notice that part of the problem is that you and Eliezer have too much of a binary view on knowledge, where you either have functionally perfect knowledge or no knowledge at all, but usually our knowledge is neither functionally perfect, nor is it zero knowledge.
Edit: This seems conceptually similar to P=NP, in that the problem is that verifying something and making something are conjectured to have very different difficulties, and essentially my claim is that verifying something isn’t equal to generating something.
I now see where the problem lies. The basic issues I see with this argument are as follows:
The implied argument is if you can’t create something by yourself by hand in the field, you know nothing at all about what you are focusing on. This is straightforwardly not true for a lot of fields.
For example, I’d probably know quite a lot about borderlands 3, not perfectly, but I actually have quite a bit of knowledge, and I even could use save editors or cheatware with video tutorials, but under nearly 0 circumstances could I actually create borderlands 3 even if the game with it’s code already existed, even with a team.
This likely generalizes: while neuroscience has some knowledge of the brain, it’s not nearly at the point where it could reliably create a human brain from scratch, knowing some things about what cars do is not enough to create a working car, and so on.
In general, I think the error is that you and Eliezer have too high expectations of what some knowledge will bring you. It helps, but in virtually no cases will the knowledge alone allow you to create the thing you are focusing on.
It’s possible that our knowledge of the AI’s internal work isn’t enough, and that progress is too slow. I might agree or disagree, but at least this would be rational. Right now, I’m seeing basic locally invalid arguments here, and I notice that part of the problem is that you and Eliezer have too much of a binary view on knowledge, where you either have functionally perfect knowledge or no knowledge at all, but usually our knowledge is neither functionally perfect, nor is it zero knowledge.
Edit: This seems conceptually similar to P=NP, in that the problem is that verifying something and making something are conjectured to have very different difficulties, and essentially my claim is that verifying something isn’t equal to generating something.