alt Hacker News> This is the first approach to activation analysis that I’ve seen that seems like a plausible path to model understanding.
I think an issue is that there is no permanent path to model understanding because of Goodhart's law. Models are motivated to appear aligned (well-trained) in any metric you use on them, which means that if you develop a new metric and train on it, it'll learn a way to cheat on it.
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The obvious fix is to make interpretation of itself a part of the model (like we can explicitly introspect to a certain extent what the brain is doing). Misinterpretation of itself, hopefully, would decrease the system's performance on all tasks and it would be rooted out by training. Of course, it doesn't mean that the fix is easy to implement and that it doesn't have other failure modes.
But that's not how the training works. Goodhart's law isn't magic.
The original model is frozen, so it doesn't learn anything. The copies of the model are learning different objectives and have no incentive to be "loyal" to the original model.
Maybe you're imagining they'll hook this up in some larger training loop, but they haven't done that yet.