Jessica Rumbelow

AI interpretability researcher

Wiki Contributions


Thanks for the comment! I'll respond to the last part:

"First, developing basic insights is clearly not just an AI safety goal. It's an alignment/capabilities goal. And as such, the effects of this kind of thing are not robustly good."

I think this could certainly be the case if we were trying to build state of the art broad domain systems, in order to use interpretability tools with them for knowledge discovery – but we're explicitly interested in using interpretability with narrow domain systems. 

"Interpretability is the backbone of knowledge discovery with deep learning": Deep learning models are really good at learning complex patterns and correlations in huge datasets that humans aren't able to parse. If we can use interpretability to extract these patterns in a human-parsable way, in a (very Olah-ish) sense we can reframe deep learning models as lenses through which to view the world, and to make sense of data that would otherwise be opaque to us.

Here are a couple of examples:

Are you concerned about AI risk from narrow systems of this kind?

Thanks! Unsure as of yet – we could either keep it proprietary and provide access through an API (with some free version for select researchers), or open source it and monetise by offering a paid, hosted tier with integration support. Discussions are ongoing. 

This isn't set in stone, but likely we'll monetise by selling access to the interpretability engine, via an API. I imagine we'll offer free or subsidised access to select researchers/orgs.  Another route would be to open source all of it, and monetise by offering a paid, hosted version with integration support etc.

Good questions. Doing any kind of technical safety research that leads to better understanding of state of the art models carries with it the risk that by understanding models better, we might learn how to improve them. However, I think that the safety benefit of understanding models outweighs the risk of small capability increases, particularly since any capability increase is likely heavily skewed towards model specific interventions (e.g. "this specific model trained on this specific dataset exhibits bias x in domain y, and could be improved by retraining with more varied data from domain y", rather than "the performance of all of models of this kind could be improved with some intervention z"). I'm thinking about this a lot at the moment and would welcome further input. 

Aha!! Thanks Neel, makes sense. I’ll update the post

Yeah! Basically we just perform gradient descent on sensibly initialised embeddings (cluster centroids, or points close to the target output), constrain the embeddings to length 1 during the process, and penalise distance from the nearest legal token. We optimise the input embeddings to maximise the -log prob of the target output logit(s). Happy to have a quick call to go through the code if you like, DM me :)

This link: says that token embeddings are normalised to length 1, but a quick inspection of the embeddings available through the huggingface model shows this isn't the case. I think that's the extent of our claim. For prompt generation, we normalise the embeddings ourselves and constrain the search to that space, which results in better performance. 

Interesting! Can you give a bit more detail or share code?

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