There have been a few remarks about how my and Vanessa’s posts on Infra-Bayesianism seem interesting, but that it's quite a formidable body of work to chew through. So anyone interested in learning Inframeasure theory to the level of proficiency where they are actually able to develop their own proofs...
This post was written as part of the Stanford Existential Risks Initiative ML Alignment Theory Scholars (MATS) program. thanks to Evan Hubinger for insightful discussion. Introduction Interpretability tools allow us to understand the internal mechanism and knowledge of a model. This post discusses methods through which interpretability could reduce x-risk...
There have been a few remarks about how my and Vanessa’s posts on Infra-Bayesianism seem interesting, but that it's quite a formidable body of work to chew through.
So anyone interested in learning Inframeasure theory to the level of proficiency where they are actually able to develop their own proofs about it and casually deploy it as a tool on other problems is probably not best served by the existing posts. You can only go so far by reading about something without ever applying it.
And so, to help people get a taste for the underlying math, I have constructed a sheet of exercises analogous to Scott's exercise sheets about fixpoints, to guide the... (read more)
This post was written as part of the Stanford Existential Risks Initiative ML Alignment Theory Scholars (MATS) program. thanks to Evan Hubinger for insightful discussion.
Interpretability tools allow us to understand the internal mechanism and knowledge of a model. This post discusses methods through which interpretability could reduce x-risk from advanced AI. Most of these ideas are from Neel Nanda’s post on the same topic, my contribution comes from elaborating on them further. I have split these into a handful of different categories. These are not disjoint, but the framing helped with the analysis. The last section includes several reasons why interpretability might not be impactful that apply, for the most part, across... (read 10805 more words →)