Daniel Murfet

[Cross-posted from the Alignment Journal's blog] The Alignment Journal is beginning to invite the authors of select papers to submit their work for review. If you are interested in participating as an action editor or a reviewer, make an account on our website; if you have a manuscript that you...

We are excited to announce that Resolution (fka Sequent) has a $160M grant from Coefficient Giving (cG) to put rigorous alignment research on a (closer to) even footing with the frontier labs. We will use it to accelerate progress towards higher-confidence alignment, or to find evidence and obstacles showing why...

EDIT: We originally launched under the name Sequent. Read why we renamed to Resolution. Alignment is not on track Artificial superintelligence (ASI) may be developed in the next few years. It is unclear whether alignment is on track to be ready on the same timeframe. At a minimum, the empirical...

> TLDR: We made substantial progress in 2024: > > * We published a series of papers that verify key predictions of Singular Learning Theory (SLT) [1, 2, 3, 4, 5, 6]. > * We scaled key SLT-derived techniques to models with billions of parameters, eliminating our main concerns around...

TLDR: We’re hiring two research assistants to work on advancing developmental interpretability and other applications of singular learning theory to alignment. About Us Timaeus’s mission is to empower humanity by making breakthrough scientific progress on alignment. Our research focuses on applications of singular learning theory to foundational problems within alignment,...

> TLDR: This post accompanies The Developmental Landscape of In-Context Learning by Jesse Hoogland, George Wang, Matthew Farrugia-Roberts, Liam Carroll, Susan Wei and Daniel Murfet (2024), which shows that in-context learning emerges in discrete, interpretable developmental stages, and that these stages can be discovered in a model- and data-agnostic way...

> TLDR: The simplicity bias in Bayesian statistics is not just a bias towards short description length. The folklore relating the simplicity bias in Bayesian statistics to description length is incomplete: while it is true that the fewer parameters you use the better, the true complexity measure which appears in...