Logan Smith

The Long-Term Future Fund has been supporting Logan Smith for 2 years, providing a stipend of $40,000 every 6 months. Recently, Logan and their team published exciting mechanistic interpretability results where they used Sparse Autoencoders (SAEs) to find highly interpretable directions in language models. The fund has also supported Hoagy Cunningham, the first author of the linked paper, for the same work.

This work was quite similar to research later published by Anthropic, which generated significant excitement in the AI safety community (and was a precursor to the recent excitement of Golden Gate Claude). You may find it helpful to read this post comparing their two papers. I believe some of the independent researchers funded by the Long-Term Future Fund to work on SAEs were subsequently hired by Anthropic to continue their interpretability work at Anthropic.

Although I (Linch) am not an expert in the field, my impression is that the work on sparse autoencoders, both from independent researchers and Anthropic, represents some of the most meaningful advances in AI safety and interpretability in 2023.

Note: When an earlier private version of these notes was circulated, a senior figure in technical AI safety strongly contested my description. They believe that the Anthropic SAE work is much more valuable than the independent SAE work, as both were published around the same time, but the Anthropic work provides sufficient evidence to be worth extending by other researchers, whereas the independent research was not dispositive. I find these arguments plausible but not overwhelmingly convincing. Unfortunately, I lack the technical expertise to be well-equipped to form an accurate independent assessment on this matter.