Archetypal Transfer Learning (ATL) is a proposal by @whitehatStoic for what is argued by the author to be a fine tuning approach that "uses archetypal data" to "embed Synthetic Archetypes". These Synthetic Archetypes are derived from patterns that models assimilate from archetypal data, such as artificial stories. The method yielded a shutdown activation rate of 57.33% in the GPT-2-XL model after fine-tuning. .. (read more)

Conjecture is an alignment startup founded by Connor Leahy, Sid Black and Gabriel Alfour, which aims to scale alignment research... (read more)

Religion is a complex group of human activities — involving commitment to higher power, belief in belief, and a range of shared group practices such as worship meetings, rites of passage, etc... (read more)