Hey everyone! I’m Guilhermo. I’m doing an AI timelines internship with Open Phil and am going to investigate that topic over the next few months.
It seems plausible to a lot of people that simply scaling up current ML architectures with more data and compute could lead to transformative AI. In particular, the recent successes of GPT-3 and the impressive scaling observed seem to suggest that a scaled-up language model could have a transformative impact. This hypothesis can be modeled within the framework of Ajeya’s report by considering that a transformative model would have the same effective horizon length as GPT-3 and assuming that the scaling will follow the same laws as current Transformer models. I’ve added an anchor corresponding to this view in an updated version of the quantitative model that can be found (together with the old one) here, where the filenames corresponding to the updated model begin with “(GPT-N)”. Please note that Ajeya’s best guess sheet hasn’t been updated to take this new anchor into account. A couple of minor numerical inconsistencies between the report and the quantitative model were also fixed.