orthonormal

Epistemic Status: I only know as much as anyone else in my reference class (I build ML models, I can grok the GPT papers, and I don't work for OpenAI or a similar lab). But I think my thesis is original. Related: Gwern on GPT-3 For the last several years, I've gone around saying that I'm worried about transformative AI, an AI capable of making an Industrial Revolution sized impact (the concept is agnostic on whether it has to be AGI or self-improving), because I think we might be one or two cognitive breakthroughs away from building one. GPT-3 has made me move up my timelines, because it makes me think we might need zero more cognitive breakthroughs, just more refinement / efficiency / computing power: basically, GPT-6 or GPT-7 might do it. My reason for thinking this is comparing GPT-3 to GPT-2, and reflecting on what the differences say about the "missing pieces" for transformative AI. My Thesis: The difference between GPT-2 and GPT-3 has made me suspect that there's a legitimate comparison to be made between the scale of a network architecture like the GPTs, and some analogue of "developmental stages" of the resulting network. Furthermore, it's plausible to me that the functions needed to be a transformative AI are covered by a moderate number of such developmental stages, without requiring additional structure. Thus GPT-N would be a transformative AI, for some not-too-large N, and we need to redouble our efforts on ways to align such AIs. The thesis doesn't strongly imply that we'll reach transformative AI via GPT-N especially soon; I have wide uncertainty, even given the thesis, about how large we should expect N to be, and whether the scaling of training and of computation slows down progress before then. But it's also plausible to me now that the timeline is only a few years, and that no fundamentally different approach will succeed before then. And that scares me. Architecture and Scaling GPT, GPT-2, and GPT-3 use nearly the same architecture; each