The Best of LessWrong

I wrote this post about a year ago.  It now strikes me as an interesting mixture of 1. Ideas I still believe are true and important, and which are (still) not talked about enough 2. Ideas that were plausible at the time, but are much less so now 3. Claims I made for their aesthetic/emotional appeal, even though I did not fully believe them at the time In category 1 (true, important, not talked about enough): * GPT-2 is a source of valuable evidence about linguistics, because it demonstrates various forms of linguistic competence that previously were only demonstrated by humans. * Much scholarly ink has been spilled over questions of the form "what would it take, computationally, to do X?" -- where X is something GPT-2 can actually do.  Since we now have a positive example, we should revisit these debates and determine which claims GPT-2 disproves, and which it supports. * Some of the key participants in those debates are not revisiting them in this way, and appear to think GPT-2 is entirely irrelevant to their work. In category 2 (plausible then but not now): * "The structure of the transformer is somehow specially apt for language, relative to other architectures that were tried." * I now think this is much less likely thanks to the 2 OpenAI scaling papers in 2020. * The first paper made it seem more plausible that LSTMs would behave like GPT-2 if given a much larger quantity of compute/data * The second paper showed that the things we know about transformers from the text domain generalize very well to image/video/math * I now think transformers are just a "good default architecture" for our current compute regime and may not have special linguistic properties * I'm finding this difficult to phrase, but in 2019 I think I believed Gary Marcus had similar preconceptions to me but was misreading the current evidence. * I now think he's more committed to the idea that GPT-2-like approaches are fundamentally barking up the wrong tree, and wi