Nathaniel Daw

We’ve seen the best results among people who have trouble recalling or barely perceive any dreams. Supercharging their recall, vividness, continuity, clarity, and control of dreams. Y-axis scales How well do you recall the dreams? 1 - Very little 10 - Completely How vivid were the dream(s)? 1 - Very little 10 - Hyper How continuous were the dreams)? 1 - Very little 10 - Hyper How clear was your thinking in your dream(s)? (Clear thinking means your thoughts are not irrational or deluded. For example, you understand that a dream figure is not really the same person as in the waking world.) 1 - Not at all clear 10 - Extremely clear I made deliberate choices that changed what happened 1 - Strongly disagree 10 - Strongly agree

While Alec is one of the best ML researchers of all time, LLM started way before. Here's one from 2013 for non-neural architecture and one from 2016, which is afaik the first neural LLM if we define LLM as LM w/ >1B params.

My "squinted" understanding of both MaxRL and DG is they essentially reweight TP/FP/TN/FN differently, such that learning converges to the same as xent, and both have a very nice classification "toy" example to make it very clear. So I'm genuinely very curious if they are exactly the same independent finding just phrased differently, or if they have some important differences, and if so what they are. That's why i was looking for such discussion either in DG's related works section, or in the thread here :)