Julian_R

• Julian_R 20 Nov 2023 5:03 UTC

  13 points

  6

  on: Am I go­ing in­sane or is the qual­ity of ed­u­ca­tion at top uni­ver­si­ties shock­ingly low?

  re institutional incentives, I’ve heard that part of US News rankings are based on asking survey respondents to evaluate other universities by reputation. Professors elsewhere (can only, and do) evaluate other professors based on the quality of their research, not teaching.

  I’m curious, did you check what the quality of teaching would be like at your university before you went? If not, why? If so, why did you pick it anyway?

Re­in­force­ment Learn­ing Goal Mis­gen­er­al­iza­tion: Can we guess what kind of goals are se­lected by de­fault?

• Julian_R 11 Feb 2022 13:48 UTC

  1 point

  in reply to: Julian_R’s comment on: Clar­ify­ing the palata­bil­ity the­ory of obesity

  to clarify, I don’t understand why positive CICO can increase your weight set point but negative CICO can’t decrease it.

• Julian_R 11 Feb 2022 13:46 UTC

  1 point

  on: Clar­ify­ing the palata­bil­ity the­ory of obesity

  Guyenet suspects that our brain’s weight set point might never go down dramatically after living long enough in the modern world, even if we eventually stop eating palatable food altogether. If true, this would make his theory harder to test, and again, his theory would earn a penalty for being more unfalsifiable, but at the same time, we should be clear about what observations his theory strongly predicts, and rapid weight loss on unpalatable diets is just not one of them.

  I don’t understand how CICO can coexist with the idea of a weight set point. If the mechanism of gaining weight is CICO via overeating because food is so palatable, then it seems natural than on unpalatable food you would eat less, and thus I would expect rapid weight loss on unpalatable diets as a prediction of the theory.

• Julian_R 14 Oct 2021 23:35 UTC

  1 point

  in reply to: Buck’s comment on: Red­wood Re­search’s cur­rent project

  I was confused by Buck’s response here because I thought we were going for worst-case quality until I realised:

  1. The model will have low quality on those prompts almost by definition—that’s the goal.

  2. Given that, we also want to have a generally useful model—for which the relevant distribution is ‘all fanfiction’, not “prompts that are especially likely to have a violent continuation”.

  In between those two cases is ‘snippets that were completed injuriously in the original fanfic … but could plausibly have non-violent completions’, which seems like the interesting case to me.

  I suppose one possibility is to construct a human-labelled dataset of specifically these cases to evaluate on.

Ju­lian_R’s Shortform