leogao's Shortform

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some lessons from ml research:

any shocking or surprising result in your own experiment is 80% likely to be a bug until proven otherwise. your first thought should always be to comb for bugs.
only after you have ruled out bugs do you get to actually think about how to fit your theory to the data, and even then, there might still be a hidden bug.
most papers are terrible and don't replicate.
most techniques that sound intuitively plausible don't work.
most techniques only look good if you don't pick a strong enough baseline.
an actually good idea can take many tries before it works.
once you have good research intuitions, the most productive state to be in is to literally not think about what will go into the paper and just do experiments that satisfy your curiosity and. convince yourself that the thing is true. once you have that, running the final sweeps is really easy
most people have no intuition whatsoever about their hardware and so will write code that is horribly inefficient. even learning a little bit about hardware fundamentals so you don't do anything obviously dumb is super valuable
in a long and complex enough project, you will almost certainly have a bug that invalidates weeks

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I agree and this is why research grant proposals often feel very fake to me. I generally just write up my current best idea / plan for what research to do, but I don't expect it actually pan out that way and it would be silly to try to stick rigidly to a plan.

Strongly agree. Exact same experience in research, but in finance / quant trading.

7testingthewaters5mo

Can basically attest to all of these, been doing intensive ML upskilling for the last half a year and almost all of these have been true. Highlights include: * Not properly setting up the attention mechanism in multiple experiments, resulting in the conclusion that attention didn't do much (lmao) * So, so many off-by-one, off-by-two errors, especially for next-token prediction setups * Entire series of weeks-long experiments that turn out to be completely useless (usually based on a seemingly-reasonable intuition of some kind) * Accidentally overwriting/resetting the residual element so the RNN was just an NN with a funky hat on * I now hate shapes, reshaping, squeezing, unsqueezing, devices, torch.nn.functional.pad, so many more functions * Using the wrong loss function * Using the right loss function but with the wrong reduction * Using the right loss function but the learning rate is too aggressive/too low/the optimiser is not initialised properly * Using all the right things but loading the model from an incorrect checkpoint/not saving the weights properly * And also learning that google colab was forged in mount doom, a tool of great power crafted with malicious intent.

5Leon Lang5mo

Are you using einops and einsum? I hate these somewhat less since using them. See here for more details.

4Mitchell_Porter5mo

Be right back, just adding all that to my "AI researcher" prompt

3Eli Tyre5mo

Research sounds really finicky and tedious.

2Garrett Baker5mo

I will add: 80% likely to be a bug, or a result from random-matrix theory.

i recently ran into to a vegan advocate tabling in a public space, and spoke briefly to them for the explicit purpose of better understanding what it feels like to be the target of advocacy on something i feel moderately sympathetic towards but not fully bought in on. (i find this kind of thing very valuable for noticing flaws in myself and improving; it's much harder to be perceptive of one's own actions otherwise). the part where i am genuinely quite plausibly persuadable of his position in theory is important; i think if i had talked to e.g flat earthers one might say my reaction is just because i'd already decided not to be persuaded. several interesting things i noticed (none of which should be surprising or novel, especially for someone less autistic than me, but as they say, intellectually knowing things is not the same as actual experience):

this guy certainly knew more about e.g health impacts of veganism than i did, and i would not have been able to hold my own in an actual debate.
- in particular, it's really easy for actually-good-in-practice heuristics to come out as logical fallacies, especially when arguing with someone much more familiar with the object level details th

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2Tiuto1mo

I don't think it would be that hard to refute flat earthers. One or two facts about how the sun travels, that the atmosphere bends light, and the fact that there are commercial flights crossing the poles seem like they would be sufficient to me. This probably won't convince a flat earther, but I think you could fairly easily convince 95% of smart unbiased 3ed listeners (not that they exist). You don't have to go down every option in their argument tree, finding one argument they are completely unable to refute can be enough.

LESSWRONG
LW

LESSWRONG
LW

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