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I understand then that there's a known and increasing tension in this domain. Instead of seeing it crash and burn, do you see any glimmer of hope? I mean we sti

by soco 3y ago

I understand then that there's a known and increasing tension in this domain. Instead of seeing it crash and burn, do you see any glimmer of hope? I mean we still need science and we still need research, so we need a solution to be able to go on with it, even if it's outside the current framework...

godelski 3y ago
> do you see any glimmer of hope? Oh, of course. Most academics don't do it for the money. It's not like they're paid much. Though that can incentivize cheating in another way because they want money. But there will always be people that don't give a fuck and just want to do good research regardless of the metrics being used. I am insistent that to be a good scientist you must be somewhere on the side of "anti-authority." Because your job relies on challenging concepts, and especially well known and widely agreed upon concepts. Generally look for people who are passionate, will rant, but importantly rant with nuance. Those are the people passionate about their research and not the metrics. The problem is we're just throwing a lot of money down the drain, wasting a lot of time, and generating distrust of the system. Any "crash and burn" is never going to lead to an extinction in any sense. It's just about general society level if we want to do good science or just do noisy science (all science is noisy). But you can never do good science if you remove nuance. This is why I hate that ML (and CS in general) uses conference systems as the main platform for publishing. It is ridiculous to think you can have a good system when it is highly competitive, zero sum, highly time consuming, there's no discussion between authors and reviewers (you may get a one page rebuttal, but your reviewers comments are often disjoint and vague), and you're being judged by those you're competing with. It's just a silly notion to believe this is useful. The solution is actually not hard. I refer to the larger phenomena as Goodhart's Hell. The solution is to stop using metrics as targets. Metrics are guides. If you don't have a deep understanding of what your metric actually measures, how well your metric aligns to the thing you're intending to measure (never 100%), and if you don't understand the biases to your data, you're fucking doomed to this bureaucratic hell. Noise is inherent to complex systems and aggregation is the bane of evaluation of complex feature spaces. Just remember that its models all the way down and that all models are wrong (despite some models being better than others).

by soco 3y ago

godelski 3y ago

> do you see any glimmer of hope? Oh, of course. Most academics don't do it for the money. It's not like they're paid much. Though that can incentivize cheating in another way because they want money. But there will always be people that don't give a fuck and just want to do good research regardless of the metrics being used. I am insistent that to be a good scientist you must be somewhere on the side of "anti-authority." Because your job relies on challenging concepts, and especially well known and widely agreed upon concepts. Generally look for people who are passionate, will rant, but importantly rant with nuance. Those are the people passionate about their research and not the metrics. The problem is we're just throwing a lot of money down the drain, wasting a lot of time, and generating distrust of the system. Any "crash and burn" is never going to lead to an extinction in any sense. It's just about general society level if we want to do good science or just do noisy science (all science is noisy). But you can never do good science if you remove nuance. This is why I hate that ML (and CS in general) uses conference systems as the main platform for publishing. It is ridiculous to think you can have a good system when it is highly competitive, zero sum, highly time consuming, there's no discussion between authors and reviewers (you may get a one page rebuttal, but your reviewers comments are often disjoint and vague), and you're being judged by those you're competing with. It's just a silly notion to believe this is useful. The solution is actually not hard. I refer to the larger phenomena as Goodhart's Hell. The solution is to stop using metrics as targets. Metrics are guides. If you don't have a deep understanding of what your metric actually measures, how well your metric aligns to the thing you're intending to measure (never 100%), and if you don't understand the biases to your data, you're fucking doomed to this bureaucratic hell. Noise is inherent to complex systems and aggregation is the bane of evaluation of complex feature spaces. Just remember that its models all the way down and that all models are wrong (despite some models being better than others).