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Richen the dataset it’s trained on enough so that the model is correct before you release it to prod.
by nolaspring 5y ago
Richen the dataset it’s trained on enough so that the model is correct before you release it to prod.
- IncRnd 5y agoThat's sort of obvious. How do you know that wasn't attempted?
- MBCook 5y agoIf it was we wouldn’t expect this problem to occur, correct?
- belltaco 5y agoThat makes it sound even worse that they knowingly released it without fixing it.
- IncRnd 5y agoI agree. It does seem worse that way.
- ummonk 5y agoAre you saying an ML system should never be released if it doesn’t have perfect accuracy?
- belltaco 5y agoTagging black people as monkeys is not a showstopper bug? If so it makes them look even worse than if it was an overlooked bug.
- urthor 5y agoQuite honestly, in a team that's stressed and run down to the line, checking for that particular classification in a model that has hundreds of classification targets can be really tricky. Say you have 1000 classification targets. You have to produce a model that checks, for each target, the odds of it being classified as one of other 999. You have to check, specifically, for "adult male as primate" out of a million potential combinations. And apply secondary business rules or optimizations to prevent that classifications. So yes it's possible, but it's not cheap, simple or easy. Facebook just decided to shove the model out the door and not worry about the consequences. Quality engineering work, costs money and time. Facebook didn't spend it.
- ironmagma 5y agoEven if it was fixed, in a probabilistic system like this, isn't it basically guaranteed to happen with some inputs?
- IncRnd 5y agoIs that a real question? Of course it will happen. In this particular case there was a single misclassified video reported in the article.
- ironmagma 5y agoYes it's a real question, since there's nothing that says that a particular misclassification must happen. Watching cars go by on the road, one might suspect that at least one is driven by an alligator, but nothing says that it must be, per se, even the law of large numbers.
- IncRnd 5y agoNobody said this particular misclassification must occur. But there will be misclassifications, which is what your original question asked. Since you know the answer, why ask the question? That's why I asked you if what you asked was a real question.
- ironmagma 5y agoYes they did, I said that. But it was a claim made as a question, because I didn't know whether it was actually true. I still can't demonstrate formally why this would be so, because again, the reasoning and even veracity of the claim is still in question due to lack of anything but a hand-waved answer.
- IncRnd 5y agoThere is no need to formally demonstrate. The veracity is clearly not in question. It must be true, due to the existence of the article we are commenting on now. If you want to argue for the general case, you can simply prove the negation is false. Since it is incorrect to say that a network trained with a tiny percentage of possible inputs will never misclassify, it is true that a network trained in such a way will eventually misclassify. This is bolstered by training any network and seeing they always will misclassify something. > Yes they did, I said that. You didn't say that. You said a misclassification would happen on some inputs. That is different from saying on these specific inputs.
- shadowgovt 5y agoWe don't actually know how to do that, or how rich is "rich enough." It's an open avenue of research to be able to extrapolate how well-tuned a neutral net is on data not in its training set. Not to imply the problem is unsolvable, just that if an institution has zero tolerance for this mistake, the fix your describing is no guarantee it won't occur.
- 6gvONxR4sf7o 5y agoThat’s not quite complete, right? It’s that we don’t know how to do that without sacrificing other things.