It does not make use aware of a new class of errors. Labeling issues is nothing new, but plenty of systems trained on them continue to work just fine. This is FUD.
Is there any statistical/mathematical tool to completely eradicate or greatly diminish the effects of bad labeling? Is there any reason - other than the combination of pure circumstance and gut feeling of the Data Scientist in charge of saying that it's good enough to deploy - that ~33% insanity in training doesn't become ~33% insanity in the system?
Not sure about bad labels, but semi-supervised learning is the term for training on data with a lot of missing labels. Essentially the algorithm makes predictions on the unlabeled data and uses its highest confidence predictions as additional training data. Generative models can also "dream up" entirely new training examples. There is a risk of amplifying the confidence in bad predictions, but it works well overall (better than using only the labeled portion of the data).
People who talk about the danger of humans driving cars always seem to talk about the raw numbers, because humans drive cars a lot and the raw numbers are rather large.
But when we talk about automated driving, it's in percentages, because it's not being done on the same scale.
So to compare apples to apples, you'd have to convert the number of fatalities to an accuracy percentage. Have you considered trying? There is certainly more than one way to do it, but it would greatly contribute to the discussion if you made some attempt.
> you'd have to convert the number of fatalities to an accuracy percentage
Telsa's early results for their very limited "self-driving" technology has shown a huge reduction in accidents for any given period of time the vehicles are on the road.
1. You can't have an infinite amount of good labels
2. Humans are in charge of labeling too.
The question is if you can reliably overcome the number of bad labels in your training set, so that 33% of bad labels equates to <33% "insanity" in the system.
How nonlinear are we talking? My understanding is probably closer to the truth than to the opposite of the truth. I'm looking for an estimate of how far from the truth I am.
How would a system reliably discredit missing labels while still learning from good labels? The simplest solution would be that system is able to spot the bad/missing labels itself with some certainty, but that seems like a catch 22.
How does making up something ridiculous like "33% insanity" give you anything that's resembles a subject that we can discuss? Hyperbole in, hyperbole out.