41–50 of 202 posts
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People should learn not to go outside if they're not labelled.
> People should learn not to go outside if they're not labelled. I worry what will happen when this idea breeds with the "why worry about privacy if you've got nothing to hide?" fallacy.
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> This is really scary. No, it's not even remotely "really scary". No one is putting an actual self-driving car on the market using this specific data set. Disingenuous to pretend this is any indication of the data using by serious companies in the space or is represented of the impact a few mislabelled samples have on the ability of these systems & algorithms to generalize.
It's scary when you consider two other factors: First, the AI hype train. People think that calling something "Artificial Intelligence" implies that it is artificial, yes, but also, critically, that it is intelligent. Many enthusiastic people, and also many policymakers, don't fully realize the extent to which machine learning is constrained by both the quality and nature of its training data, and the capabilities of…
If you look at the Uber accident, killing 1 person severly harmed the company.
The only extremely harmful scenario that I can imagine in self driving is a bad remote mass software update (which is sadly possible even in an otherwise great company) causing lots of accidents at the same time in different vehicles.
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People should learn not to go outside if they're not labelled.
What a funny future it'd be, if we have to wear something distinctive (giant QR codes?) so we don't get killed outside. As a side effect it would make tracking us much easier...
Earlier quoted context omitted.
People should learn not to go outside if they're not labelled.
What a funny future it'd be, if we have to wear something distinctive (giant QR codes?) so we don't get killed outside. As a side effect it would make tracking us much easier...
Earlier quoted context omitted.
It's scary when you consider two other factors: First, the AI hype train. People think that calling something "Artificial Intelligence" implies that it is artificial, yes, but also, critically, that it is intelligent. Many enthusiastic people, and also many policymakers, don't fully realize the extent to which machine learning is constrained by both the quality and nature of its training data, and the capabilities of…
I'm not sure what point you're trying to make, but it has nothing to do with some mislabeled examples. Every system using supervised training assets has labeling issues, and the world hasn't ended yet. Take a look at Google Translate. This is only "scary" if you're ignorant of the problem. The OP is selling something, and it's in their best interest to spread FUD to sell it.
But for safety critical systems it's six nines reliability or GTFO.
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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?
Cellophane
Mister Cellophane
Shoulda Been My Name
Mister Cellophane
'Cause You Can Look Right Through Me
Walk Right By Me
And Never Know I'm There...These are manually tagged stills, right? Not video? That's a data set for training CAPCHA breakers, not self-driving. You need to use video, where you get to see the same objects at different ranges. Recognition gets better as you get closer. Then track the recognized objects backwards to when they first appear, and try to recognize them at smaller sizes.
Perception means understanding that just because a truck (that we recognized 3 frames ago) went behind a tree, doesn't mean it ceased being a truck. Furthermore, this knowledge should be used to refine the model, to say "hey I can still see the wheels, I know those wheels were attached to that truck a moment ago, therefore I still know where the truck is, even if I can't recognize it plainly as such right now".
Furthermore, even if it's completely out of view, it's still there, probably moving close to the same speed and track it was. And if its path intersects ours, we need to assume that it'll reappear at some point. And the longer it's out of view, the bigger are the errors on its estimated position, according to our knowledge of the acceleration and braking limits of trucks of that type.
I've never heard of anyone even working towards this sort of perception, much less having achieved it. And until we get there, these things are all toys. Dangerous, legally nebulous toys.
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I'm not sure what point you're trying to make, but it has nothing to do with some mislabeled examples. Every system using supervised training assets has labeling issues, and the world hasn't ended yet. Take a look at Google Translate. This is only "scary" if you're ignorant of the problem. The OP is selling something, and it's in their best interest to spread FUD to sell it.
Nobody gets killed by Google Translate etc. messing up, so no sweat, throw together something that works 99% or the time. But for safety critical systems it's six nines reliability or GTFO.
It's not so much that "nobody gets killed by Google translate messing up" as "when people get killed by Google Translate messing up, I can't tell".
Earlier quoted context omitted.
People should learn not to go outside if they're not labelled.
What a funny future it'd be, if we have to wear something distinctive (giant QR codes?) so we don't get killed outside. As a side effect it would make tracking us much easier...