6 ms·

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 labelin

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.

semi-extrinsic 7y ago
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.
- thaumasiotes 7y ago
  > Nobody gets killed by Google Translate etc. messing up, so no sweat 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".
  - perl4ever 7y ago
    I think Google Maps is more relevant to self driving. Google Maps has become, in my opinion, the best way to navigate, far superior to any other GPS-enabled option I know of. But the better it gets, the more obvious it is that you can't rely on it totally. If self-driving was feasible tomorrow or in the next decade, navigation would be much better. And if anyone could do it, Google would. Think of the trope about idiots driving into lakes or off the road or whatever because they were following navigation. And now think about unconditionally trusting Google Maps or anything else, for say, a year. You know it's not up to that standard. I think you can debate whether it needs to be an order of magnitude better, or multiple orders of magnitude better.
- tomnipotent 7y ago
  Again, how does that have anything to do with a public data set? The amount of pure BS FUD in this topic is astounding.
gugagore 7y ago
That's probably the least charitable interpretation possible. No idea who the OP is, in any case. It's scary that the only way we know how to build something that detects pedestrians in an image with any kind of reliability is to use training data. You: All training data has labeling issues. Me: Training data is the only way we know how to build some aspects of systems. Other people here: Some of these systems are safety-critical. I doesn't feel like FUD to be concerned that we need to have strategies for mitigating quality issues in training data. That if you don't have anything like that, then you cannot be in the market of building a safety-critical system. As you have identified, labeling issues are always there. Kind of how human error is always there. Okay, so now that you believe that, you have to find ways to mitigate it. But when I hang around people who do self-driving cars, they don't have a good answer to how to mitigate this risk. Some of them don't even really believe in it (which is different than being ignorant of the problem), because they argue with enough data things will get good enough. It's all really sloppy. Instead of just saying "This is FUD", why not tell us why you feel so reassured that this is not a problem? So far, you said "this always has been a problem and always will be a problem", "the world hasn't ended yet" and "Google translate". What? re: The world hasn't ended yet: okay, but we also haven't been building self-driving cars
- tomnipotent 7y ago
  > No idea who the OP is, in any case. The person that originally posted this thread that works for the company that's selling this FUD. At the bottom of the blog post: "Roboflow accelerates your computer vision workflow through automated annotation quality assurance" This is a non-issue exasperated by a for-profit corporation that's creating an "issue" they conveniently have a service to help you fix.
  - wpietri 7y ago
    I think you missed the key part of the post you're replying to: > tell us why you feel so reassured that this is not a problem Because to me you're coming across as the old-school "you don't need QA if your code is perfect" type.

semi-extrinsic 7y ago

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.

thaumasiotes 7y ago
> Nobody gets killed by Google Translate etc. messing up, so no sweat 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".
- perl4ever 7y ago
  I think Google Maps is more relevant to self driving. Google Maps has become, in my opinion, the best way to navigate, far superior to any other GPS-enabled option I know of. But the better it gets, the more obvious it is that you can't rely on it totally. If self-driving was feasible tomorrow or in the next decade, navigation would be much better. And if anyone could do it, Google would. Think of the trope about idiots driving into lakes or off the road or whatever because they were following navigation. And now think about unconditionally trusting Google Maps or anything else, for say, a year. You know it's not up to that standard. I think you can debate whether it needs to be an order of magnitude better, or multiple orders of magnitude better.
tomnipotent 7y ago
Again, how does that have anything to do with a public data set? The amount of pure BS FUD in this topic is astounding.

gugagore 7y ago

That's probably the least charitable interpretation possible. No idea who the OP is, in any case. It's scary that the only way we know how to build something that detects pedestrians in an image with any kind of reliability is to use training data. You: All training data has labeling issues. Me: Training data is the only way we know how to build some aspects of systems. Other people here: Some of these systems are safety-critical. I doesn't feel like FUD to be concerned that we need to have strategies for mitigating quality issues in training data. That if you don't have anything like that, then you cannot be in the market of building a safety-critical system. As you have identified, labeling issues are always there. Kind of how human error is always there. Okay, so now that you believe that, you have to find ways to mitigate it. But when I hang around people who do self-driving cars, they don't have a good answer to how to mitigate this risk. Some of them don't even really believe in it (which is different than being ignorant of the problem), because they argue with enough data things will get good enough. It's all really sloppy. Instead of just saying "This is FUD", why not tell us why you feel so reassured that this is not a problem? So far, you said "this always has been a problem and always will be a problem", "the world hasn't ended yet" and "Google translate". What? re: The world hasn't ended yet: okay, but we also haven't been building self-driving cars

tomnipotent 7y ago
> No idea who the OP is, in any case. The person that originally posted this thread that works for the company that's selling this FUD. At the bottom of the blog post: "Roboflow accelerates your computer vision workflow through automated annotation quality assurance" This is a non-issue exasperated by a for-profit corporation that's creating an "issue" they conveniently have a service to help you fix.
- wpietri 7y ago
  I think you missed the key part of the post you're replying to: > tell us why you feel so reassured that this is not a problem Because to me you're coming across as the old-school "you don't need QA if your code is perfect" type.