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The 900lb gorilla in the deep learning room that everyone likes to ignore is that machine learning is horrible at providing corrective action data. Traditional

The 900lb gorilla in the deep learning room that everyone likes to ignore is that machine learning is horrible at providing corrective action data. Traditional machine vision is well adapted to providing statistical data such as "the diameter of the pizza is out of tolerance by 8mm" or "there are supposed to be 22 pepperonis on the pizza, but only 19 were found". Machine learning leans towards "it's not a good pizza" and doesn't provide a lot of additional data.

krisoft 4y ago
> machine learning is horrible at providing corrective action data I don’t recognise the truth in what you are writing. > there are supposed to be 22 pepperonis on the pizza, but only 19 were found Instance segmentation is a solved problem. A properly constructed and trained neural network can tell you exactly how many pepperonies it sees and exactly where. Telling if that is the right number is a trivial problem from there. > the diameter of the pizza is out of tolerance by 8mm Here too, the neural network can recognise the edges of the pizza and then you can fit a shape to it. You can do this second step either with classical algorithms or with a machine learning one. (I would use a classical algorithm if the pizza is meant to be circular or rectangular shaped, and a machine learning algorithm if they are aiming for something weird, like an Italy shaped pizza or something.) > Machine learning leans towards "it's not a good pizza" Sounds like you have only heard of simple classifier models.
- vidanay 4y ago
  I will accept your opinion as I have never implemented a complete ML based solution. All of my opinion is based on promises and demonstrations for ML products such as Cognex VIDI. If those systems have capabilities like you describe, they have not been well presented during their sales pitches.
  - version_five 4y ago
    I've observed that the ML (or otherwise) model quality is not really the bottleneck in most computer vision systems so it gets less attention. The state of the art is way beyond what you would see in an implemented solution (like cognex), but some combination of market immaturity and that not being the biggest problem means there's not much industry demand for really good models
  - krisoft 4y ago
    > All of my opinion is based on promises and demonstrations for ML products such as Cognex VIDI. I see! Thank you for the explanation. That now makes sense. Basically you were talking about what is available on the market as a product, and I was talking about what the state of the art is in machine learning. Now obviously if you actually want to put a factory line together you care about the available products, not what is possible in theory. It is kind of like asking someone if it is possible to travel to the moon. If you are asking a physicist they will do some calculations with the rocket equation and will tell you that it is perfectly possible. If you ask the same question from a travel agent they will tell you it is not possible because they can’t sell you a moon holiday right there and then. They are both right, just in different contexts. > If those systems have capabilities like you describe, they have not been well presented during their sales pitches. All I can tell to those companies is that they should “git gud”. :) Thank you for your explanation about the context you were talking about!
jeffreyrogers 4y ago
There's a lot of progress on this recently with things like conformal prediction.
version_five 4y ago
As the other person said, this is largely a strawman. It would be possible to build some kind of black box system, but it's not mandatory. An OK/NOK pizza classifier could do so without telling you why, and in some measures may be more robust than some kind of filter, threshold, morphological rules, pepperoni counter, but the latter will not tell you if something is wrong with the crust. A pepperoni object detector would be trivial and way more robust than whatever classical pepperoni finder you could build.

krisoft 4y ago

> machine learning is horrible at providing corrective action data I don’t recognise the truth in what you are writing. > there are supposed to be 22 pepperonis on the pizza, but only 19 were found Instance segmentation is a solved problem. A properly constructed and trained neural network can tell you exactly how many pepperonies it sees and exactly where. Telling if that is the right number is a trivial problem from there. > the diameter of the pizza is out of tolerance by 8mm Here too, the neural network can recognise the edges of the pizza and then you can fit a shape to it. You can do this second step either with classical algorithms or with a machine learning one. (I would use a classical algorithm if the pizza is meant to be circular or rectangular shaped, and a machine learning algorithm if they are aiming for something weird, like an Italy shaped pizza or something.) > Machine learning leans towards "it's not a good pizza" Sounds like you have only heard of simple classifier models.

vidanay 4y ago
I will accept your opinion as I have never implemented a complete ML based solution. All of my opinion is based on promises and demonstrations for ML products such as Cognex VIDI. If those systems have capabilities like you describe, they have not been well presented during their sales pitches.
- version_five 4y ago
  I've observed that the ML (or otherwise) model quality is not really the bottleneck in most computer vision systems so it gets less attention. The state of the art is way beyond what you would see in an implemented solution (like cognex), but some combination of market immaturity and that not being the biggest problem means there's not much industry demand for really good models
- krisoft 4y ago
  > All of my opinion is based on promises and demonstrations for ML products such as Cognex VIDI. I see! Thank you for the explanation. That now makes sense. Basically you were talking about what is available on the market as a product, and I was talking about what the state of the art is in machine learning. Now obviously if you actually want to put a factory line together you care about the available products, not what is possible in theory. It is kind of like asking someone if it is possible to travel to the moon. If you are asking a physicist they will do some calculations with the rocket equation and will tell you that it is perfectly possible. If you ask the same question from a travel agent they will tell you it is not possible because they can’t sell you a moon holiday right there and then. They are both right, just in different contexts. > If those systems have capabilities like you describe, they have not been well presented during their sales pitches. All I can tell to those companies is that they should “git gud”. :) Thank you for your explanation about the context you were talking about!

jeffreyrogers 4y ago

There's a lot of progress on this recently with things like conformal prediction.

version_five 4y ago

As the other person said, this is largely a strawman. It would be possible to build some kind of black box system, but it's not mandatory. An OK/NOK pizza classifier could do so without telling you why, and in some measures may be more robust than some kind of filter, threshold, morphological rules, pepperoni counter, but the latter will not tell you if something is wrong with the crust. A pepperoni object detector would be trivial and way more robust than whatever classical pepperoni finder you could build.