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> You can do handwritten digit recognition with 90% accuracy? Sounds pretty good, but if you need to turn that into recognizing a 12 digit account number you no
by mFixman 3y ago
> You can do handwritten digit recognition with 90% accuracy? Sounds pretty good, but if you need to turn that into recognizing a 12 digit account number you now have a 70% chance of getting at least one digit incorrect.
You are assuming that the probability of failure is independent, which couldn't be further from the truth. If a digit recogniser can recognise one of your "hard" handwritten digits, such as a 4 or a 9, it will likely be able to recognise all of them.
The same happens with AI agents. They are not good at some tasks, but really really food at others.
- jhbadger 3y agoAnd the US Post Office and other postal services have been using this tech to sort letters for several decades now (although postal codes with both letters and numbers like Canada's are harder). It was viewed as the "killer app" for ML in the 1990s.
- PheonixPharts 3y agoThis thread is an object lesson in the point I'm making: people have forgotten everything we've learned about making ML based products. Parent comment doesn't understand the concept of expectation, and this comment is apparently unfamiliar with the fact that SotA for digit recognition [0] has been much higher than 90% even in the 90s. 90% accuracy for digit recognition is what you get if you use logistic regression as your model. My point was that numbers that look good in terms of research often aren't close to go enough for the real world. It's not that 90% works for zip codes, it's that in the 90s accuracy was closer to 99%. You have validated my point rather than rejected it. 0. https://en.wikipedia.org/wiki/MNIST_database https://en.wikipedia.org/wiki/MNIST_database
- allanwind 3y agoThe "food" typo is just too good to ignore in this context.