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How do you know it solves the problem, instead of just tricking your measure? Do you know when it doesn't work? Business does not care about rigor, but then t
by AstralStorm 7y ago
How do you know it solves the problem, instead of just tricking your measure?
Do you know when it doesn't work?
Business does not care about rigor, but then terrible things happen when your face recognition system happens to not work for people with dark skin tone.
Even more terrible things happen when it detects cats as people, and worse once it's used to link data into a police database.
Even pretty bad things happen when it's used for detecting potential (treated as absolute) copyright violations.
Security holes when it's used for detecting security problems.
Loss of business when it's used for ticket prioritization.
Business world does not care that it sells a broken solution as long as it's not obvious and someone has been paid. Everyone else pays for the failures. And you cannot sue an ML system really.
- dorgo 7y agoWhy don't we do online learning? Each failure is a new training sample. Why dont't we do active learning? Or adversarial networks, or.... Sure it is easy to fail in machine learning. But it doesn't mean that we need to undstand how and what our models learn. That's the only big advantage of machine learning: the model learns, so I don't have to.