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Think of computer vision tasks. Until modern deep learning approaches came around, it was built on brittle, explicitly defined pipelines that could break entire
by GuiA 7y ago
Think of computer vision tasks. Until modern deep learning approaches came around, it was built on brittle, explicitly defined pipelines that could break entirely if something minor about the input data changed.
Then the great deep learning wave of 201X happened, replacing dozens/hundreds of carefully defined steps with a more flexible, generalizable approach. The new approach still has limitations and failure cases, but it operates at a scale and efficiency the previous approaches could not even dream of.
- MegaButts 7y agoThat's not redefining the problem, so much as applying a new technology to solve the same problem. Usually using the flashy new technology decreases reliability due to immature tooling, lack of testing, and just general lack of knowledge of the new approach. Also deep learning, while incredibly powerful and useful, is not the magic cure-all to all of computer vision's problems and I have personally seen upper management's misguided belief in this ruin a company (by which I mean they can no longer retain senior staff, they have never once hit a deadline, every single one of their metrics is not where they want it to be, and a bunch of other stuff I can't say without breaking anonymity).