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The word "objective" is problematic here. Normally people mean by this term "explainable by some quantitative criteria". In the case of AI this is technically t
by zwkrt 8y ago
The word "objective" is problematic here. Normally people mean by this term "explainable by some quantitative criteria". In the case of AI this is technically true, but seems to lack substance. the workings of a neural networks for facial recognition could be decomposed to some god-awful switch statement involving all the pixels of the input picture (after all, this is exactly the structure of the resultant machine instructions), but how would one go about justifying any of the individual branches of such a structure? In other words, could you comment the code? I'm not 100% disagreeing with you, I just think it is problematic to call a statistical process "objective".
- bko 8y agoModern facial recognition uses landmarks, much like humans do. First it identifies a face, which is essentially a flat 2-d plane. It's able to identify locations of the nose, ears, mouth, chin, etc. It then looks for relationships between these features (e.g. how wide is the mouth, how far apart are the eyes, etc). It's a much simpler problem than the general image detection. Imagine how many different types of dogs you can encounter at various angles, sizes, colors, perspectives. For that it's much more of a black box as your model would have to support detecting a dog from behind, side, front, above, sitting down, walking, jumping, standing, dogs with long tails, short tails, long hair, short hair, etc. Now think about a face. Most faces are very much alike. https://github.com/ageitgey/face_recognition https://github.com/ageitgey/face_recognition