> the deep learning approach uncovers all kinds of hidden features and relationships automatically that a team of humans might miss
sitting in a lecture from a decent DeepLearning practitioner, there were two questions from the audience (among others). The first question asked "How can we check the results using other models, so that computers will catch the errors that humans miss?"
The second question was more like "when a model is built across a non-trivial input space, the features and classes that come out are one set of possibilities, but there are many more possibilities. How can we discover more about the model that is built, knowing that there are inherent epistemological conflicts in any model?"
I also thought it was interesting that the two questioners were from large but very different demographic groups, and at different stages of learning and practice (the second question was from a senior coder).
sitting in a lecture from a decent DeepLearning practitioner, there were two questions from the audience (among others). The first question asked "How can we check the results using other models, so that computers will catch the errors that humans miss?"
The second question was more like "when a model is built across a non-trivial input space, the features and classes that come out are one set of possibilities, but there are many more possibilities. How can we discover more about the model that is built, knowing that there are inherent epistemological conflicts in any model?"
I also thought it was interesting that the two questioners were from large but very different demographic groups, and at different stages of learning and practice (the second question was from a senior coder).