I can understand Amit Singhal's opposition to replacing hand-coded features with machine learning models. He's right that ML models have bizarre failure cases

I can understand Amit Singhal's opposition to replacing hand-coded features with machine learning models. He's right that ML models have bizarre failure cases across large sample sizes, but he's apparently career-endingly wrong to seemingly believe that one cannot do anything about it. He's also wrong IMO to not recognize that hand-crafted signals and features lack bizarre failure cases themselves. IMO this shifts the focus from lovingly hand-crafted signals and features to lovingly hand-crafted loss functions and variants of boosting and training algorithms to address those bizarre failures as they occur. For example, recently much ado was made about minimal changes to the input data to image recognition convolutional nets to spoof the object ID. And the simplest remedy is to augment the training data with these cases and perhaps boost the gradients of outputs that are wrong. It's not perfect, but Google search was never perfect either. Evidence: I was on the Google search team for a bit and we had all sorts of meetings to address such failures as they happened. While I agree that the quality of Google technical searches has declined dramatically recently, I believe there's huge opportunity to fix them by understanding why the ML models are failing (shooting from the hip, I suspect it's a long-tail problem writ large) and changing the loss functions, models and training algorithms to address these failures as they're detected. Anything less IMO is a failure of imagination in an age of 6.6 TFLOPS for ~$1000 and the ability to stuff 8 of them into a $20K server and go wild.