Geoffrey Hinton talk "What is wrong with convolutional neural nets ?"

I have a hypothesis that these errors in interpretation stem mainly from the max pooling layers that occurs in nearly all CNNs today. Simply cutting out a large fraction (at least 75%!) of the information passing through each pooling layer is making the models quite brittle. That, and obviously it would be a big help to be able to interpret meaningful symbols (e.g. words) from the images rather than just recognize a certain arbitrary class of pattern activations. That is pretty hard, of course, but IMO essential for the upcoming self-driving car revolution, if it is to succeed. G. Hinton has some ideas though they aren't very fleshed out yet.[1] [1] [this video]

I'm certain that AI business will plateau and there will be yet another AI winter without new big breakthroughs in fundamental research. We are currently living in a era where the results from Hinton & Canadian Mafia are still begin adopted and refined. It's just alternating layers of affine transformations and nonlinearity with lots of tricks and improved routing. There needs to be fundamental theoretical breakthrough in every decade or so, and not just refinements and new applications. Hinton made his "What is wrong with convolutional neural nets" speech years ago " [this video] I believe that Hinton's capsule networks type attempts are what we need more. They may not deliver immediately impressive results because fundamental research involves lots of setbacks.