Deep learning is hitting a wall?
I like Gary Marcus as a personality and I look out for his work. Recently he's been doing this thing where he lists examples of deep learning failures (which are trivially easy to find) and then proposing symbolic / causal learning as an alternative. This can be somewhat repetitive, but it can also illustrate some important ideas about and limitations of the current paradigm.I think that the best way to communicate the message that deep learning isn't enough is to use a different approach to achieve superior results. Ideally something that makes a lot of money quickly. There are only so many people that read nautil.us articles. But everyone pays attention to things that make a lot of money.I can point out many conceptual flaws in the internal combustion engine. And I can also propose other types of engines as alternatives. But people don't really start paying attention until someone makes a Tesla. If symbol manipulation can do a better job of identifying pictures of rabbits or humans holding stop signs, then let's see it.