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.
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.