Deep learning is hitting a wall?
I like Gary Marcus as a personality and I look out for his work. Recently he&#x27;s been doing this thing where he lists examples of deep learning failures (which are trivially easy to find) and then proposing symbolic &#x2F; 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.<p>I think that the best way to communicate the message that deep learning isn&#x27;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.<p>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&#x27;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&#x27;s see it.