It occurred to me today that the time-honored mathematical technique of taking a problem you cannot solve and re-formulating it as a problem (perhaps in a completely different domain) that you can solve, is undergoing a sort of cambrian explosion.
For example, using big data sets and deep learning, machines are getting really good at parsing images of things like cats.
The more general capability is to use a zillion images of things-like-X to properly classify a new image being either like-an-X or not-like-an-X, for any X you like.
But X is not limited to things we can take pictures of. Images don't have to come from cameras. We can create images from any abstraction we like. All we need is an encoding strategy....a Semantic CODEC if you will.
We seem to be hurtling towards large infrastructure that is specifically optimized for image classification. It follows, I think, that if you can re-cast a problem into an image recognition problem - even if it has nothing to do with images - you get to piggy-back on that infrastructure.