[darksaints]

Machine learning is extremely useful, used in hundreds of industries for probably a million different things. Most of these uses are not exciting.

The latest trendy forms of machine learning, which are all some form of deep learning neural network, are pushing beyond the boundaries of human capability, but for a fairly narrow set of usecases. Some people get excited cause they can exceed human capabilities for some object recognition type of task, and then end up thinking that SkyNet is around the corner and they rightfully get called out for it.

You should know though that it is rarely the experts that are guilty of overhyping. It's usually VCs, or product managers, or marketers, or regular software engineers that took an intro class on Coursera where they were told exactly how to solve a problem but haven't yet been exposed to how hard it is to generalize.

With deep neural networks in particular, all of the new innovations have come from novel neural connection topologies. Most of the successful new topologies are the result of attempts to model biological function of some sorts, but that is just the tip of the iceberg. With neuron counts technically unbounded, and the topological search space essentially being the factorial of the neuron count, we will never fully explore the capabilities of neural networks, and only an infinitessimally tiny fraction of those would ever be useful in any circumstance. So deep learning is still extremely exciting because the opportunities are so boundless, yet still extremely disappointing because of how hard it is to find anything useful.