If machine learning techniques keep improving, what's the role of algorithmics in the future?
This is a question that has been haunting me recently, so I am glad you asked it.
However, I am less interested in classifying the application areas for which machine learning will dominate the computational approach than I am in the impact on advancement in the field of (traditional) algorithms.  What kind of "human-crafted algorithms" research will we still be interested in, in 30 years?  Admittedly, this is the harder question to answer in general, because it is the application that determines what quality of result is required.

I think that enumeration algorithms will number among the survivors.  There will continue to be a need to test all inputs to a chip, or conduct an exhaustive search.  The object-specific attention, the finely-tuned design required to generate each object instance exactly (or at least) once; and the running-time amplification of any wasted effort (there may be an exponential number of instances, as a function of the description of the object) -- these factors make me skeptical that a general-purpose automated learning process can match a clever, purpose-designed solution in this area.