Reconceiving Machine Learning
Machine learning is a cottage industry, not an engineering discipline.
Nearly every new problem is solved from scratch. There is no reuse of
previous solutions. There is no language to describe problems. Research
technique-oriented rather than problem-oriented.
This lack of modularity and composability limits the field advancing.
Reinvention is rife.
My research agenda is to develop a
composable basis for machine
learning. My detailed plan is partially explained in this formal
research proposal jointly withRalf Herbrich,
Ule von Luxburg and Peter Grünwald . The
project is now funded and underway.
As the proposal says, I want to focus on problems rather than solutions. I
recently came across a great quote that sums up my feeling on this:
The formulation of
a problem is often
more essential than its solution, which may be merely a matter of
mathematical or experimental skill. To raise new questions, new
possibilities, to regard old problems from a new angle, requires
creative imagination and marks real advance in science.
Progress to Date
In order to get a feel for the sort of things done so far, look at
these recent publications.