Let me kick things off by posing this question to ML researchers:
What do you think are some important holy grails of machine learning?
– “A classifier with SVM-level performance but much more scalable”
– “Practical confidence bounds (or learning bounds) for classification”
– “A reinforcement learning algorithm that can handle the ___ problem”
– “Understanding theoretically why ___ works so well in practice”
I pose this question because I believe that when goals are stated explicitly and well (thus providing clarity as well as opening up the problems to more people), rather than left implicit, they are likely to be achieved much more quickly. I would also like to know more about the internal goals of the various machine learning sub-areas (theory, kernel methods, graphical models, reinforcement learning, etc) as stated by people in these respective areas. This could help people cross sub-areas.