In this position paper, we analyze ways that a human can best be involved in interactive artificial learning against a backdrop of traditional AI programming and conventional artificial learning. Our primary claim is that interactive artificial learning can produce a higher return on human investment than conventional methods, meaning that performance of the agent exceeds performance of traditional agents at a lower cost to the human. This claim is clarified by identifying metrics that govern the effectiveness of interactive artificial learning. We then present a roadmap for achieving this claim, identifying ways in which interactive artificial learning can be used to improve each stage of training an artificial agent: configuring, planning, acting, observing, and updating. We conclude by presenting a case study that contrasts programming using conventional artificial learning to programming using interactive artificial learning.
Why is it so hard to install deep Learning / Neural Network libraries? I switched to Linux because a lot of different sources indicate that a installation on windows/osx is even harder. First I tried to install Caffe. But after a while I had to give up. After that I convinced myself that I could live without c and tried a python environment.