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.
For those considering an autodidactic alternative, this is for you. You can't go deeply into every machine learning topic. There's too much to learn, and the field is advancing rapidly. Motivation is far more important than micro-optimizing a learning strategy for some long-term academic or career goal. If you're trying to force yourself forward, you'll slow down.