Despite prevalence of computational tools, the fundamental step of making new hypotheses has always been ultimately driven by human insight.  It has been the human scientist at the center stage - doing the “science”, with machines assisting by carrying out routine calculations. The notion of automating scientific discovery is based on the possibility of reversing these roles. In this talk, I will attempt to make a case in the light of new breakthroughs in automated reasoning, zero-knowledge inference, and the new computable metrics for universal similarity and statistical causality.  I show how machine learning may be carried out without choosing features, how we can carry out non-parametric analysis in the absence of prior knowledge and yet infer generative models, and how we can go beyond computing correlations and begin computing causation in data.