  The paper surveys automated scientific discovery, from equation discovery and
symbolic regression to autonomous discovery systems and agents. It discusses
the individual approaches from a "big picture" perspective and in context, but
also discusses open issues and recent topics like the various roles of deep
neural networks in this area, aiding in the discovery of human-interpretable
knowledge. Further, we will present closed-loop scientific discovery systems,
starting with the pioneering work on the Adam system up to current efforts in
fields from material science to astronomy. Finally, we will elaborate on
autonomy from a machine learning perspective, but also in analogy to the
autonomy levels in autonomous driving. The maximal level, level five, is
defined to require no human intervention at all in the production of scientific
knowledge. Achieving this is one step towards solving the Nobel Turing Grand
Challenge to develop AI Scientists: AI systems capable of making Nobel-quality
scientific discoveries highly autonomously at a level comparable, and possibly
superior, to the best human scientists by 2050.
