  The overarching goal of this paper is to develop a general model of the state
space of AI. Given the breathtaking progress in AI research and technologies in
recent years, such conceptual work is of substantial theoretical interest. The
present AI hype is mainly driven by the triumph of deep learning neural
networks. As the distinguishing feature of such networks is the ability to
self-learn, self-learning is identified as one important dimension of the AI
state space. Another main dimension lies in the possibility to go over from
specific to more general types of problems. The third main dimension is
provided by semantic grounding. Since this is a philosophically complex and
controversial dimension, a larger part of the paper is devoted to it. We take a
fresh look at known foundational arguments in the philosophy of mind and
cognition that are gaining new relevance in view of the recent AI developments
including the blockhead objection, the Turing test, the symbol grounding
problem, the Chinese room argument, and general use-theoretic considerations of
meaning. Finally, the AI state space, spanned by the main dimensions
generalization, grounding and "selfx-ness", possessing self-x properties such
as self-learning, is outlined.
