  In this paper, we present our vision of so called zero-shot learning for
databases which is a new learning approach for database components. Zero-shot
learning for databases is inspired by recent advances in transfer learning of
models such as GPT-3 and can support a new database out-of-the box without the
need to train a new model. Furthermore, it can easily be extended to few-shot
learning by further retraining the model on the unseen database. As a first
concrete contribution in this paper, we show the feasibility of zero-shot
learning for the task of physical cost estimation and present very promising
initial results. Moreover, as a second contribution we discuss the core
challenges related to zero-shot learning for databases and present a roadmap to
extend zero-shot learning towards many other tasks beyond cost estimation or
even beyond classical database systems and workloads.
