  The recent rapid development of artificial intelligence (AI, mainly driven by
machine learning research, especially deep learning) has achieved phenomenal
success in various applications. However, to further apply AI technologies in
real-world context, several significant issues regarding the AI ecosystem
should be addressed. We identify the main issues as data privacy, ownership,
and exchange, which are difficult to be solved with the current centralized
paradigm of machine learning training methodology. As a result, we propose a
novel model training paradigm based on blockchain, named Galaxy Learning, which
aims to train a model with distributed data and to reserve the data ownership
for their owners. In this new paradigm, encrypted models are moved around
instead, and are federated once trained. Model training, as well as the
communication, is achieved with blockchain and its smart contracts. Pricing of
training data is determined by its contribution, and therefore it is not about
the exchange of data ownership. In this position paper, we describe the
motivation, paradigm, design, and challenges as well as opportunities of Galaxy
Learning.
