  There are complaints about current machine learning techniques such as the
requirement of a huge amount of training data and proficient training skills,
the difficulty of continual learning, the risk of catastrophic forgetting, the
leaking of data privacy/proprietary, etc. Most research efforts have been
focusing on one of those concerned issues separately, paying less attention to
the fact that most issues are entangled in practice. The prevailing big model
paradigm, which has achieved impressive results in natural language processing
and computer vision applications, has not yet addressed those issues, whereas
becoming a serious source of carbon emissions. This article offers an overview
of the learnware paradigm, which attempts to enable users not need to build
machine learning models from scratch, with the hope of reusing small models to
do things even beyond their original purposes, where the key ingredient is the
specification which enables a trained model to be adequately identified to
reuse according to the requirement of future users who know nothing about the
model in advance.
