The learnware paradigm proposed by Zhou [2016] aims to enable users to reuse
numerous existing well-trained models instead of building machine learning
models from scratch, with the hope of solving new user tasks even beyond
models' original purposes. In this paradigm, developers worldwide can submit
their high-performing models spontaneously to the learnware dock system
(formerly known as learnware market) without revealing their training data.
Once the dock system accepts the model, it assigns a specification and
accommodates the model. This specification allows the model to be adequately
identified and assembled to reuse according to future users' needs, even if
they have no prior knowledge of the model. This paradigm greatly differs from
the current big model direction and it is expected that a learnware dock system
housing millions or more high-performing models could offer excellent
capabilities for both planned tasks where big models are applicable; and
unplanned, specialized, data-sensitive scenarios where big models are not
present or applicable.
This paper describes Beimingwu, the first open-source learnware dock system
providing foundational support for future research of learnware paradigm.The
system significantly streamlines the model development for new user tasks,
thanks to its integrated architecture and engine design, extensive engineering
implementations and optimizations, and the integration of various algorithms
for learnware identification and reuse. Notably, this is possible even for
users with limited data and minimal expertise in machine learning, without
compromising the raw data's security. Beimingwu supports the entire process of
learnware paradigm. The system lays the foundation for future research in
learnware-related algorithms and systems, and prepares the ground for hosting a
vast array of learnwares and establishing a learnware ecosystem.

该论文描述了第一个开源的学软入口系统 Beimingwu，为学软范式未来的研究提供了基础支持。

北明屋：学习舱系统

Beimingwu: A Learnware Dock System

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.

介绍了 learnware 范例，它的目标是使用户无需从头开始构建机器学习模型，使用小模型甚至超越其原始目的的希望。