Machine learning (ML) is widely applied in a variety of applications. Despite its great success, it is commonly developed in closed-environment scenarios where some important factors of the learning process hold invariant. However, in many real-world applications, the learning environments are open such that various learning factors can be changing over time. As a result, it is of great importance to develop new machine learning techniques to adapt to open environments.
In a recent article “Open-environment Machine Learning” published at National Science Review , Prof. Zhi-Hua Zhou, from Nanjing University, defined the research scope of open-environment machine learning (or “open ML” for short) and reviewed recent advances on this subject.
Specifically, the article specified four important challenges in open ML and introduced some general principles and strategies. Consider the task of predicting forest disease with sensor signals in a forest.
“It is fundamentally important to enable machine learning models to achieve excellent performance in usual case while keeping satisfactory performance no matter what unexpected unfortunate issues occur. This is crucial for achieving robust artificial intelligence and carries the desired properties of learnware” said Prof. Zhi-Hua Zhou.
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See the article:
Open-environment Machine Learning
https://doi.org/10.1093/nsr/nwac123
National Science Review