Learnware: on the future of machine learning
Current machine learning techniques have achieved great success; however, there are many deficiencies. First, to train a strong model, a large amount of training examples are required, whereas collecting the data, particularly data with labels, is expensive or even difficult in many real tasks. Second, once a model has been trained, if environment changes, which often happens in real tasks, the model can hardly perform well or even become useless. Third, the trained models are usually black boxes, whereas people usually want to know what have been learned by the models, particularly in real tasks where decision reliability is crucial and rigorous judgment by human beings are critical.
Frontiers of Computer Science