Communications of the ACM
'transfer Learning' Jump-Starts New AI Projects
Data scientists rely on the fast-evolving frontier of data science known as "transfer learning" to tap into statistical knowledge that was gained on prior projects through supervised, semi-supervised, unsupervised, or reinforcement learning.
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Abstracting and reusing knowledge gleaned from a machine-learning application in other, newer apps--or "transfer learning"--is supplementing other learning methods that constitute the backbone of most data science practices.
Among the technique's practical uses is productivity acceleration modeling, which is viable when prior work can be reused without extensive revision in order to speed up time to insight.
Another transfer-learning application involves the method helping scientists produce machine-learning models that exploit relevant training data from prior modeling projects. This technique is particularly appropriate for addressing projects in which prior training data can easily become obsolete, which is a problem that frequently occurs in dynamic problem domains.
A third area of data science in which transfer learning could yield benefits is risk mitigation. In this situation, transfer learning can help scientists leverage subsets of training data and feature models from related domains when the underlying conditions of the modeled phenomenon have radically changed. This can help researchers ameliorate the risk of machine-learning-driven predictions in any problem domain vulnerable to extremely improbable events.
Transfer learning also is critical to data scientists' efforts to create "master learning algorithms" that automatically obtain and apply fresh contextual knowledge via deep neural networks and other forms of artificial intelligence.
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