Recently, Yoshua Bengio and researchers from the University of Montreal, the Max-Planck Institute for Intelligent Systems and Google Research demonstrated how causal representation learning contributes to the robustness and generalisation of machine learning models. The team reviewed the fundamental concepts of causal inference and related them to crucial open problems of machine learning, including transfer and generalisation.

Attaining general intelligence is one of the key goals in machine learning and deep learning. As things stand, the machine learning techniques are limited at some crucial feats where natural intelligence excels. These include transfer to new problems and any form of generalisation.

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