Efficient human-like semantic representations via the Information Bottleneck principle

Understand

Maintaining efficient semantic representations of the environment is a major challenge both for humans and for machines.

• While human languages represent useful solutions to this problem, it is not yet clear what computational principle could give rise to similar solutions in machines.
• In this work we propose an answer to this open question.
• We suggest that languages compress percepts into words by optimizing the Information Bottleneck (IB) tradeoff between the complexity and accuracy of their lexicons.