DeepMind Proposes Symmetry-Based Representations as a Fundamental Principle for Learning Good Representations in General Intelligence

The complex behaviours of humans and other animals are products of a biological intelligence informed largely by the learned “good representations” of sensory inputs, which serve as a fundamental computational step for enabling data-efficient, generalizable and transferrable skill acquisition. Neuroscientists and machine learning (ML) researchers are thus both interested in just what constitutes these good representations of the often high-dimensional, non-linear and multiplexed sensory signals that support general intelligence.

In the new paper Symmetry-Based Representations for Artificial and Biological General Intelligence, a DeepMind research team argues that the mathematical description of symmetries in group theory and symmetry transformations for representation learning in the brain suggest symmetries as an important general framework that determines the structure of the universe, constrains the nature of natural tasks, and consequently shapes both biological and artificial intelligence. The team explores symmetry transformations as a fundamental principle for defining what makes good representations.