Learning to Make Analogies by Contrasting Abstract Relational Structure
  Analogical reasoning has been a principal focus of various waves of AI
research. Analogy is particularly challenging for machines because it requires
relational structures to be represented such that they can be flexibly applied
across diverse domains of experience. Here, we study how analogical reasoning
can be induced in neural networks that learn to perceive and reason about raw
visual data. We find that the critical factor for inducing such a capacity is
not an elaborate architecture, but rather, careful attention to the choice of
data and the manner in which it is presented to the model. The most robust
capacity for analogical reasoning is induced when networks learn analogies by
contrasting abstract relational structures in their input domains, a training
method that uses only the input data to force models to learn about important
abstract features. Using this technique we demonstrate capacities for complex,
visual and symbolic analogy making and generalisation in even the simplest
neural network architectures.
