  IQ tests are an accepted method for assessing human intelligence. The tests
consist of several parts that must be solved under a time constraint. Of all
the tested abilities, pattern recognition has been found to have the highest
correlation with general intelligence. This is primarily because pattern
recognition is the ability to find order in a noisy environment, a necessary
skill for intelligent agents. In this paper, we propose a convolutional neural
network (CNN) model for solving geometric pattern recognition problems. The CNN
receives as input multiple ordered input images and outputs the next image
according to the pattern. Our CNN is able to solve problems involving rotation,
reflection, color, size and shape patterns and score within the top 5% of human
performance.
