Geoffrey Hinton's highly anticipated paper on capsules. A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. The length of the activity vector is used to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. The authors show that a discriminatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits.