  Current Artificial Intelligence (AI) methods, most based on deep learning,
have facilitated progress in several fields, including computer vision and
natural language understanding. The progress of these AI methods is measured
using benchmarks designed to solve challenging tasks, such as visual question
answering. A question remains of how much understanding is leveraged by these
methods and how appropriate are the current benchmarks to measure understanding
capabilities. To answer these questions, we have analysed existing benchmarks
and their understanding capabilities, defined by a set of understanding
capabilities, and current research streams. We show how progress has been made
in benchmark development to measure understanding capabilities of AI methods
and we review as well how current methods develop understanding capabilities.
