Measuring abstract reasoning in neural networks DeepMind

Standard human IQ tests often require test-takers to interpret perceptually simple visual scenes by applying principles that they have learned through everyday experience. For example, human test-takers may have already learned about'progressions' (the notion that some attribute can increase) by watching plants or buildings grow, by studying addition in a mathematics class, or by tracking a bank balance as interest accrues. They can then apply this notion in the puzzles to infer that the number of shapes, their sizes, or even the intensity of their colour will increase along a sequence. We do not yet have the means to expose machine learning agents to a similar stream of'everyday experiences', meaning we cannot easily measure their ability to transfer knowledge from the real world to visual reasoning tests. Nonetheless, we can create an experimental set-up that still puts human visual reasoning tests to good use.