 Training nets to model aspects of human intelligence is a fine art. Success with backpropagation and other connectionist learning methods may depend on quite subtle adjustment of the algorithm and the training set. Training typically involves hundreds of thousands of rounds of weight adjustment. Given the limitations of computers in the past, training a net to perform an interesting task took days or even weeks. More recently, the use of massively parallel dedicated processors (GPUs) has helped relieve these heavy computational burdens. But even here, some limitations to connectionist theories of learning will remain to be faced. Humans (and many less intelligent animals) display an ability to learn from single examples; for example, a child shown a novel two-wheeled vehicle and given the name “Segway”, knows right away what a Segway is (Lake, Zaremba et al. 2015). Connectionist learning techniques such as backpropagation are far from explaining this kind of “one shot” learning.