Because we need to recognise real-world objects in order to do some tasks.
After the long AI winter, neural-nets have sprung up again, due to more powerful machines, millions of easily accessible images via internet, and an understanding of back-propagation. They make very good classifiers for known object sets, and can be run on machines available to the average user - a quality desktop computer with a $AU1000.00 GPU does nicely.
This section is a collection of notes about how the object recognition system was set up. Basically the strategy was to use the retraining mechanism described at https://www.tensorflow.org/hub/tutorials/image_retraining "How to Retrain an Image Classifier for New Categories" to train to recognise kangaroos. This involved