The state of today's machine learning: Short, wide, deep but not high

Comment Remember that kid in middle school who was deeply into Dungeons & Dragons, and hadn't seen his growth spurt yet? Machine learning is sort of like that kid – deep, wide, and short – and not so tall. Big data – an increased availability of large data sets for training and deployment has also driven the need for deeper nets. Deeper nets – deep neural nets have multiple layers, and often possess higher-order architecture (width) within a given layer. Clever training – it was discovered that a large dose of unsupervised learning in the earlier stages of training allowed for the net to do its own automated, lower-level feature recognition and extraction, and pass those features on to the next stage for higher-level feature recognition.