Understanding how machines learn

For decades, there have been great hopes for advances in artificial intelligence. We have seen occasional waves of euphoria, but as often as not, each new florescence has been followed by another AI winter. The expectations of these new technologies were too high for science to ever have a real chance at meeting them.

A couple of years ago, all that changed. The breakthrough came about with developments in deep neural networks, one of the deep-learning techniques that are standard AI technology today. The rapid growth in processing power also contributed significantly to the AI revolution, making it possible to access enormous datasets that can be used to train these self-learning systems. But it isn’t yet entirely clear why deep learning is so effective. Gitta Kutyniok even goes so far as to call it a mystery—not exactly a common description for a mathematician to use.