Why you need to understand machine learning

Paradoxically, even as they open new windows on nature and human behaviour, learning algorithms themselves have remained shrouded in mystery. Hardly a day goes by without a story in the media involving machine learning, whether it's a tech company launching a virtual personal assistant; Google's AlphaGo beating the human Go champion; US retailer Target finding out a teenager is pregnant before her parents do; or the US National Security Agency (NSA) looking for dots to connect. But in each case the learning algorithm driving the story is a black box. Even books on big data skirt around what really happens when the computer swallows all those terabytes and magically comes up with new insights. At best, we're left with the impression that learning algorithms just find correlations between pairs of events, such as Googling "flu medicine" and having the flu.