Machine-Learning Maestro Michael Jordan on the Delusions of Big Data

“…if people use data and inferences they can make with the data without any concern about error bars, about heterogeneity, about noisy data, about the sampling pattern, about all the kinds of things that you have to be serious about if you’re an engineer and a statistician—then you will make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori. You will just produce these outputs and hope for the best. And so that’s where we are currently. A lot of people are building things hoping that they work, and sometimes they will. And in some sense, there’s nothing wrong with that; it’s exploratory. But society as a whole can’t tolerate that; we can’t just hope that these things work. Eventually, we have to give real guarantees.”