WTF?: What's the Future and Why It's Up to Us

by Tim O'Reilly

Using machine learning, the developer starts out with a hypothesis, just like before, but instead of producing a handcrafted algorithm to process the data, she collects a set of training data reflecting that hypothesis, then feeds the data into a program that outputs a model—a mathematical representation of features to be looked for in the data. This cycle is repeated again and again, with the program making minute adjustments to the model, gradually modifying the hypothesis using a technique such as gradient descent until it more perfectly matches the data. In short, the refined model is learned from the data. That model can then be turned loose on real-world data similar to that in the training data set.