Virtually every discipline of significant human endeavor has a way explaining itself as fundamental and important. In all the cases I know of, they are both right (they are vital) and wrong (they are not solely vital).
- Politics. This is the one that everyone is familiar with at the moment. “What could be more important than the process of making decisions?”
- Science and Technology. This is the one that we-the-academics are familiar with. “The loss of modern science and technology would be catastrophic.”
- Military. “Without the military, a nation will be invaded and destroyed.”
- (insert your favorite here)
Within science and technology, the same thing happens again.
- Mathematics. “What could be more important than a precise language for establishing truths?”
- Physics. “Nothing is more fundamental than the laws which govern the universe. Understanding them is the key to understanding everything else.”
- Biology. “Without life, we wouldn’t be here, so clearly the study of life is fundamental.”
- Computer Science. “Everything is a computer. Controlling computation is fundamental to controlling the world.”
This post is a “me too” for machine learning. The basic claim is that all problems can be rephrased as prediction problems. In particular, for any agent (human or machine), there are things which are sensed and the goal is make good predictions about which actions to take. Here are some examples:
- Soccer. Playing soccer with Peter Stone is interesting because he sometimes reacts to a pass before it is made. The ability to predict what will happen in the future is a huge edge in games.
- Defensive Driving is misnamed. It’s really predictive driving. You, as a driver, attempt to predict how the other cars around you can mess up, and take that into account in your own driving style.
- Predicting well can make you very wealthy by playing the stock market. Some companies have been formed around the idea of automated stock picking, with partial success. More generally, the idea of prediction as the essential ingredient is very common when gambling with stocks.
- Information markets generalize the notion of stock picking to make predictions about arbitrary facts.
Prediction problems are prevalent throughout our lives so studying the problems and their solution, which is a core goal of machine learning, is essential. From the predictionist viewpoint, it is not about what you know, what you can prove or infer, who your friends are, or how much wealth you have. Instead, it’s about how well you can predict (and act on predictions of) the future.