How to Predict Extreme Weather
Thanks to advances in machine learning over the last two decades, it’s no longer in question whether humans can beat computers at games like chess; we’d have about as much chance winning a bench-press contest against a forklift. But ask the current computer champion, Google’s AlphaZero, for advice on chess theory, like whether a bishop or a knight is more valuable in the Ruy Lopez opening, and all you’ll get is a blank stare from a blinking cursor. Theory is a human construct the algorithm has no need for. The computer knows only how to find the best move in any given position because it’s trained extensively—very extensively—by practicing against itself and learning what works.
Even with a lead time of 18 months, the neural network was able to see El Niño events coming.
The computing methods underlying success stories like AlphaZero’s have been termed “deep learning,” so-called because they employ complex structures such as deep neural networks with multiple layers of computational nodes between input and output. Here the input can be richly structured, like the positions of pieces on a chessboard or the color values of pixels in an image, and the output might be an assessment needed to make a decision, like the value of a possible chess move or the probability the image is. Training the network typically involves tuning all of the available dials, the parameters of the model, until it does well against a set of training data and then testing its performance on a separate out-of-sample data set. One of the complaints about such systems is that, once their training is complete, they can be a black box; exactly how the algorithm processes the information it’s given, and why, is often shrouded in mystery. (When all that’s at stake is a chess game, this is no great concern, but when the same techniques are used to determine people’s creditworthiness or likelihood of committing a crime, say, the demand for accountability understandably goes up.) The more layers of nodes, and the more parameters to be adjusted in the learning process, the more opaque the box becomes.
