Via Chris Dixon descobreixo una curiosa història sobre els perills de prendre decisions basades en patrons. Sovint els patrons més “evidents” poden no ser els més rellevants…

A famous story in artificial intelligence is how the US military developed algorithms to determine whether an image had a tank in it. They used a standard machine learning method: feed the computer a “training set” of photos, some of which had tanks in them and some of which didn’t, and let algorithms identify which features in the photos correlated to tanks being shown.

This method worked for a while but then mysteriously stopped working. Since the features the computer identified were embedded in complicated mathematical equations, no one could figure out what it was really doing and therefore why it stopped working. Eventually someone realized that in the training set, all of the images with tanks were taken on a cloudy day, and all the images without tanks were taken on a sunny day. The algorithms had fixated on the most obvious pattern – the color of the sky. When the algorithm was tested on new photos where the weather varied, it was completely flummoxed.