From Dog to Cat
From Dog to Cat: A More Geometric Way to Think About Artificial Intelligence Imagine asking an AI system to turn a picture of a dog into a picture of a cat. One approach is to build a complicated collection of nonlinear functions and train them to produce the desired result. With enough layers and training, this can work remarkably well. But there is a catch: complicated nonlinear transformations can behave unpredictably. A tiny change in the input can sometimes produce a surprisingly large change in the output. A transformation that works well on familiar examples may behave strangely on examples that lie between them. As layers are composed, small distortions can accumulate, creating complicated geometric structures. There may be another way to approach the problem. Think in terms of a journey, not a jump Instead of asking for one complicated function that turns a dog into a cat, imagine breaking the transformation into a sequence of smaller steps. For example: Start with the o...