»For example, when the cutting-edge image-recognition system Noisy Student converts the pixel values of an image into probabilities for what the object in that

»For example, when the cutting-edge image-recognition system Noisy Student converts the pixel values of an image into probabilities for what the object in that image is, it does so using a network with 480 million parameters. The training to ascertain the values of such a large number of parameters is even more remarkable because it was done with only 1.2 million labeled images—which may understandably confuse those of us who remember from high school algebra that we are supposed to have more equations than unknowns. Each picture has more than one pixel, so for the network to "remember" the images, we need more parameters than there are pictures.