When teaching the machine, the team had to take some care with the images. Thrun hoped that people could one day simply submit smartphone pictures of their worrisome lesions, and that meant that the system had to be undaunted by a wide range of angles and lighting conditions. But, he recalled, “In some pictures, the melanomas had been marked with yellow disks. We had to crop them out—otherwise, we might teach the computer to pick out a yellow disk as a sign of cancer.”
It was an old conundrum: a century ago, the German public became entranced by Clever Hans, a horse that could supposedly add and subtract, and would relay the answer by tapping its hoof. As it turns out, Clever Hans was actually sensing its handler’s bearing. As the horse’s hoof-taps approached the correct answer, the handler’s expression and posture relaxed. The animal’s neural network had not learned arithmetic; it had learned to detect changes in human body language. “That’s the bizarre thing about neural networks,” Thrun said. “You cannot tell what they are picking up. They are like black boxes whose inner workings are mysterious.”
The “black box” problem is endemic in deep learning. The system isn’t guided by an explicit store of medical knowledge and a list of diagnostic rules; it has effectively taught itself to differentiate moles from melanomas by making vast numbers of internal adjustments—something analogous to strengthening and weakening synaptic connections in the brain. Exactly how did it determine that a lesion was a melanoma? We can’t know, and it can’t tell us.
And, in the same vein, here are some thoughts on terrorism.