But deep learning requires massive amounts of training data to work properly, incorporating nearly every scenario the algorithm will encounter. Systems like Google Images, for instance, are great at recognizing animals as long as they have training data to show them what each animal looks like. Marcus describes this kind of task as “interpolation,” taking a survey of all the images labeled “ocelot” and deciding whether the new picture belongs in the group.  

Engineers can get creative in where the data comes from and how it’s structured, but it places a hard limit on how far a given algorithm can reach. The same algorithm can’t recognize an ocelot unless it’s seen thousands of pictures of an ocelot — even if it’s seen pictures of housecats and jaguars, and knows ocelots are somewhere in between. That process, called “generalization,” requires a different set of skills.