Humans have a knack for making visual associations, but computers don't have it so easy; we often have to tell them what they see. Carnegie Mellon's recently launched Never Ending Image Learner (NEIL) supercomputer bucks that trend by forming those connections itself. Building on the university's earlier NELL research, the 200-core cluster scours the internet for images and defines objects based on the common attributes that it finds. It knows that buildings are frequently tall, for example, and that ducks look like geese. While NEIL is occasionally prone to making mistakes, it's also transparent -- a public page lets you see what it's learning, and you can suggest queries if you think there's a gap in the system's logic. The project could eventually lead to computers and robots with a much better understanding of the world around them, even if they never quite gain human-like perception.