UCLA researchers found the limits of deep learning networks – distinguishing between an otter and a can opener. Nicholas Baker, a cognitive psychology graduate student, explored the behaviors of two machine learning networks known as convolutional neural networks, which are well-established machine learning networks capable of visual recognition. The study aimed to test whether the networks are able to visualize automatically in the same way that humans are able to. "Recognizing by shape is a very abstract task," Baker said. "There is a lot of symbolic processing between what you initially see in sensation and what you encode in a more durable format, and we think this might be a very hard thing to do for neural networks."