The Computational Limits of Deep Learning Are Closer Than You Think

Deep in the bowels of the Smithsonian National Museum of American History in Washington, D.C., sits a large metal cabinet the size of a walk-in wardrobe. The cabinet houses a remarkable computer -- the front is covered in dials, switches and gauges, and inside, it is filled with potentiometers controlled by small electric motors. Behind one of the cabinet doors is a 20 by 20 array of light sensitive cells, a kind of artificial eye. This is the Perceptron Mark I, a simplified electronic version of a biological neuron. It was designed by the American psychologist Frank Rosenblatt at Cornell University in the late 1950s who taught it to recognize simple shapes such as triangles.