The Computational Limits of Deep Learning Are Closer Than You Think « Machine Learning Times

Deep learning eats so much power that even small advances will be unfeasible give the massive environmental damage they will wreak, say computer scientists. 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.