Mr Robot

Katrina Onstad:

For more than 30 years, Geoffrey Hinton hovered at the edges of artificial intelligence research, an outsider clinging to a simple proposition: that computers could think like humans do—using intuition rather than rules. The idea had taken root in Hinton as a teenager when a friend described how a hologram works: innumerable beams of light bouncing off an object are recorded, and then those many representations are scattered over a huge database. Hinton, who comes from a somewhat eccentric, generations-deep family of overachieving scientists, immediately understood that the human brain worked like that, too—information in our brains is spread across a vast network of cells, linked by an endless map of neurons, firing and connecting and transmitting along a billion paths. He wondered: could a computer behave the same way?

The answer, according to the academic mainstream, was a deafening no. Computers learned best by rules and logic, they said. And besides, Hinton’s notion, called neural networks—which later became the groundwork for “deep learning” or “machine learning”—had already been disproven. In the late ’50s, a Cornell scientist named Frank Rosenblatt had proposed the world’s first neural network machine. It was called the Perceptron, and it had a simple objective—to recognize images. The goal was to show it a picture of an apple, and it would, at least in theory, spit out “apple.” The Perceptron ran on an IBM mainframe, and it was ugly. A riot of criss-crossing silver wires, it looked like someone had glued the guts of a furnace filter to a fridge door. Still, the device sparked some serious sci-fi hyperbole. In 1958, the New York Times published a prediction that it would be the first device to think like the human brain. “[The Perceptron] will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.”