In 1943, at the height of World War II, the U.S. military hired an audacious psychologist named B.F. Skinner to develop pigeon-guided missiles. These were the early days of munitions guidance technology, and the Allies were apparently quite desperate to find more reliable ways to get missiles to hit their targets. It went like this: […] 
In 1943, at the height of World War II, the U.S. military hired an audacious psychologist named B.F. Skinner to develop pigeon-guided missiles. These were the early days of munitions guidance technology, and the Allies were apparently quite desperate to find more reliable ways to get missiles to hit their targets.
It went like this: Skinner trained pigeons to peck at an image of the military target projected onto a screen. Whenever their beaks hit the moving target dead center, he rewarded the birds with food pellets. Once the pigeons had learned how to peck at targets, they earned their wings: Skinner would strap three of his little pilots into a missile cockpit specially fitted with straps attached to gyroscopes that would steer the bomb.
Now, when American jets released their pigeon-filled bombs, the birds would peck at an image of the bomb’s target, their little straps twisting and bending, gyroscopes whirling, guiding the bomb and the birds to their final resting place.
The military eventually pulled the plug on Project Pigeon, while Skinner continued to develop a discipline that came to be known as behavioral psychology. “Behavioral” because, unlike his Freudian predecessors, Skinner didn’t care about unobservable characteristics of conscious intelligence — things like thoughts, emotions, desires, and fears. He just wanted to discover how to train animals (and his children) using scientific techniques of stimulus, reward, and punishment.
If there’s a modern Project Pigeon, it’s DeepMind’s AlphaGo. Over the past three years, using techniques similar to those pioneered by Skinner, DeepMind has developed some of the most sophisticated machine-learning techniques in order to train a computer with artificial intelligence (AI) to master the ancient board game of Go.
Weirdly enough, this millenia-old board game is the perfect demonstration of human complexity, machine limitations, and how powerful AI has become.
For decades, researchers considered playing Go to be the holy grail of game-playing AI. No computer had ever come close to beating a professional in an even, full-board game. Many thought it impossible.
Intriguingly, AlphaGo plays Go with something akin to human-like intuition. That’s new. Computers have always been good at doing the kinds of tasks that we can logically define, like multiplying large numbers, storing information, and playing recorded movies. But they struggle with implicit knowledge. Those are the things we know how to do but cannot explain — even to ourselves — how we do them. Recognizing faces, learning a language, identifying diseases, and exercising common sense are all activities we might like machines to perform, but which can’t be codified in a set of rules. Broadening AI’s capabilities to include implicit knowledge opens up a vast number of new tasks to computers.
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