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They seem to be getting smarter every day: one deep-learning machine has managed to teach itself to solve a Rubik’s Cube, proving that humans aren’t the only ones capable of tackling complex problems. Stephen McAleer and colleagues from the University of California, Irvine made it possible with a new kind of deep-learning technique called “autodidactic iteration.”
Given an unsolved cube, the machine must decide whether a specific move is an improvement on the existing configuration. To do this, it must be able to evaluate the move. Autodidactic iteration does this by starting with the finished cube and working backwards to find a configuration that is similar to the proposed move. This process is not perfect, but deep learning helps the system figure out which moves are generally better than others.
Given an unsolved cube, the machine must decide whether a specific move is an improvement on the existing configuration. To do this, it must be able to evaluate the move. Autodidactic iteration does this by starting with the finished cube and working backwards to find a configuration that is similar to the proposed move. This process is not perfect, but deep learning helps the system figure out which moves are generally better than others.