After 72 hours of training, Giraffe figured out the best possible move 46 percent of the time. The move that Giraffe selected was in the top 3 moves 70 percent of the time. Previous attempts at machine learning in chess, like Knightcap, needed programmers to design "pattern recognizers," separate functions to learn moves like shielding a king with a pawn, or the importance of having both colors of a bishop, says Lai. The machine learning algorithm would watch already-defined moves, and learn how strong they were. Giraffe discovers these patterns automatically, so it can learn moves that even the programmer wouldn't have considered.