Brute forcing is a method in hacking (and apparently computer chess simulation) that means to run every possibility of a problem until the program finds the best solution.

But Matthew Lai wants to make chess-playing computers smarter. For part of his Masters degree at Imperial College London, Lai trained artificial neural networks to play at the level of a FIDE International Master, better than 97.8 percent of rated tournament players. He calls his software Giraffe.

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.