Agree. However, the described technique isn't really AI, there's no neural network or training. It's GA-driven exploration for testing: mutate inputs, keep what gets you further down the state space, discard what doesn't. AlphaGo optimizes for winning; testing optimizes for coverage. That said, what does apply well to testing from the AI field is the exploration during the training phase, as well as the ability to beat the game, giving you paths to branch off from to explore the test space further.