The software’s ‘Q function’ plays a decisive role in this. It compares and evaluates the solutions found by the module and recognises the learning rate. The better way with the better result is pursued in future, too. Due to the central importance of the Q functions, self-learning is also referred to as deep Q learning. Experts call the data processing of the many millions of calculations in a short time the deep Q network.

That this principle could function using today’s computing capacities became clear in 2015. Scientists were able to train a deep Q network in self-learning so that it could play the Chinese strategic game Go better than a human – which had been thought impossible up until that point.

Put simply, the game is played with each player placing their stones on the 361 points where 19 vertical and 19 horizontal lines cross, one after another, so that the opponents stones are closed in on four sides and thus “captured”. The game requires a combination of long-term planning, quick thinking and new learning processes.

The challenges for systems controlled by artificial intelligence: They must learn to recognise the patterns in their human partner’s intuitive actions when selecting from a practically unlimited number of moves. This is the only way they can predict the moves and counter them before it is too late. Previous AI modules were overwhelmed because they were not tasked with achieving a clear target but rather a changing one.