Uses

Artificial neural networks have proven to be practical, robust tools that are used in many applications; distinguishing bombs and weapons from alarm clocks in semi-automatic airport x-ray machines; translating spoken words into computer commands; and the control of autonomous robots, to mention a few. Some of the network theory helps by defining a conceptual vocabulary that enables scientists to more accurately describe the vastly more complex phenomenon that we observe in systems like our own brains.

As usual, there are problems as well. Even if you have a nice network that does its job, it is almost impossible to tell just how it does it. This goes along the same lines as asking a natural talent how s/he does whatever s/he is good at. They just do it. Artificial neural networks also typically involve the use of non-linear optimisation (explained later), and are then largely dependent on the performance of this rather difficult procedure.