Since neural networks (including binary ones) are promising at recognizing patterns, they can be a fitted solution for dealing with problems thinking out of the box. E.D.&A. investigated how well a neural network can be trained, for example in hob ventilation in which the electronic controller is operated using capacitive touch sensors. These sensors are located under a thick sheet of glass and in addition there is moisture, dirt and interference from the induction hob itself. Thus, E.D.&A. trained the neural network to recognize the right signal (switch on) among the others. The training examples required were obtained by using an artificial finger, which operated the sensor using compressed air.
The collection of data could be automated. In addition, E.D.&A. also evaluated the binary variant of neural networks for use with its electronic controllers (microcontrollers). The electronics manufacturer implemented a demo of its own embedded AI framework for machine and device manufacturers. It operated by drawing a number from 0 to 9 on a touchscreen and in the background, it is passed on to a binary neural network. This binary neural network works out the number you mean and shows the result. These examples show us how we can make machines and devices “smart” by training them with an artificial neural network.