We’ve all come to terms with a neural network doing jobs such as handwriting recognition. The basics have been in place for years and the recent increase in computing power and parallel processing has made it a very practical technology. However, at the core level it is still a digital computer moving bits around just like any other program. That isn’t the case with a new neural network fielded by researchers from the University of Wisconsin, MIT, and Columbia. This panel of special glass requires no electrical power, and is able to recognize gray-scale handwritten numbers.

The glass contains precisely controlled inclusions such as air holes or an impurity such as graphene or other material. When light strikes the glass, complex wave patterns occur and light becomes more intense in one of the ten areas on the glass. Each of those areas corresponds to a digit. For example, here are two examples of the pattern of light recognizing a two on the glass:



With a training set of 5,000 images, the network was able to correctly identify 79% of 1,000 input images. The team thinks they could do better if they allowed looser constraints on the glass manufacturing. They started with very strict design rules to assist in getting a working device, but they will evaluate ways to improve recognition percentage without making it too difficult to produce. The team also has plans to create a network in 3D, as well.

If you want to learn more about traditional neural networks, we have seen plenty of starter projects. If TensorFlow is too much to swallow, try these 200 lines of C code.