Data scientists face a problem: machine learning models need to be trained on labeled datasets, but labeling the data is tedious and time-consuming. Enter automatic data labeling, in which most of the preprocessing work is done by a computer. At first glance, automatic data labeling sounds too good to be true. Of course, more automation is typically a good thing regarding efficiency. In many industries, automation has increased productivity and production and has increased the quality of both while keeping them consistent.

Deep learning is already helping the designers to make a new edition of a product. With the help of data from the sensors and some of the finest deep learning methods, the experts are able to optimize the designs. We have already seen that deep learning, AI, ML and big data have augmented creativity the design space. The biggest example is the IBM's machine learning system, Watson. A large number of images were inserted into the system, and of the images were the work of artist Gaudi.