We intend to have a similar panel discussion next year as well. Most of them were from process industries and automation companies. There is a clear trend towards better training algorithms. At the same time, people talk less and less about training algorithms in their papers, which is a positive sign of progress. It was pointed out that there is very little talk about the quality of data.
Most papers mention the amount of data available.
However, a large amount of data is not worth very much if it contains copies of the same observation with varying amounts of noise. It is therefore important to take into consideration the variation of important input variables in the data, and the characteristics of the noise contained in the data set. The industries which are interested in developing advanced models could think of collecting data appropriately to facilitate the development of neural network models from it. It was noticed that there is a trend towards hybrid systems. Nobody wants to throw away physical models developed after years of work, even if they are not as good as a small network.