Comment Still no solution after 28 years. (Score 1) 210
This is from the conclusion of my dissertation from 1997 on using a neural network control system in mineral prcessing.
"Overall, it is now evident that neural networks have been trained haphazardly at best. There is almost no discussion in the text books or with the commercial programs about how to gather a good data set. Usually, the advice is limited to getting all relevant operating modes, or to just get as much as data as possible. This work has pulled together concepts from several sources that together can guarantee to a given confidence level that a neural network can be trained. However, this did not solve most of the operational problems with neural network controllers.
Neural networks cannot run across a change in ores, or more generally, they cannot remain valid when the underlying distribution changes. Since neural networks work on pattern recognition and have no a-priori model from which to work, they have several weaknesses when applied to industrial processes.
They must be trained on all possible combinations of inputs, or they fail in unpredictable ways when they encounter a new condition. In simpler terms, they are not particularly robust."
Unpredictable failure modes (the models are non-linear, you don't know what they are going to do) are not compatible with mineral processing or chemical plants in general.
Current AI researchers threw hardware and electricity at it. The models are much bigger, but still fail the same way.