DEEP LEARNING

摘要

MARTIN GADSBY, DIRECTOR AT OPTIMAL INDUSTRIAL TECHNOLOGIES, LOOKS AT HOW DEEP LEARNING-POWERED PROCESS ANALYTICAL TECHNOLOGY CAN BOOST MANUFACTURING PROCESSES. Deep learning has the potential to revolutionise a broad range of industries by offering increasingly accurate predictive capabilities with little to no human supervision. These can have a tremendous impact on the manufacturing sector by supporting the application of process analytical technology (PAT) and its concomitant increase in process performance and product quality. Deep learning is a highly flexible and adaptive artificial intelligence tool that, when exposed to new datasets, can increase its ability to identify patterns and classify relationships between data. This means that the larger the volume of data fed into a deep learning-generated predictive model, the higher the probability that the system will create more accurate and precise forecasts. Furthermore, the evolution of the model is automatic, ie no programming or other actions from human operators are required. These unique capabilities are enabled by artificial neural network (ANN) architectures that mimic the human brain. ANNs are collections of interconnected artificial neurons or nodes organised in layers. Each neuron receives an input with data to analyse and automatically performs different computations on it without the need for any rule-based programming. The resulting output is then sent to another node for further processing. Every time an input is fed to the ANN, the system may be able to notice new correlations between data and implement them into its predictive model. An extremely advanced ANN may even be able to find out interdependencies that are not known to human experts, thus delivering forecasts with unprecedented accuracy.