become visible as trends . AI-assisted systems , however , can forecast them based on smallest indicators and prevent them if they arise by means of early alerts . Seidl explains : “ This does presuppose that the knowledge about all process steps is digitalised and pre-processed to train the AI so it can identify correlations at the end of the day . In this process it is constantly learning about thickness changes , climate conditions , all influence factors – sooner or later the AI will be capable of capturing and assessing all factors .” ISRA Vision GmbH also already uses AI systems . The company offers inspection solutions for the glass industry – which by their own accounts detect 100 % of defects in float glass and processed products , at full production speed and with reliable classification in real time . ISRA systems monitor the complete process to optimise the glass yield in a resource and energy-saving manner . One concrete example of the use of AI is the EPROMI solution , as Florian Sterzing , R & D Team Leader Data Analytics at ISRA Surface Vision knows . “ This solution makes the potential hidden in production data visible and helps to infer decisions along the value chain . To this end , it collects data from inspection systems and other sources available , processes them and visualises them in intuitive dashboards . The most important and most heatedly disputed development area currently is predictive analytics where AI-assisted systems analyse historical trends , identify patterns and anticipate bottlenecks and inefficiencies , thereby prompting continuous process improvements . Initially , these systems learn by given data and then also from each experience acquired . Specialised decisionmaking AIs can be trained for every application , every production area that generates data . This helps to cut maintenance times , avoid down-
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