AI in Industrial Applications: Challenges, Solutions and Future Directions
Applications of AI in industrial applications are in many ways different from their use in consumer applications, in terms of availability of data, accessibility of data, meaning of data and explainability of data. The presentation then discusses solutions to these thorny challenges, by combining machine learning with existing domain knowhow in the form of knowledge graph. The practices involves using various machine learning practice to convert existing domain knowhow to knowledge graph and to grasp the meaning of data, building standard information models to enable interoperability across domains, and giving explainable answer by combine the power of machine learning and knowledge graph. Finally, the presentation outlooks how combining symbolic and statistic AI will maximize the benefits to industrial applications in general..
Dan Yu is an Innovator at Siemens Corporate Technology since June 2015. Prior to joining Siemens US, he founded Siemens Innovation Center in Wuxi, China in 2012, where Siemens innovates with local partners. He joined Siemens Research (Corporate Technology) China after his study in Tsinghua University and Munich University of Technology. Since then he has been doing technology innovation for a wide spectrum of industrial application areas including industrial manufacturing, intelligent traffic, smart building and logistics with various Internet of Things technologies. Because of the significant innovation contribution, he was awarded “Siemens Inventor of the Year 2010”. Among the 70 patents he authored/coauthored many have already become products or part of Siemens products.