Chief Strategy Officer
William Sobel is Chief Strategy Officer and Co-Founder of VIMANA, the leading Industrial AI platform for discrete manufacturing, and the Chief Architect and Chair of the Technical Steering Committee for the MTConnect Standard, the leading international semantic standard for manufacturing equipment run by The Association for Manufacturing Technology. He was the co-chair of the Industrial AI group at IIC and advises DMSC regarding the QIF standard as well as liaising with ROS Industrial, ISA, MIMOSA, OMG, OPC Foundation, VDW, VDMA and MESA organization for standards harmonization and information architecture. He was recently a guest researcher at NIST to develop information models for task-based process choreography automation with the ROS Industrial group at SWRI. Mr. Sobel has over 30 years of experience building companies and architecting complex technology systems for numerous industries. Before working at VIMANA, I was a visiting lecturer at UC Berkeley working at the RadLAB teaching agile web development and data center optimization using machine learning. During his employment at the university, I authored the MTConnect Standard as a consultant to the Association for Manufacturing Technology to address the lack of semantic interoperability in manufacturing. Before UC Berkeley, Mr. Sobel worked in the financial industry for 17 years, finishing as VP and Chief Architect at MSCI-Barra where he led a team to architect the industry's first and leading SaaS financial risk management software.
Industrial AI Platforms – Beyond Machine Learning and Addressing Causality
WEDNESDAY, 6/19, 2:00 PM // TECH THEATER
Industrial Artificial Intelligence has focused on the relationship between data and observation using machine learning. These approaches have failed to deliver on many of the promises of reduced costs, increased agility, improved machine reliability and create useful digital twins. The reason is a lack of understanding of the reason "why" something happens, the causes. Utilizing technology around causality analysis, we will present how we can deliver on the promises of industrial AI.