Research Challenges in Industrial AI - From Engineering Design to Shopfloor Operations

Explore the unique research and application challenges in Industrial AI through this insightful conference talk. Delve into three key clusters: the fusion of data-driven machine learning with physics-based methods, operational and control problems on the shopfloor, and the need for safe, reliable "industrial grade" AI. Discover how ML-based surrogate models and Bayesian Learning can significantly accelerate automotive and turbine design processes. Learn about the potential of Generative AI to overcome scalability and transferability barriers in visual quality inspection and anomaly detection. Gain valuable insights from various industrial domains and understand the open challenges in the field. Presented by Dr. Michael May, leader of Industrial Data Analytics & AI Research at Siemens AG, this talk offers a comprehensive overview of the current landscape and future directions in Industrial AI.

KDD2024 - Research Challenges in Industrial AI – from engineering design to shopfloor operations