Pt 2: Maintaining the Physics Model within AI & ML

Pt 2: Maintaining the Physics Model within AI & ML

Arnaud Hubaux, ASML


In this second part episode of the True North Podcast, IpX Director of MBE, Max Gravel and Senior Technical Program Manager, ASML, Arnaud Hubaux continue their discussion on the challenges of maintaining the physics model in AI/ML with rapidly changing datasets. 

Arnaud describes the need for an enterprise-level AI/ML framework that considers the complete lifecycle change to expand beyond niche use-cases and realize the dream of a fully automated product line. Max and Arnaud discuss where to start when implementing AI and the role of CM in maturing the adoption of AI in a manufacturing environment. Hear more on:

  • The need for the impact analysis to stretch beyond the walls of the company into the environment where it will be running.
  • The perceived unpredictability of AI/ML and the reluctance to deploy in a manufacturing environment.
  • The importance of building in safety mechanisms to prevent financial loss in case of AI/ML failure.
  • How CM establishes gates and checks when releasing a change to make sure the proper qualifications are implemented.
  • AI/ML sustainability and the need to look beyond short-term benefits into the complete lifecycle of a product.

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