Pt 2: Maintaining the Physics Model within AI & ML
Arnaud Hubaux, ASMLListen
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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