Cutting Through the Hype

This talk will explore state-of-the-art techniques and tools for validating AI models and performance metrics tailored to specific industrial use cases and highlight the need for rigorous error analysis and the development of fail-safe mechanisms to mitigate risks. We will discuss real-world case studies demonstrating the impact of AI failures and the strategies employed to prevent and manage such incidents. We will delve into the significance of robust testing frameworks to ensure the reliability and performance of AI models. We will emphasize the importance of repeatability and stability, particularly in the face of new training data, which is essential for maintaining the integrity of AI systems over time. 

James Davidson, Chief Architect, MiR

Jeremy Rockman, Product Marketing Manager, Mobile Industrial Robots

Back