For these sessions utilize the Q & A function by clicking on the button on the lower right of the session screen next to the view session button. Q & A will open in a separate window. Q&A is available for the first hour of this session's scheduled date and time.
Adoption of Artificial Intelligence is a critical first step for the Quality 4.0 Organization. After the declaration of support for AI is made, the organization must begin to focus on how to qualify and incorporate AI in a way that is consistent with expectations from customers and the 3rd Party Registrars. From a gage qualification and policy perspective this expectation is historically based on traditional (non-AI) gages and metrology and a focus on calibration and Gage R&R. AI solutions designed to make conformity decisions require the creation of an adapted expectation set, which is greatly influenced by these legacy practices. When utilizing AI, there is hardware and software involved, but not the same as organizations are used to dealing with. The hardware is essentially a visual camera, not a measurement device. Given this, one may ask, what do i calibrate and what is the measurement variation that i must control? AI maturity depends on detection and classification capabilities, as well as an adequate source of training data, in addition to minimized hardware variation (visual cameras and lighting hardware). These focus areas can be grouped into two main actions for the organization; 1) qualification of the AI Algorithm, and 2) Attribute Gage R&R for the hardware which is used to capture the image and enable execution of the AI Algorithm. Only after these two actions are successfully satisfied can the Quality 4.0 organization display confidence in their efforts to ensure this new form of measurement variation is minimized.
Enable the quality practitioner to use specific tools to minimize measurement variation associated with AI solutions.
Enable the quality practitioner to speak and understand relevant AI terminology.
Enable quality practitioner leaders to action their employees toward AI measurement variation control.
Enable an understanding of what "repeatability" means for AI users.