For industrial equipment operators, who are constantly being pushed to reduce downtime (leading to more frequent equipment use) and decrease maintenance costs (which typically rise as equipment is used), predictive maintenance has shifted from a “nice-to-have” to a must-have.

This week’s podcast guest, Johanna Pingel, a Product Manager at MathWorks, said that engineers should be concerned with the AI workflow.

How do we transition from our historical lack of understanding about what’s going on under the hood of complex neural networks, and into an era of AI explainability around how these models operate?

MathWorks and AImotive have begun a collaboration to integrate RoadRunner into aiSim allowing users to create road and 3D networks for simulation in aiSim

Cyberattacks are present in various embedded devices and throughout different types of industries and products.

When it comes to an end-to-end text analytics workflow, what do engineers need to know? There are four main phases in the workflow:

AI is not only used for path planning and obstacle avoidance, but incorporated in every step of development from modeling how systems will perform on the road to gathering parts for manufacturing.