GE Global Research, where Jason Nichols works, is setting up online programs that teach machine learning and symposia where scientists can explore new roles.
GE builds these “Digital twins” using information it gathers from sensors on the machines, supplemented with physics-based models, AI, data analytics, and knowledge from its scientists and engineers.
What’s more, if data is corrupted or missing, the company fills in the gaps with the aid of machine learning, a type of AI that lets computers learn without being explicitly programmed, says Colin Parris, GE Global Research’s vice president for software research.
Parris says GE pairs computer vision with deep learning, a type of AI particularly adept at recognizing patterns, and reinforcement learning, another recent advance in AI that enables machines to optimize operations, to enable cameras to find minute cracks on metal turbine blades even when they are dirty and dusty.
No fad. To develop and work with these systems, GE researchers need to understand both the physics of the machines and the AI algorithms. Parris, the software research leader, admits that some of GE’s 2,000 researchers still regard certain aspects of the new approach as a “Passing fad.”.
That’s after creating 100 new research jobs related to AI and robotics in 2016.