Bassam Mohammed Abdelnabi adjusts a combustion test rig in a GE lab in Niskayuna, N.Y., on which researchers have run tests to validate simulations used to develop an AI model that they say will radically cut the time it takes to design products.Courtesy of General Electric Artificial intelligence is helping computers drive cars, recognize faces in a crowd and hold lifelike conversations. General Electric engineers now say they've used the data-intensive technology to develop tools that could cut the industrial giant's design process for jet engines and power turbines in at least half, speeding up its next generation of products. Today, it might take two days for engineers to run a computational analysis of the fluid dynamics of a single design for a turbine blade or an engine component. Scientists at General Electric's research center in Niskayuna, New York, say they've leveraged machine learning to train a surrogate model so that it can evaluate a million variations of a design in just 15 minutes. "This is, we think, a huge breakthrough," Robert Zacharias, technology director of thermosciences at GE Research, tells Forbes.

In a picnic area at General Electric Co.'s Global Research Center, a group of scientists and engineers are working on a new industrial revolution that will involve robots, drones and artificial intelligence. GE has been developing robot and artificial intelligence technologies for many years now. But these researchers in Niskayuna are part of GE's latest effort to monetize that technology with the launch of Avitas Systems, a new GE-created company being incubated in Boston with help from scientists here in the Capital Region. Avitas is creating technologies that will be artificial intelligence, or AI, combined with robots and predictive data analytics and software to provide high-tech inspection services to energy and transportation companies. On Tuesday, a team supervised by John Lizzi, director of robotics at GE Global Research, and Judy Guzzo, a project leader, were performing drone testing on a simulated oil rig flare stack.